Datasets:

fineset-io
/

protein-language-models-papers

id string	sources list	title string	abstract string	authors list	categories list	fields_of_study list	published_date timestamp[s]	url string	pdf_url string	arxiv_id string	doi string	citation_count int64	influential_citation_count int64	has_code bool	code_url string	venue string	quality_score float64
eebb5f82634b672b77e763fad1ac6c54b17384f857471be86c32a6883226b91f	[ "arxiv" ]	Structured Inference with Large Language Gibbs	The knowledge encoded in large language models (LLMs) can serve as a substrate for structured reasoning over variables describing a complex world, but accessing this knowledge in a probabilistically coherent manner poses a difficult inference problem. We propose Large Language Gibbs, a scheme for structured probabilist...	[ "Sanghyeok Choi", "Henry Gouk", "Esmeralda S. Whitammer" ]	[ "cs.LG", "cs.CL" ]	[]	2026-06-17T00:00:00	https://arxiv.org/abs/2606.19264	https://arxiv.org/pdf/2606.19264v1	2606.19264	null	0	0	true	https://github.com/hyeok9855/large-language-gibbs	null	0.65
e62eac19d405492b987241008282a6f5f4ab31ba1c081e4fa3ff8ea47c697650	[ "arxiv", "semantic_scholar" ]	Circuit Tracing in Autoregressive Protein Language Models	Protein language models (pLMs) can generate novel protein sequences with properties beyond those observed in nature, yet the mechanisms underlying protein generation remain poorly understood. Existing mechanistic interpretability methods based on sparse autoencoders and transcoders primarily focus on protein representa...	[ "Darin Tsui", "William Deinzer", "Daniel Saeedi", "Amirali Aghazadeh" ]	[ "cs.LG", "q-bio.QM" ]	[ "Computer Science", "Biology" ]	2026-06-14T00:00:00	https://arxiv.org/abs/2606.16044	https://arxiv.org/pdf/2606.16044v1	2606.16044	null	0	0	false	null	null	0.35
b4ea4f2bd4905955546c4b718c1176776c1aa0eca5c37d120f4c93bce089dd0e	[ "arxiv", "semantic_scholar" ]	Viral Proteins Reveal Geometry of Protein Language Models	Protein language models are trained on highly imbalanced datasets, raising the question of how they represent underrepresented biological sequences. Using viral proteins as a case study across ESM model families, we identify a dominant nativeness axis in embedding space, aligned with masked reconstruction perplexity, t...	[ "Arthur Bigot", "Harmon Bhasin", "Core Francisco Park", "Eugene Shakhnovich", "Dianzhuo Wang" ]	[ "cs.LG", "q-bio.QM" ]	[ "Computer Science", "Biology" ]	2026-06-10T00:00:00	https://arxiv.org/abs/2606.12609	https://arxiv.org/pdf/2606.12609v1	2606.12609	null	0	0	true	https://github.com/MisteFr/viral-proteins-plms	null	0.65
c49810ee97d0a51419cf749c4b4fa9f086d8e584ea6e8b149b965b62160da914	[ "arxiv", "semantic_scholar" ]	Interpretable enzyme function prediction via sparse autoencoder features of ESMC across the microbial protein universe	Microbial genomes and metagenomes contain millions of proteins whose enzymatic functions remain unknown, the enzyme dark matter. While deep learning has improved protein function prediction, most methods are black boxes relying on sequence or structural similarity, limiting discovery of novel catalytic activities. The ...	[ "Yue Hu", "Wanyu Cheng", "Junqing Wang", "Yingchao Liu" ]	[ "q-bio.QM" ]	[ "Biology" ]	2026-06-10T00:00:00	https://arxiv.org/abs/2606.12209	https://arxiv.org/pdf/2606.12209v1	2606.12209	null	0	0	false	null	null	0.35
11fb3a5074ccf50b67955588d254dd0415c57abf44f31d2d4a86f30758314396	[ "arxiv", "semantic_scholar" ]	The Dynamics of Human and AI-Generated Language: How Semantics Fluctuates across Different Timescales	Spoken language, whether produced by humans or large language models (LLM), unfolds over time with varying semantic content. However, we still lack simple, interpretable time-series features that capture how generic versus specific content is distributed over time, and that can be used to compare human and AI-generated...	[ "Han-Jen Chang", "Yasir Çatal", "Angelika Wolman", "Agustín Ibáñez", "David Smith", "I-Wen Su", "Kai-Yuan Cheng", "Georg Northoff" ]	[ "cs.CL", "cs.AI", "eess.AS", "eess.SP" ]	[ "Computer Science", "Engineering" ]	2026-06-09T00:00:00	https://arxiv.org/abs/2606.11371	https://arxiv.org/pdf/2606.11371v1	2606.11371	10.1016/j.csl.2026.102013	0	0	false	null	Computer Speech & Language (2026) 102013	0.55
73b2733d2ce24b808a8208758c7cf16e243f584bf9eb4076327effc258f373bb	[ "arxiv", "semantic_scholar" ]	Protein Dynamics Beyond Structure Prediction	The ability to predict protein three-dimensional structures from amino acid sequences is a landmark achievement in molecular biology, where recent deep learning approaches such as AlphaFold are the culmination of decades of work. Yet, the quantitative understanding of how protein sequences give rise to dynamic conforma...	[ "Juliette Griffié", "Sviatlana Shashkova", "Antonio Ciarlo", "Sreekanth K. Manikandan", "Claes Andréasson", "Malin Bäckström", "Tristan Bereau", "Hjalmar Brismar", "Carlos Bustamante", "Marta Carroni", "Roberto Covino", "Andreas Dahlin", "Sebastian Deindl", "Lucie Delemotte", "Arne Elofs...	[ "q-bio.BM", "cond-mat.mes-hall", "cond-mat.soft" ]	[ "Biology", "Physics" ]	2026-06-07T00:00:00	https://arxiv.org/abs/2606.08647	https://arxiv.org/pdf/2606.08647v1	2606.08647	null	0	0	false	null	null	0.35
8c80f94a1a6012d40c0562932106544a7d6f569f69e4ef60962c84aaf6c77f9e	[ "arxiv", "semantic_scholar" ]	AF_Cache: Efficient Pipeline for Running AlphaFold for High-Throughput Protein-Protein Interaction Prediction	Motivation: Accurate prediction of protein-protein interactions is essential for understanding biological processes, and recent advances such as AlphaFold2 and AlphaFold3 have enabled structure-based interaction prediction at unprecedented accuracy. However, the high computational cost of these methods, driven primaril...	[ "Sarah Narrowe", "Arne Elofsson Claudio Mirabello" ]	[ "q-bio.BM" ]	[ "Biology" ]	2026-06-03T00:00:00	https://arxiv.org/abs/2606.04566	https://arxiv.org/pdf/2606.04566v1	2606.04566	null	0	0	true	https://github.com/clami66/AF_cache	null	0.65
40e0bc6e12f212225681f7b0f972a0b20191b5b0c8b6c7a60c1372f27ea3d36d	[ "arxiv", "semantic_scholar" ]	Structure-Aware Prediction of PROTAC-Mediated Protein Degradability via Graph Neural Networks	Proteolysis-targeting chimeras (PROTACs) can selectively degrade disease-causing proteins, yet predicting which targets are amenable to degradation remains a critical bottleneck: existing computational methods require the complete PROTAC molecular structure, information unavailable before synthesis. We present DegradoM...	[ "Bryan Cheng", "Austin Jin" ]	[ "q-bio.QM", "cs.LG" ]	[ "Biology", "Computer Science" ]	2026-06-01T00:00:00	https://arxiv.org/abs/2606.04021	https://arxiv.org/pdf/2606.04021v1	2606.04021	null	0	0	false	null	null	0.35
2bbd135e974bdbf6d0306032a4adb89962a70f145e2c380cbb4063d72814d5e0	[ "arxiv", "semantic_scholar" ]	Structure-Guided Adaptive Propagation for Protein-Protein Interaction Site Prediction	Accurate prediction of protein-protein interaction sites (PPIS) is essential for understanding cellular processes, disease mechanisms, and therapeutic target discovery. Graph-based deep learning has advanced PPIS prediction by incorporating residue-level structural context. However, most graph-based models still rely o...	[ "Enqiang Zhu", "Yizi Liu", "Yilong Luo", "Yao Chen", "Yu Zhang", "Baoshan Ma" ]	[ "cs.AI" ]	[ "Computer Science" ]	2026-06-01T00:00:00	https://arxiv.org/abs/2606.01781	https://arxiv.org/pdf/2606.01781v1	2606.01781	null	0	0	false	null	null	0.35
8ca15f915155713f54943353f5b4b9e6af4ceabda10e34de0f67d0ef46d07d85	[ "arxiv", "semantic_scholar" ]	AMix-2: Establishing Protein as a Native Modality in Large Language Models	We present AMix-2, a protein-text foundation model that establishes protein as a native modality in large language models (LLMs), unifying protein understanding and sequence design within a single foundation model. AMix-2 is built upon two key ideas: (1) a unified protein-text formulation that embeds natural language a...	[ "Keyue Qiu", "Yixin Wu", "Lihao Wang", "Yawen Ouyang", "Jixiang Yu", "Zihan Zhou", "Changze Lv", "Dongyu Xue", "Yuxuan Song", "Xinbo Zhang", "Hao Wang", "Jiangtao Feng", "Zhiqiang Gao", "Lijun Wu", "Xiaoqing Zheng", "Ka-Chun Wong", "Lei Bai", "Ya-Qin Zhang", "Wei-Ying Ma", "Dah...	[ "q-bio.BM", "cs.AI" ]	[ "Biology", "Computer Science" ]	2026-05-29T00:00:00	https://arxiv.org/abs/2605.30963	https://arxiv.org/pdf/2605.30963v1	2605.30963	null	0	0	false	null	null	0.35
27ca13b36cf993fbd2645d1eeff7b54b139fc4615dc893021602240f66fde2fd	[ "arxiv", "semantic_scholar" ]	Atom-level Protein Representation Learning Improves Protein Structure Prediction	Recent advances in generative modeling show that pretrained representations can improve generation as conditioning features or alignment targets. Motivated by this, we study protein representations for predicting structures beyond conventional function annotation. We propose TriProRep, a structure-aware pretraining met...	[ "Taewon Kim", "Hyosoon Jang", "Hyunjin Seo", "Seonghwan Seo", "Hyeongwoo Kim", "Wonho Zhung", "Mingyeong Shin", "Wooyoun Kim", "Sungsoo Ahn" ]	[ "q-bio.BM", "cs.AI" ]	[ "Biology", "Computer Science" ]	2026-05-21T00:00:00	https://arxiv.org/abs/2605.22133	https://arxiv.org/pdf/2605.22133v3	2605.22133	null	0	0	false	null	null	0.35
877b175f6bb2cb864fffd0f095bb42ae4943f860f74df5445e19a4bbf23378de	[ "arxiv", "semantic_scholar" ]	Structural Interpretations of Protein Language Model Representations via Differentiable Graph Partitioning	Protein language models such as ESM-2 learn rich residue representations that achieve strong performance on protein function prediction, but their features remain difficult to interpret as structural $\&$ evolutionary signals are encoded in dense latent spaces. We propose a plug-$\&$-play framework that projects ESM-2 ...	[ "Siddhant Dutta", "Edward Tan Beng Wai", "Soumick Sarker", "Pasan Gunawardane", "Jagath C. Rajapakse" ]	[ "cs.LG", "cs.AI", "q-bio.BM" ]	[ "Computer Science", "Biology" ]	2026-05-09T00:00:00	https://arxiv.org/abs/2605.10985	https://arxiv.org/pdf/2605.10985v1	2605.10985	null	0	0	false	null	null	0.35
db93d0bc6e7ba59e810d3ca8284e31894b682acdf22b257e42ac0fbdac7dc1e6	[ "arxiv", "semantic_scholar" ]	ProteinJEPA: Latent prediction complements protein language models	Protein language models are trained primarily with masked language modeling (MLM), which predicts amino-acid identities at masked positions. We ask whether latent-space prediction can complement these token-level objectives under matched wall-clock budget. Across pretrained and random-init protein sequence encoders at ...	[ "Dan Ofer", "Dafna Shahaf", "Michal Linial" ]	[ "cs.LG", "cs.AI", "q-bio.BM", "stat.ML" ]	[ "Computer Science", "Biology", "Mathematics" ]	2026-05-08T00:00:00	https://arxiv.org/abs/2605.07554	https://arxiv.org/pdf/2605.07554v1	2605.07554	null	0	0	false	null	null	0.35
0e173453c857154935b528bc7b7cb2eabb08e7d9bcd1e397e97bf180b93df6c4	[ "arxiv", "semantic_scholar" ]	ProtSent: Protein Sentence Transformers	Protein language models (pLMs) produce per-residue representations that capture evolutionary and structural information, yet their mean-pooled sequence embeddings are not explicitly trained to reflect functional, evolutionary or structural similarity between proteins. We present Protein Sentence Transformers (ProtSent)...	[ "Dan Ofer", "Oriel Perets", "Michal Linial", "Nadav Rappoport" ]	[ "cs.LG", "cs.CL" ]	[ "Computer Science" ]	2026-05-07T00:00:00	https://arxiv.org/abs/2605.06830	https://arxiv.org/pdf/2605.06830v1	2605.06830	null	0	0	false	null	null	0.35
a002f1dab6946b4f6d0de16dc2bea7cb2812c9d0637198b47185b56397bfc963	[ "arxiv", "semantic_scholar" ]	Better Protein Function Prediction by Modeling Survivorship Bias	Protein sequence data from nature exhibits survivorship bias: we only observe data from those organisms that survive and reproduce, while non-functional protein mutations are eliminated by natural selection. Thus, predicting whether a protein sequence is functional often requires learning from positive examples alone. ...	[ "Zhongmou Chao", "Poompol Buathong", "Ekaterina Selivanovitch", "Susan Daniel", "Peter I. Frazier" ]	[ "cs.LG", "q-bio.QM" ]	[ "Computer Science", "Biology" ]	2026-05-07T00:00:00	https://arxiv.org/abs/2605.06879	https://arxiv.org/pdf/2605.06879v1	2605.06879	null	0	0	false	null	null	0.35
0160c746d795fb93edde86692344de66f756e9585e28f87f19d3c574e4c56d48	[ "arxiv", "semantic_scholar" ]	Targeted Linguistic Analysis of Sign Language Models with Minimal Translation Pairs	Models of sign language have historically lagged behind those for spoken language (text and speech). Recent work has greatly improved their performance on tasks like sign language translation and isolated sign recognition. However, it remains unclear to what extent existing models capture various linguistic phenomena o...	[ "Serpil Karabüklü", "Kanishka Misra", "Shester Gueuwou", "Diane Brentari", "Greg Shakhnarovich", "Karen Livescu" ]	[ "cs.CL" ]	[ "Computer Science" ]	2026-04-29T00:00:00	https://arxiv.org/abs/2604.27232	https://arxiv.org/pdf/2604.27232v2	2604.27232	10.48550/arXiv.2604.27232	0	0	false	null	arXiv.org	0.55
7b983dc87dcd7c6de4d0a1e0eaa81de2c6bbc08aecaf31285565e7426bb841a8	[ "arxiv", "semantic_scholar" ]	TriFit: Trimodal Fusion with Protein Dynamics for Mutation Fitness Prediction	Predicting the functional impact of single amino acid substitutions (SAVs) is central to understanding genetic disease and engineering therapeutic proteins. While protein language models and structure-based methods have achieved strong performance on this task, they systematically neglect protein dynamics; residue flex...	[ "Seungik Cho" ]	[ "cs.LG", "q-bio.BM", "q-bio.QM" ]	[ "Computer Science", "Biology" ]	2026-04-13T00:00:00	https://arxiv.org/abs/2604.12026	https://arxiv.org/pdf/2604.12026v1	2604.12026	10.48550/arXiv.2604.12026	0	0	false	null	arXiv.org	0.542
f6ef3dbd4c2d0ecc1c194c9780bc2c4a5c1737fcc4f9ca45959a2d45664202c8	[ "arxiv", "semantic_scholar" ]	Rethinking Token Prediction: Tree-Structured Diffusion Language Model	Discrete diffusion language models have emerged as a competitive alternative to auto-regressive language models, but training them efficiently under limited parameter and memory budgets remains challenging. Modern architectures are predominantly based on a full-vocabulary token prediction layer, which accounts for a su...	[ "Zihao Wu", "Haoming Yang", "Juncheng Dong", "Vahid Tarokh" ]	[ "cs.CL", "cs.LG" ]	[ "Computer Science" ]	2026-04-04T00:00:00	https://arxiv.org/abs/2604.03537	https://arxiv.org/pdf/2604.03537v1	2604.03537	10.48550/arXiv.2604.03537	0	0	false	null	arXiv.org	0.5317
5cd5b775f73bda757c9e0ee7d672d210d6921b01a58fc4bd66b545c57fabd49d	[ "arxiv", "semantic_scholar" ]	Sampling at intermediate temperatures is optimal for training large language models in protein structure prediction	We investigate the parameter space of transformer models trained on protein sequence data using a statistical mechanics framework, sampling the loss landscape at varying temperatures by Langevin dynamics to characterize the low-loss manifold and understand the mechanisms underlying the superior performance of transform...	[ "L. Ghiringhelli", "A. Zambon", "G. Tiana" ]	[ "cond-mat.dis-nn", "cs.LG", "q-bio.BM" ]	[ "Computer Science", "Physics", "Biology" ]	2026-03-31T00:00:00	https://arxiv.org/abs/2603.29529	https://arxiv.org/pdf/2603.29529v1	2603.29529	10.48550/arXiv.2603.29529	0	0	false	null	arXiv.org	0.5271
5a7bfbdbb17debee63d7dc76a7d9cd3d77aa9dc92a88a2dd96ce4a58b472aa10	[ "arxiv", "semantic_scholar" ]	Merge and Conquer: Instructing Multilingual Models by Adding Target Language Weights	Large Language Models (LLMs) remain heavily centered on English, with limited performance in low-resource languages. Existing adaptation approaches, such as continual pre-training, demand significant computational resources. In the case of instructed models, high-quality instruction data is also required, both of which...	[ "Eneko Valero", "Maria Ribalta i Albado", "Oscar Sainz", "Naiara Perez", "German Rigau" ]	[ "cs.CL", "cs.AI" ]	[ "Computer Science" ]	2026-03-30T00:00:00	https://arxiv.org/abs/2603.28263	https://arxiv.org/pdf/2603.28263v1	2603.28263	10.48550/arXiv.2603.28263	0	0	false	null	arXiv.org	0.5259
9847154ed392bb82e7bc3ce3b3a65071cf778f1242631fc4cef375fc4dae1124	[ "arxiv", "semantic_scholar" ]	Introducing MELI: the Mandarin-English Language Interview Corpus	We introduce the Mandarin-English Language Interview (MELI) Corpus, an open-source resource of 29.8 hours of speech from 51 Mandarin-English bilingual speakers. MELI combines matched sessions in Mandarin and English with two speaking styles: read sentences and spontaneous interviews about language varieties, standardne...	[ "Suyuan Liu", "Molly Babel" ]	[ "cs.CL" ]	[ "Computer Science" ]	2026-03-27T00:00:00	https://arxiv.org/abs/2603.27043	https://arxiv.org/pdf/2603.27043v2	2603.27043	10.63317/3umiyc4sxwhk	0	0	true	null	arXiv.org	0.8075
1a8e942c570ec1a135a7c5d4dc93befb71353719b61f0b94db814cd5c9d2833d	[ "arxiv", "semantic_scholar" ]	Central Dogma Transformer III: Interpretable AI Across DNA, RNA, and Protein	Biological AI models increasingly predict complex cellular responses, yet their learned representations remain disconnected from the molecular processes they aim to capture. We present CDT-III, which extends mechanism-oriented AI across the full central dogma: DNA, RNA, and protein. Its two-stage Virtual Cell Embedder ...	[ "Nobuyuki Ota" ]	[ "cs.LG", "q-bio.GN" ]	[ "Computer Science", "Biology" ]	2026-03-24T00:00:00	https://arxiv.org/abs/2603.23361	https://arxiv.org/pdf/2603.23361v2	2603.23361	10.48550/arXiv.2603.23361	0	0	false	null	arXiv.org	0.5191
74b4d0a833d0a0d211a85ff9a40d6f83f116c8f371a835e80fef0e0af83894b0	[ "arxiv", "semantic_scholar" ]	Task-Specific Efficiency Analysis: When Small Language Models Outperform Large Language Models	Large Language Models achieve remarkable performance but incur substantial computational costs unsuitable for resource-constrained deployments. This paper presents the first comprehensive task-specific efficiency analysis comparing 16 language models across five diverse NLP tasks. We introduce the Performance-Efficienc...	[ "Jinghan Cao", "Yu Ma", "Xinjin Li", "Qingyang Ren", "Xiangyun Chen" ]	[ "cs.CL", "cs.LG" ]	[ "Computer Science" ]	2026-03-22T00:00:00	https://arxiv.org/abs/2603.21389	https://arxiv.org/pdf/2603.21389v1	2603.21389	10.14428/esann/2026.es2026-274	9	1	false	null	The European Symposium on Artificial Neural Networks	0.5168
ca82ff6b387612787890f2f522dc113d867a0ef39f15c8b538fcc7d49f7fa677	[ "arxiv", "semantic_scholar" ]	Cross-Granularity Representations for Biological Sequences: Insights from ESM and BiGCARP	Recent advances in general-purpose foundation models have stimulated the development of large biological sequence models. While natural language shows symbolic granularity (characters, words, sentences), biological sequences exhibit hierarchical granularity whose levels (nucleotides, amino acids, protein domains, genes...	[ "Hanlin Xiao", "Rainer Breitling", "Eriko Takano", "Mauricio A. Álvarez" ]	[ "cs.LG" ]	[ "Computer Science" ]	2026-03-21T00:00:00	https://arxiv.org/abs/2603.20825	https://arxiv.org/pdf/2603.20825v1	2603.20825	10.1109/BIBM66473.2025.11356265	0	0	false	null	IEEE International Conference on Bioinformatics and Biomedicine	0.5156
f62f079d344668a8da9e108e465f468bc97ae2dfc32bc99307cd0a72e3d24188	[ "arxiv", "semantic_scholar" ]	From Snapshots to Symphonies: The Evolution of Protein Prediction from Static Structures to Generative Dynamics and Multimodal Interactions	The protein folding problem has been fundamentally transformed by artificial intelligence, evolving from static structure prediction toward the modeling of dynamic conformational ensembles and complex biomolecular interactions. This review systematically examines the paradigm shift in AI driven protein science across f...	[ "Jingzhi Chen", "Lijian Xu" ]	[ "cs.CV" ]	[ "Computer Science" ]	2026-03-19T00:00:00	https://arxiv.org/abs/2603.18505	https://arxiv.org/pdf/2603.18505v1	2603.18505	10.48550/arXiv.2603.18505	1	0	false	null	arXiv.org	0.5133
8b422bdf48b01054475ad524262a98b442d93a3196c1372bab5ed383b7c27c79	[ "arxiv", "semantic_scholar" ]	Integrative modelling of protein-glycan interactions with HADDOCK3	Glycans are structurally diverse and flexible biomolecules that play key roles in many biological processes. Their conformational variability makes the modeling of their interactions with proteins particularly challenging. This chapter presents a step-by-step protocol for modeling protein-glycan interactions using HADD...	[ "Victor Reys", "Marco Giulini", "Alexandre M. J. J. Bonvin" ]	[ "q-bio.BM" ]	[ "Biology" ]	2026-03-18T00:00:00	https://arxiv.org/abs/2603.17251	https://arxiv.org/pdf/2603.17251v1	2603.17251	null	0	0	false	null	null	0.3259
5c142e2cc97364a477a5751796bb3145d048a3a33452ce2c4487484b411bf719	[ "arxiv", "semantic_scholar" ]	Robust Language Identification for Romansh Varieties	The Romansh language has several regional varieties, called idioms, which sometimes have limited mutual intelligibility. Despite this linguistic diversity, there has been a lack of documented efforts to build a language identification (LID) system that can distinguish between these idioms. Since Romansh LID should also...	[ "Charlotte Model", "Sina Ahmadi", "Jannis Vamvas" ]	[ "cs.CL" ]	[ "Computer Science" ]	2026-03-16T00:00:00	https://arxiv.org/abs/2603.15969	https://arxiv.org/pdf/2603.15969v2	2603.15969	10.48550/arXiv.2603.15969	1	0	false	null	arXiv.org	0.5099
546c6c9d10d36a66d8559116e5310a5fac6b86ec200f9c2eeb9fc5a55770ee28	[ "arxiv", "semantic_scholar" ]	Reverse Distillation: Consistently Scaling Protein Language Model Representations	Unlike the predictable scaling laws in natural language processing and computer vision, protein language models (PLMs) scale poorly: for many tasks, models within the same family plateau or even decrease in performance, with mid-sized models often outperforming the largest in the family. We introduce Reverse Distillati...	[ "Darius Catrina", "Christian Bepler", "Samuel Sledzieski", "Rohit Singh" ]	[ "cs.LG", "q-bio.BM" ]	[ "Computer Science", "Biology" ]	2026-03-08T00:00:00	https://arxiv.org/abs/2603.07710	https://arxiv.org/pdf/2603.07710v1	2603.07710	10.48550/arXiv.2603.07710	1	0	true	https://github.com/rohitsinghlab/plm_reverse_distillation	arXiv.org	0.7739
354177cd48b5857eb8de69946e5d2468cdcc5165bb0f02b26bc6e2c5313fd3ae	[ "arxiv", "semantic_scholar" ]	Inference-Time Toxicity Mitigation in Protein Language Models	Protein language models (PLMs) are becoming practical tools for de novo protein design, yet their dual-use potential raises safety concerns. We show that domain adaptation to specific taxonomic groups can elicit toxic protein generation, even when toxicity is not the training objective. To address this, we adapt Logit ...	[ "Manuel Fernández Burda", "Santiago Aranguri", "Iván Arcuschin Moreno", "Enzo Ferrante" ]	[ "cs.LG", "cs.AI" ]	[ "Computer Science" ]	2026-03-04T00:00:00	https://arxiv.org/abs/2603.04045	https://arxiv.org/pdf/2603.04045v1	2603.04045	10.48550/arXiv.2603.04045	0	0	false	null	arXiv.org	0.4961
7d765f6bd5fc5ccd6161eea473c9184004ad2e915edd17dadba0cc3210530035	[ "arxiv", "semantic_scholar" ]	Building a Strong Instruction Language Model for a Less-Resourced Language	Large language models (LLMs) have become an essential tool for natural language processing and artificial intelligence in general. Current open-source models are primarily trained on English texts, resulting in poorer performance on less-resourced languages and cultures. We present a set of methodological approaches ne...	[ "Domen Vreš", "Tjaša Arčon", "Timotej Petrič", "Dario Vajda", "Marko Robnik-Šikonja", "Iztok Lebar Bajec" ]	[ "cs.CL", "cs.LG" ]	[ "Computer Science" ]	2026-03-02T00:00:00	https://arxiv.org/abs/2603.01691	https://arxiv.org/pdf/2603.01691v1	2603.01691	10.48550/arXiv.2603.01691	1	0	true	null	arXiv.org	0.7632
fa76891b27c573bb84ee4a7a5f133548d5e487aab28969c9776638fd8e2a5e01	[ "arxiv", "semantic_scholar" ]	CoPeP: Benchmarking Continual Pretraining for Protein Language Models	Protein language models (pLMs) have recently gained significant attention for their ability to uncover relationships between sequence, structure, and function from evolutionary statistics, thereby accelerating therapeutic drug discovery. These models learn from large protein databases that are continuously updated by t...	[ "Darshan Patil", "Pranshu Malviya", "Mathieu Reymond", "Quentin Fournier", "Sarath Chandar" ]	[ "cs.LG" ]	[ "Computer Science" ]	2026-02-27T00:00:00	https://arxiv.org/abs/2603.00253	https://arxiv.org/pdf/2603.00253v2	2603.00253	10.48550/arXiv.2603.00253	0	0	false	null	arXiv.org	0.4904
cd1ae30c83a185e14356aaf886458a66daa9f1d6c5c56030eb41238790c1e750	[ "arxiv", "semantic_scholar" ]	Protein Language Models Diverge from Natural Language: Comparative Analysis and Improved Inference	Modern Protein Language Models (PLMs) apply transformer-based model architectures from natural language processing to biological sequences, predicting a variety of protein functions and properties. However, protein language has key differences from natural language, such as a rich functional space despite a vocabulary ...	[ "Anna Hart", "Chi Han", "Jeonghwan Kim", "Huimin Zhao", "Heng Ji" ]	[ "cs.LG", "cs.AI", "cs.CL", "q-bio.BM" ]	[ "Computer Science", "Biology" ]	2026-02-24T00:00:00	https://arxiv.org/abs/2602.20449	https://arxiv.org/pdf/2602.20449v1	2602.20449	10.48550/arXiv.2602.20449	0	0	false	null	arXiv.org	0.487
fce9ef0f1765578e157b0d25acca84cc9ee92bd3dce842954e8a8c18b188401c	[ "arxiv", "semantic_scholar" ]	BeamVLM for Low-altitude Economy: Generative Beam Prediction via Vision-language Models	For low-altitude economy (LAE), fast and accurate beam prediction between high-mobility unmanned aerial vehicles (UAVs) and ground base stations is of paramount importance, which ensures seamless coverage and reliable communications. However, existing deep learning-based beam prediction methods lack high-level semantic...	[ "Chenran Kou", "Changsheng You", "Mingjiang Wu", "Dingzhu Wen", "Zezhong Zhang", "Chengwen Xing" ]	[ "cs.NI", "cs.IT" ]	[ "Computer Science", "Mathematics" ]	2026-02-23T00:00:00	https://arxiv.org/abs/2602.19929	https://arxiv.org/pdf/2602.19929v1	2602.19929	10.48550/arXiv.2602.19929	1	0	false	null	arXiv.org	0.4858
39dcd2d9ac944e68a62b34cb8a42d6e302188d247e477387a052da3067cc84c8	[ "arxiv", "semantic_scholar" ]	STProtein: predicting spatial protein expression from multi-omics data	The integration of spatial multi-omics data from single tissues is crucial for advancing biological research. However, a significant data imbalance impedes progress: while spatial transcriptomics data is relatively abundant, spatial proteomics data remains scarce due to technical limitations and high costs. To overcome...	[ "Zhaorui Jiang", "Yingfang Yuan", "Lei Hu", "Wei Pang" ]	[ "cs.AI" ]	[ "Computer Science" ]	2026-02-05T00:00:00	https://arxiv.org/abs/2602.05811	https://arxiv.org/pdf/2602.05811v1	2602.05811	10.48550/arXiv.2602.05811	0	0	true	https://github.com/zhaorui-bi/STProtein	arXiv.org	0.719
25dc729932d8178547f234c0a1b91071752207d8855a087cef99fcfa5a038908	[ "arxiv", "semantic_scholar" ]	Controlling Repetition in Protein Language Models	Protein language models (PLMs) have enabled advances in structure prediction and de novo protein design, yet they frequently collapse into pathological repetition during generation. Unlike in text, where repetition merely reduces readability, in proteins it undermines structural confidence and functional viability. To ...	[ "Jiahao Zhang", "Zeqing Zhang", "Di Wang", "Lijie Hu" ]	[ "q-bio.BM", "cs.AI" ]	[ "Biology", "Computer Science" ]	2026-01-31T00:00:00	https://arxiv.org/abs/2602.00782	https://arxiv.org/pdf/2602.00782v1	2602.00782	10.48550/arXiv.2602.00782	3	0	false	null	arXiv.org	0.4595
3b1e983c10ad679f44efe516c25757ed09fc760ae26e3899d43f8d0b605a9300	[ "arxiv", "semantic_scholar" ]	CalPro: Prior-Aware Evidential--Conformal Prediction with Structure-Aware Guarantees for Protein Structures	Deep protein structure predictors such as AlphaFold provide confidence estimates (e.g., pLDDT) that are often miscalibrated and degrade under distribution shifts across experimental modalities, temporal changes, and intrinsically disordered regions. We introduce CalPro, a prior-aware evidential-conformal framework for ...	[ "Ibne Farabi Shihab", "Sanjeda Akter", "Anuj Sharma" ]	[ "cs.LG", "cs.AI" ]	[ "Computer Science" ]	2026-01-12T00:00:00	https://arxiv.org/abs/2601.07201	https://arxiv.org/pdf/2601.07201v1	2601.07201	10.48550/arXiv.2601.07201	0	0	false	null	arXiv.org	0.4377
8c06ab9f0ef554e926895b82f6f23c06c9ccf8d15125591eaf5ba6433931edba	[ "arxiv", "semantic_scholar" ]	Knowledge Distillation of a Protein Language Model Yields a Foundational Implicit Solvent Model	Implicit solvent models (ISMs) promise to deliver the accuracy of explicit solvent simulations at a fraction of the computational cost. However, despite decades of development, their accuracy has remained insufficient for many critical applications, particularly for simulating protein folding and the behavior of intrin...	[ "Justin Airas", "Bin Zhang" ]	[ "physics.bio-ph", "physics.chem-ph", "physics.comp-ph" ]	[ "Physics", "Medicine" ]	2026-01-08T00:00:00	https://arxiv.org/abs/2601.05388	https://arxiv.org/pdf/2601.05388v2	2601.05388	null	3	0	false	null	arXiv.org	0.4331
8108741252aedfc85cdc48b2017260c56b8adc01e8f4b65009708ca6b1b25d1d	[ "arxiv", "semantic_scholar" ]	Quantum Simulation of Protein Fragment Electronic Structure Using Moment-based Adaptive Variational Quantum Algorithms	Background: Understanding electronic interactions in protein active sites is fundamental to drug discovery and enzyme engineering, but remains computationally challenging due to exponential scaling of quantum mechanical calculations. Results: We present a quantum-classical hybrid framework for simulating protein fragme...	[ "Biraja Ghoshal" ]	[ "q-bio.QM", "cs.ET" ]	[ "Biology", "Computer Science" ]	2026-01-02T00:00:00	https://arxiv.org/abs/2601.00656	https://arxiv.org/pdf/2601.00656v1	2601.00656	10.48550/arXiv.2601.00656	0	0	false	null	arXiv.org	0.4263
3a3f618d9a2d8c4f089f480f98da544cf9129429dee02de1958c9b28294e2f01	[ "arxiv", "semantic_scholar" ]	Physio-DPO: Aligning Large Language Models with the Protein Energy Landscape to Eliminate Structural Hallucinations	Large Protein Language Models have shown strong potential for generative protein design, yet they frequently produce structural hallucinations, generating sequences with high linguistic likelihood that fold into thermodynamically unstable conformations. Existing alignment approaches such as Direct Preference Optimizati...	[ "QiWei Meng" ]	[ "cs.CL", "cs.CE", "q-bio.QM" ]	[ "Computer Science", "Biology" ]	2026-01-02T00:00:00	https://arxiv.org/abs/2601.00647	https://arxiv.org/pdf/2601.00647v1	2601.00647	10.48550/arXiv.2601.00647	1	0	false	null	arXiv.org	0.4263
ef39d241eab949d3efe800528741c9edc69bf639b9982c7d2bc4f769fdcf64a3	[ "arxiv", "semantic_scholar" ]	HD-Prot: A Protein Language Model for Joint Sequence-Structure Modeling with Continuous Structure Tokens	Proteins inherently possess a consistent sequence-structure duality. The abundance of protein sequence data, which can be readily represented as discrete tokens, has driven fruitful developments in protein language models (pLMs). A key remaining challenge, however, is how to effectively integrate continuous structural ...	[ "Yi Zhou", "Haohao Qu", "Yunqing Liu", "Shanru Lin", "Le Song", "Wenqi Fan" ]	[ "cs.CE", "cs.AI" ]	[ "Computer Science" ]	2025-12-17T00:00:00	https://arxiv.org/abs/2512.15133	https://arxiv.org/pdf/2512.15133v3	2512.15133	10.48550/arXiv.2512.15133	4	0	false	null	arXiv.org	0.4079
8a43a9c0833ff2879a90ef7a720ceb443aba6543f1531486d21d70e48f00eff3	[ "arxiv", "semantic_scholar" ]	Large language models have learned to use language	Acknowledging that large language models have learned to use language can open doors to breakthrough language science. Achieving these breakthroughs may require abandoning some long-held ideas about how language knowledge is evaluated and reckoning with the difficult fact that we have entered a post-Turing test era.	[ "Gary Lupyan" ]	[ "cs.CL" ]	[ "Computer Science" ]	2025-12-13T00:00:00	https://arxiv.org/abs/2512.12447	https://arxiv.org/pdf/2512.12447v1	2512.12447	10.48550/arXiv.2512.12447	0	0	false	null	arXiv.org	0.4033
3274b8e6361e5f626a23dcdf3d21269828073d7b99bef850e3fd10e198486cf9	[ "arxiv", "semantic_scholar" ]	Self Distillation Fine-Tuning of Protein Language Models Improves Versatility in Protein Design	Supervised fine-tuning (SFT) is a standard approach for adapting large language models to specialized domains, yet its application to protein sequence modeling and protein language models (PLMs) remains ad hoc. This is in part because high-quality annotated data are far more difficult to obtain for proteins than for na...	[ "Amin Tavakoli", "Raswanth Murugan", "Ozan Gokdemir", "Arvind Ramanathan", "Frances Arnold", "Anima Anandkumar" ]	[ "cs.LG", "cs.CE" ]	[ "Computer Science" ]	2025-12-10T00:00:00	https://arxiv.org/abs/2512.09329	https://arxiv.org/pdf/2512.09329v1	2512.09329	10.48550/arXiv.2512.09329	0	0	false	null	arXiv.org	0.3999
84809160d7b398c91a840edeca946d2c754922ae674dc128c66faaf21cbac684	[ "arxiv", "semantic_scholar" ]	Soft Inductive Bias Approach via Explicit Reasoning Perspectives in Inappropriate Utterance Detection Using Large Language Models	Recent incidents in certain online games and communities, where anonymity is guaranteed, show that unchecked inappropriate remarks frequently escalate into verbal abuse and even criminal behavior, raising significant social concerns. Consequently, there is a growing need for research on techniques that can detect inapp...	[ "Ju-Young Kim", "Ji-Hong Park", "Se-Yeon Lee", "Sujin Park", "Gun-Woo Kim" ]	[ "cs.CL" ]	[ "Computer Science" ]	2025-12-09T00:00:00	https://arxiv.org/abs/2512.08480	https://arxiv.org/pdf/2512.08480v1	2512.08480	10.48550/arXiv.2512.08480	0	0	false	null	arXiv.org	0.3987
9ba643e751032fea310460ad18dd169975fd9a5b6f0e49b49795abedebb99662	[ "arxiv", "semantic_scholar" ]	Protein Secondary Structure Prediction Using Transformers	Predicting protein secondary structures such as alpha helices, beta sheets, and coils from amino acid sequences is essential for understanding protein function. This work presents a transformer-based model that applies attention mechanisms to protein sequence data to predict structural motifs. A sliding-window data aug...	[ "Manzi Kevin Maxime" ]	[ "cs.AI" ]	[ "Computer Science" ]	2025-12-09T00:00:00	https://arxiv.org/abs/2512.08613	https://arxiv.org/pdf/2512.08613v1	2512.08613	10.48550/arXiv.2512.08613	0	0	false	null	arXiv.org	0.3987
99d189816a72fcfcddc3b3cdacae324df68e34ffef6119675ee59b392a606c82	[ "arxiv", "semantic_scholar" ]	Classifying German Language Proficiency Levels Using Large Language Models	Assessing language proficiency is essential for education, as it enables instruction tailored to learners needs. This paper investigates the use of Large Language Models (LLMs) for automatically classifying German texts according to the Common European Framework of Reference for Languages (CEFR) into different proficie...	[ "Elias-Leander Ahlers", "Witold Brunsmann", "Malte Schilling" ]	[ "cs.CL", "cs.AI" ]	[ "Computer Science" ]	2025-12-06T00:00:00	https://arxiv.org/abs/2512.06483	https://arxiv.org/pdf/2512.06483v1	2512.06483	10.1109/FLLM67465.2025.11390912	1	1	false	null	null	0.2516
edde90edeaeb66075f25b08e388d7a8200913a436424fafe35130994ca6732d5	[ "arxiv", "semantic_scholar" ]	Small Language Models Reshape Higher Education: Courses, Textbooks, and Teaching	While large language models (LLMs) have introduced novel paradigms in science and education, their adoption in higher education is constrained by inherent limitations. These include a tendency to produce inaccuracies and high computational requirements, which compromise the strict demands for accurate and reliable know...	[ "Jian Zhang", "Jia Shao" ]	[ "physics.ed-ph", "cs.CL" ]	[ "Physics", "Computer Science" ]	2025-12-02T00:00:00	https://arxiv.org/abs/2512.06001	https://arxiv.org/pdf/2512.06001v1	2512.06001	10.48550/arXiv.2512.06001	0	0	false	null	arXiv.org	0.3907
a0d097ff2267023d9e4988fc24ed75aa95ef7a815bdd28db41101eee7a0dfc6f	[ "arxiv", "semantic_scholar" ]	Layer Probing Improves Kinase Functional Prediction with Protein Language Models	Protein language models (PLMs) have transformed sequence-based protein analysis, yet most applications rely only on final-layer embeddings, which may overlook biologically meaningful information encoded in earlier layers. We systematically evaluate all 33 layers of ESM-2 for kinase functional prediction using both unsu...	[ "Ajit Kumar", "IndraPrakash Jha" ]	[ "q-bio.QM", "cs.AI", "cs.LG" ]	[ "Computer Science", "Biology" ]	2025-11-29T00:00:00	https://arxiv.org/abs/2512.00376	https://arxiv.org/pdf/2512.00376v1	2512.00376	10.48550/arXiv.2512.00376	1	0	false	null	arXiv.org	0.3873
a99edac5fb61c22f532891c8c445ed6b3de89af4abe31d2f6d1e5946f58699c1	[ "arxiv", "semantic_scholar" ]	Language-conditioned world model improves policy generalization by reading environmental descriptions	To interact effectively with humans in the real world, it is important for agents to understand language that describes the dynamics of the environment--that is, how the environment behaves--rather than just task instructions specifying "what to do". Understanding this dynamics-descriptive language is important for hum...	[ "Anh Nguyen", "Stefan Lee" ]	[ "cs.CL", "cs.LG" ]	[ "Computer Science" ]	2025-11-28T00:00:00	https://arxiv.org/abs/2511.22904	https://arxiv.org/pdf/2511.22904v1	2511.22904	10.48550/arXiv.2511.22904	0	0	false	null	arXiv.org	0.3861
1d2eefd3f45c2512327996cda188ad850678166a12210efbdc9fa5ea932f8663	[ "arxiv", "semantic_scholar" ]	Swarms of Large Language Model Agents for Protein Sequence Design with Experimental Validation	Designing proteins de novo with tailored structural, physicochemical, and functional properties remains a grand challenge in biotechnology, medicine, and materials science, due to the vastness of sequence space and the complex coupling between sequence, structure, and function. Current state-of-the-art generative metho...	[ "Fiona Y. Wang", "Di Sheng Lee", "David L. Kaplan", "Markus J. Buehler" ]	[ "cs.AI", "cond-mat.mes-hall", "cond-mat.soft", "cs.CL", "cs.LG" ]	[ "Computer Science", "Physics" ]	2025-11-27T00:00:00	https://arxiv.org/abs/2511.22311	https://arxiv.org/pdf/2511.22311v1	2511.22311	10.48550/arXiv.2511.22311	4	0	false	null	arXiv.org	0.385
8a89f3b0e767124b3ecac378cd663d2380ea5d550ae836716db1baf52997f70a	[ "arxiv", "semantic_scholar" ]	DeepPNI: Language- and graph-based model for mutation-driven protein-nucleic acid energetics	The interaction between proteins and nucleic acids is crucial for processes that sustain cellular function, including DNA maintenance and the regulation of gene expression and translation. Amino acid mutations in protein-nucleic acid complexes often lead to vital diseases. Experimental techniques have their own specifi...	[ "Somnath Mondal", "Tinkal Mondal", "Soumajit Pramanik", "Rukmankesh Mehra" ]	[ "q-bio.BM", "cs.AI" ]	[ "Biology", "Computer Science" ]	2025-11-27T00:00:00	https://arxiv.org/abs/2511.22239	https://arxiv.org/pdf/2511.22239v1	2511.22239	10.48550/arXiv.2511.22239	0	0	false	null	arXiv.org	0.385
b5353a619eaea8460535a73e12d9d3852e5dd4eb66907753d18382b152f074cc	[ "arxiv", "semantic_scholar" ]	Protein Secondary Structure Prediction Using 3D Graphs and Relation-Aware Message Passing Transformers	In this study, we tackle the challenging task of predicting secondary structures from protein primary sequences, a pivotal initial stride towards predicting tertiary structures, while yielding crucial insights into protein activity, relationships, and functions. Existing methods often utilize extensive sets of unlabele...	[ "Disha Varshney", "Samarth Garg", "Sarthak Tyagi", "Deeksha Varshney", "Nayan Deep", "Asif Ekbal" ]	[ "cs.LG", "cs.AI" ]	[ "Computer Science" ]	2025-11-17T00:00:00	https://arxiv.org/abs/2511.13685	https://arxiv.org/pdf/2511.13685v1	2511.13685	10.48550/arXiv.2511.13685	0	0	false	null	arXiv.org	0.3735
859d10bcf2c0d311d4ca6c85f4ab9d3caf30455d644e046df07579d451de2c74	[ "arxiv", "semantic_scholar" ]	Studies with impossible languages falsify LMs as models of human language	According to Futrell and Mahowald [arXiv:2501.17047], both infants and language models (LMs) find attested languages easier to learn than impossible languages that have unnatural structures. We review the literature and show that LMs often learn attested and many impossible languages equally well. Difficult to learn im...	[ "Jeffrey S. Bowers", "Jeff Mitchell" ]	[ "cs.CL" ]	[ "Computer Science" ]	2025-11-14T00:00:00	https://arxiv.org/abs/2511.11389	https://arxiv.org/pdf/2511.11389v1	2511.11389	10.48550/arXiv.2511.11389	1	0	false	null	arXiv.org	0.3701
f90693cfcb34bb06f6da918410e6071af0a85d07f8c013ffed0acd13a591e1dc	[ "arxiv", "semantic_scholar" ]	Boosting In-Silicon Directed Evolution with Fine-Tuned Protein Language Model and Tree Search	Protein evolution through amino acid mutations is a cornerstone of life sciences. Recent advances in protein language models have shown rich evolutionary patterns, offering unprecedented potential for in-silicon directed evolution. However, existing directed evolution methods largely rely on heuristic evolution strateg...	[ "Yaodong Yang", "Yang Wang", "Jinpeng Li", "Pei Guo", "Da Han", "Guangyong Chen", "Pheng-Ann Heng" ]	[ "cs.AI", "cs.CE" ]	[ "Computer Science" ]	2025-11-13T00:00:00	https://arxiv.org/abs/2511.09900	https://arxiv.org/pdf/2511.09900v4	2511.09900	10.48550/arXiv.2511.09900	0	0	false	null	arXiv.org	0.369
db887845bcd21311022f915d4f4cb72543394233d12ba77db512a8983536728d	[ "arxiv", "semantic_scholar" ]	From Static Structures to Ensembles: Studying and Harnessing Protein Structure Tokenization	Protein structure tokenization converts 3D structures into discrete or vectorized representations, enabling the integration of structural and sequence data. Despite many recent works on structure tokenization, the properties of the underlying discrete representations are not well understood. In this work, we first demo...	[ "Zijing Liu", "Bin Feng", "He Cao", "Yu Li" ]	[ "cs.LG" ]	[ "Computer Science" ]	2025-11-13T00:00:00	https://arxiv.org/abs/2511.10056	https://arxiv.org/pdf/2511.10056v1	2511.10056	10.48550/arXiv.2511.10056	1	0	true	https://github.com/IDEA-XL/TokenMD	arXiv.org	0.5702
fb4597c9e9b5aabf4aadfd5fe87bb078c02f418eb222ac34b819275b6da1526e	[ "arxiv", "semantic_scholar" ]	Joint Design of Protein Surface and Structure Using a Diffusion Bridge Model	Protein-protein interactions (PPIs) are governed by surface complementarity and hydrophobic interactions at protein interfaces. However, designing diverse and physically realistic protein structure and surfaces that precisely complement target receptors remains a significant challenge in computational protein design. I...	[ "Guanlue Li", "Xufeng Zhao", "Fang Wu", "Sören Laue" ]	[ "cs.LG", "cs.AI" ]	[ "Computer Science" ]	2025-11-08T00:00:00	https://arxiv.org/abs/2511.16675	https://arxiv.org/pdf/2511.16675v1	2511.16675	10.48550/arXiv.2511.16675	0	0	false	null	arXiv.org	0.3632
3ae6593ec242f09e125cff955f93079e9f444726bd1bbd13887809491bd31d73	[ "arxiv", "semantic_scholar" ]	Quantifying the Role of OpenFold Components in Protein Structure Prediction	Models such as AlphaFold2 and OpenFold have transformed protein structure prediction, yet their inner workings remain poorly understood. We present a methodology to systematically evaluate the contribution of individual OpenFold components to structure prediction accuracy. We identify several components that are critic...	[ "Tyler L. Hayes", "Giri P. Krishnan" ]	[ "q-bio.BM", "cs.AI" ]	[ "Biology", "Computer Science" ]	2025-11-06T00:00:00	https://arxiv.org/abs/2511.14781	https://arxiv.org/pdf/2511.14781v1	2511.14781	10.48550/arXiv.2511.14781	1	0	false	null	arXiv.org	0.3609
196d7f0b69a25eb0d0446bc422d71c79a3a9a9a39a01cb2c7361217e9b974e66	[ "arxiv", "semantic_scholar" ]	GeoPep: A geometry-aware masked language model for protein-peptide binding site prediction	Multimodal approaches that integrate protein structure and sequence have achieved remarkable success in protein-protein interface prediction. However, extending these methods to protein-peptide interactions remains challenging due to the inherent conformational flexibility of peptides and the limited availability of st...	[ "Dian Chen", "Yunkai Chen", "Tong Lin", "Sijie Chen", "Xiaolin Cheng" ]	[ "eess.SP", "cs.LG" ]	[ "Computer Science", "Engineering" ]	2025-10-30T00:00:00	https://arxiv.org/abs/2510.27040	https://arxiv.org/pdf/2510.27040v1	2510.27040	10.48550/arXiv.2510.27040	1	0	false	null	arXiv.org	0.3529
4f5c67c8776219add5efccfd1dd935c1f2d5cc687012b88b01ecadc3093d86be	[ "arxiv", "semantic_scholar" ]	Precision Design of Cyclic Peptides using AlphaFold	This independent research investigates methods to improve the precision of cyclic peptide generation targeting the HIV gp120 trimer using AlphaFold. The study explores proximity-based hotspot mapping at the CD4 binding site, centroid distance penalization, generative loss tuning, and custom loss function development. T...	[ "Cheuk Sau Au" ]	[ "q-bio.BM" ]	[ "Biology" ]	2025-10-15T00:00:00	https://arxiv.org/abs/2510.13127	https://arxiv.org/pdf/2510.13127v1	2510.13127	null	0	0	false	null	null	0.2136
9f4b6fd0003622d2b0f41321ed3835c8a74e1588375112fe263338aa82471af1	[ "arxiv", "semantic_scholar" ]	Protein as a Second Language for LLMs	Deciphering the function of unseen protein sequences is a fundamental challenge with broad scientific impact, yet most existing methods depend on task-specific adapters or large-scale supervised fine-tuning. We introduce the "Protein-as-Second-Language" framework, which reformulates amino-acid sequences as sentences in...	[ "Xinhui Chen", "Zuchao Li", "Mengqi Gao", "Yufeng Zhang", "Chak Tou Leong", "Haoyang Li", "Jiaqi Chen" ]	[ "cs.LG", "cs.AI", "q-bio.BM" ]	[ "Computer Science", "Biology" ]	2025-10-13T00:00:00	https://arxiv.org/abs/2510.11188	https://arxiv.org/pdf/2510.11188v1	2510.11188	10.48550/arXiv.2510.11188	0	0	true	null	arXiv.org	0.5153
cfaabc6209ae2cfb268efc15bbadb592d452e9c2e47c2e08bf452883fabc0bac	[ "arxiv", "semantic_scholar" ]	A Hybrid Quantum-AI Framework for Protein Structure Prediction on NISQ Devices	Variational quantum algorithms provide a direct, physics-based approach to protein structure prediction, but their accuracy is limited by the coarse resolution of the energy landscapes generated on current noisy devices. We propose a hybrid framework that combines quantum computation with deep learning, formulating str...	[ "Yuqi Zhang", "Yuxin Yang", "Feixiong Chen", "Cheng-Chang Lu", "Nima Saeidi", "Samuel L. Volchenboum", "Junhan Zhao", "Siwei Chen", "Weiwen Jiang", "Qiang Guan" ]	[ "cs.ET" ]	[ "Computer Science" ]	2025-10-07T00:00:00	https://arxiv.org/abs/2510.06413	https://arxiv.org/pdf/2510.06413v1	2510.06413	10.48550/arXiv.2510.06413	0	0	false	null	arXiv.org	0.3266
bd93db712752904771ffc9457b5979fcaf5d6841220c1b7e758c14eea6542406	[ "arxiv", "semantic_scholar" ]	Attending on Multilevel Structure of Proteins enables Accurate Prediction of Cold-Start Drug-Target Interactions	Cold-start drug-target interaction (DTI) prediction focuses on interaction between novel drugs and proteins. Previous methods typically learn transferable interaction patterns between structures of drug and proteins to tackle it. However, insight from proteomics suggest that protein have multi-level structures and they...	[ "Ziying Zhang", "Yaqing Wang", "Yuxuan Sun", "Min Ye", "Quanming Yao" ]	[ "cs.LG", "cs.AI" ]	[ "Computer Science" ]	2025-10-05T00:00:00	https://arxiv.org/abs/2510.04126	https://arxiv.org/pdf/2510.04126v1	2510.04126	10.48550/arXiv.2510.04126	0	0	false	null	arXiv.org	0.3243
997e330af66c5a44c97b0a05f0f23917a51a2ac6948203afda211d53201b5e66	[ "arxiv", "semantic_scholar" ]	Model Merging to Maintain Language-Only Performance in Developmentally Plausible Multimodal Models	State-of-the-art vision-and-language models consist of many parameters and learn from enormous datasets, surpassing the amounts of linguistic data that children are exposed to as they acquire a language. This paper presents our approach to the multimodal track of the BabyLM challenge addressing this discrepancy. We dev...	[ "Ece Takmaz", "Lisa Bylinina", "Jakub Dotlacil" ]	[ "cs.CL", "cs.CV" ]	[ "Computer Science" ]	2025-10-02T00:00:00	https://arxiv.org/abs/2510.01845	https://arxiv.org/pdf/2510.01845v1	2510.01845	10.48550/arXiv.2510.01845	1	0	false	null	null	0.2042
8aac8fc547a50e6f6816a05a13d9322940af5e1daf5af4043c434a5e9b506b40	[ "arxiv", "semantic_scholar" ]	Type-Less yet Type-Aware Inductive Link Prediction with Pretrained Language Models	Inductive link prediction is emerging as a key paradigm for real-world knowledge graphs (KGs), where new entities frequently appear and models must generalize to them without retraining. Predicting links in a KG faces the challenge of guessing previously unseen entities by leveraging generalizable node features such as...	[ "Alessandro De Bellis", "Salvatore Bufi", "Giovanni Servedio", "Vito Walter Anelli", "Tommaso Di Noia", "Eugenio Di Sciascio" ]	[ "cs.CL", "cs.AI" ]	[ "Computer Science" ]	2025-09-30T00:00:00	https://arxiv.org/abs/2509.26224	https://arxiv.org/pdf/2509.26224v1	2509.26224	10.48550/arXiv.2509.26224	0	0	true	https://github.com/sisinflab/tyler	Conference on Empirical Methods in Natural Language Processing	0.4923
c68474d09149161b5097adb4aae09f1a6303eed62ff697027b51f0ce86025856	[ "arxiv", "semantic_scholar" ]	LAMP-PRo: Label-aware Attention for Multi-label Prediction of DNA- and RNA-binding Proteins using Protein Language Models	Identifying DNA- (DBPs) and RNA-binding proteins (RBPs) is crucial for the understanding of cell function, molecular interactions as well as regulatory functions. Owing to their high similarity, most of the existing approaches face challenges in differentiating between DBPs and RBPs leading to high cross-prediction err...	[ "Nimisha Ghosh", "Dheeran Sankaran", "Rahul Balakrishnan Adhi", "Sharath S", "Amrut Anand" ]	[ "q-bio.QM", "cs.AI", "cs.LG" ]	[ "Computer Science", "Biology" ]	2025-09-29T00:00:00	https://arxiv.org/abs/2509.24262	https://arxiv.org/pdf/2509.24262v2	2509.24262	10.48550/arXiv.2509.24262	0	0	true	https://github.com/NimishaGhosh/LAMP-PRo	arXiv.org	0.4905
60930b7601b2044af93819ee3ae0a1b7afd4c36d360c05d42914fcc934902aa8	[ "arxiv", "semantic_scholar" ]	Twin Peaks: Dual-Head Architecture for Structure-Free Prediction of Protein-Protein Binding Affinity and Mutation Effects	We present a novel dual-head deep learning architecture for protein-protein interaction modeling that enables simultaneous prediction of binding affinity ($ΔG$) and mutation-induced affinity changes ($ΔΔG$) using only protein sequence information. Our approach offers a significant advancement over existing methods by e...	[ "Supantha Dey", "Ratul Chowdhury" ]	[ "q-bio.QM" ]	[ "Biology" ]	2025-09-26T00:00:00	https://arxiv.org/abs/2509.22950	https://arxiv.org/pdf/2509.22950v1	2509.22950	null	1	0	false	null	null	0.1998
2bc1a481d3045b1f8b702408de30814fdca3d3fa1d01dbb18b974d3564addc64	[ "arxiv", "semantic_scholar" ]	Efficient Quantum Protein Structure Prediction with Problem-Agnostic Ansatzes	Accurately predicting protein structures from amino acid sequences remains a fundamental challenge in computational biology, with profound implications for understanding biological functions and enabling structure-based drug discovery. Quantum computing approaches based on coarse-grained lattice models combined with va...	[ "Hanna Linn", "Rui-Hao Li", "Alexander Holden", "Abdullah Ash Saki", "Frank DiFilippo", "Tomas Radivoyevitch", "Daniel Blankenberg", "Laura García-Álvarez", "Göran Johansson" ]	[ "quant-ph" ]	[ "Physics" ]	2025-09-22T00:00:00	https://arxiv.org/abs/2509.18263	https://arxiv.org/pdf/2509.18263v1	2509.18263	null	1	0	false	null	null	0.1969
2ea56703821928365e5f4eac2439d5e2942c319b75b2a594a2e7de9a80b27a4d	[ "arxiv", "semantic_scholar" ]	From Prediction to Simulation: AlphaFold 3 as a Differentiable Framework for Structural Biology	AlphaFold 3 represents a transformative advancement in computational biology, enhancing protein structure prediction through novel multi-scale transformer architectures, biologically informed cross-attention mechanisms, and geometry-aware optimization strategies. These innovations dramatically improve predictive accura...	[ "Alireza Abbaszadeh", "Armita Shahlaee" ]	[ "q-bio.BM", "cs.LG" ]	[ "Computer Science", "Biology" ]	2025-08-25T00:00:00	https://arxiv.org/abs/2508.18446	https://arxiv.org/pdf/2508.18446v1	2508.18446	10.48550/arXiv.2508.18446	1	0	false	null	arXiv.org	0.2773
e022f1918a4154619461042e4a679001df53ea10cd5e186a91a4c4c279d51ba0	[ "arxiv", "semantic_scholar" ]	Deep Learning Model for Amyloidogenicity Prediction using a Pre-trained Protein LLM	The prediction of amyloidogenicity in peptides and proteins remains a focal point of ongoing bioinformatics. The crucial step in this field is to apply advanced computational methodologies. Many recent approaches to predicting amyloidogenicity within proteins are highly based on evolutionary motifs and the individual p...	[ "Zohra Yagoub", "Hafida Bouziane" ]	[ "cs.LG", "cs.AI", "q-bio.QM" ]	[ "Computer Science", "Biology" ]	2025-08-18T00:00:00	https://arxiv.org/abs/2508.12575	https://arxiv.org/pdf/2508.12575v1	2508.12575	10.2991/978-94-6463-805-9_22	1	0	false	null	arXiv.org	0.2693
7dece0cfbb92ef34733e5a16ae907e3ec336efaff4fadf467989600c0c44684c	[ "arxiv", "semantic_scholar" ]	Driving Accurate Allergen Prediction with Protein Language Models and Generalization-Focused Evaluation	Allergens, typically proteins capable of triggering adverse immune responses, represent a significant public health challenge. To accurately identify allergen proteins, we introduce Applm (Allergen Prediction with Protein Language Models), a computational framework that leverages the 100-billion parameter xTrimoPGLM pr...	[ "Brian Shing-Hei Wong", "Joshua Mincheol Kim", "Sin-Hang Fung", "Qing Xiong", "Kelvin Fu-Kiu Ao", "Junkang Wei", "Ran Wang", "Dan Michelle Wang", "Jingying Zhou", "Bo Feng", "Alfred Sze-Lok Cheng", "Kevin Y. Yip", "Stephen Kwok-Wing Tsui", "Qin Cao" ]	[ "cs.LG", "q-bio.QM" ]	[ "Computer Science", "Biology" ]	2025-08-14T00:00:00	https://arxiv.org/abs/2508.10541	https://arxiv.org/pdf/2508.10541v1	2508.10541	10.48550/arXiv.2508.10541	0	0	true	null	arXiv.org	0.4091
290803a475eeed7c71953073b94d51203d2339516da2e71e81a90fe36e9abf07	[ "arxiv", "semantic_scholar" ]	Energy-Based Models for Predicting Mutational Effects on Proteins	Predicting changes in binding free energy ($ΔΔG$) is a vital task in protein engineering and protein-protein interaction (PPI) engineering for drug discovery. Previous works have observed a high correlation between $ΔΔG$ and entropy, using probabilities of biologically important objects such as side chain angles and re...	[ "Patrick Soga", "Zhenyu Lei", "Yinhan He", "Camille Bilodeau", "Jundong Li" ]	[ "cs.LG" ]	[ "Computer Science" ]	2025-08-14T00:00:00	https://arxiv.org/abs/2508.10629	https://arxiv.org/pdf/2508.10629v1	2508.10629	10.1145/3711896.3736931	0	0	false	null	Knowledge Discovery and Data Mining	0.2647
e4b968583cd5dac10956364486238675088de6b7ce1db2a8194760db6512cef2	[ "arxiv", "semantic_scholar" ]	Not Yet AlphaFold for the Mind: Evaluating Centaur as a Synthetic Participant	Simulators have revolutionized scientific practice across the natural sciences. By generating data that reliably approximate real-world phenomena, they enable scientists to accelerate hypothesis testing and optimize experimental designs. This is perhaps best illustrated by AlphaFold, a Nobel-prize winning simulator in ...	[ "Sabrina Namazova", "Alessandra Brondetta", "Younes Strittmatter", "Matthew Nassar", "Sebastian Musslick" ]	[ "cs.LG", "cs.AI" ]	[ "Computer Science" ]	2025-08-11T00:00:00	https://arxiv.org/abs/2508.07887	https://arxiv.org/pdf/2508.07887v1	2508.07887	10.48550/arXiv.2508.07887	2	1	false	null	arXiv.org	0.2612
6bcbf04dbaa3032ba85de972916bafeace4d1a2244148b2740390bf53e49725b	[ "arxiv", "semantic_scholar" ]	Scaling and Data Saturation in Protein Language Models	Data in biology is redundant, noisy, and sparse. How does the type and scale of available data impact model performance? In this work, we specifically investigate how protein language models (pLMs) scale with increasing pretraining data. We investigate this relationship by measuring the performance of protein function ...	[ "Aviv Spinner", "Erika DeBenedictis", "Corey M. Hudson" ]	[ "q-bio.QM" ]	[ "Biology" ]	2025-07-29T00:00:00	https://arxiv.org/abs/2507.22210	https://arxiv.org/pdf/2507.22210v1	2507.22210	null	4	0	true	https://github.com/Align-to-Innovate/data-saturation-and-scaling	null	0.2911
a4bc9146f7a0426d9bce76a3696c3b193afce96a171c0a45f8d21791cbc16aeb	[ "arxiv", "semantic_scholar" ]	A novel language model for predicting serious adverse event results in clinical trials from their prospective registrations	Objectives: With accurate estimates of expected safety results, clinical trials could be better designed and monitored. We evaluated methods for predicting serious adverse event (SAE) results in clinical trials using information only from their registrations prior to the trial. Material and Methods: We analyzed 22,107 ...	[ "Qixuan Hu", "Xumou Zhang", "Jinman Kim", "Florence Bourgeois", "Adam G. Dunn" ]	[ "cs.CL", "cs.AI" ]	[ "Computer Science" ]	2025-07-21T00:00:00	https://arxiv.org/abs/2507.22919	https://arxiv.org/pdf/2507.22919v2	2507.22919	10.48550/arXiv.2507.22919	1	0	false	null	arXiv.org	0.2372
dff613cd5afdfb5b33cc648ae472a565c46ecde7d7a36e62bcb42e8b00757bde	[ "arxiv", "semantic_scholar" ]	Continued domain-specific pre-training of protein language models for pMHC-I binding prediction	Predicting peptide--major histocompatibility complex I (pMHC-I) binding affinity remains challenging due to extreme allelic diversity ($\sim$30,000 HLA alleles), severe data scarcity for most alleles, and noisy experimental measurements. Current methods particularly struggle with underrepresented alleles and quantitati...	[ "Sergio E. Mares", "Ariel Espinoza Weinberger", "Nilah M. Ioannidis" ]	[ "q-bio.QM" ]	[ "Computer Science", "Biology" ]	2025-07-16T00:00:00	https://arxiv.org/abs/2507.13077	https://arxiv.org/pdf/2507.13077v1	2507.13077	null	2	0	false	null	null	0.1473
615a089e8e1750031a63923f8b0581717f0d58295045bf2313c3d7b7766f3413	[ "arxiv", "semantic_scholar" ]	Conformation-Aware Structure Prediction of Antigen-Recognizing Immune Proteins	We introduce Ibex, a pan-immunoglobulin structure prediction model that achieves state-of-the-art accuracy in modeling the variable domains of antibodies, nanobodies, and T-cell receptors. Unlike previous approaches, Ibex explicitly distinguishes between bound and unbound protein conformations by training on labeled ap...	[ "Frédéric A. Dreyer", "Jan Ludwiczak", "Karolis Martinkus", "Brennan Abanades", "Robert G. Alberstein", "Pan Kessel", "Pranav Rao", "Jae Hyeon Lee", "Richard Bonneau", "Andrew M. Watkins", "Franziska Seeger" ]	[ "q-bio.BM", "cs.LG" ]	[ "Medicine", "Biology", "Computer Science" ]	2025-07-11T00:00:00	https://arxiv.org/abs/2507.09054	https://arxiv.org/pdf/2507.09054v1	2507.09054	10.1080/19420862.2025.2602217	6	1	true	https://github.com/prescient-design/ibex	mAbs	0.3489
85c60dba527fa4258ea2bfbeafd52499e3f6b46e5c68f5f2cb1560f8639e1815	[ "arxiv", "semantic_scholar" ]	PRING: Rethinking Protein-Protein Interaction Prediction from Pairs to Graphs	Deep learning-based computational methods have achieved promising results in predicting protein-protein interactions (PPIs). However, existing benchmarks predominantly focus on isolated pairwise evaluations, overlooking a model's capability to reconstruct biologically meaningful PPI networks, which is crucial for biolo...	[ "Xinzhe Zheng", "Hao Du", "Fanding Xu", "Jinzhe Li", "Zhiyuan Liu", "Wenkang Wang", "Tao Chen", "Wanli Ouyang", "Stan Z. Li", "Yan Lu", "Nanqing Dong", "Yang Zhang" ]	[ "cs.LG", "cs.AI", "q-bio.BM", "q-bio.MN" ]	[ "Computer Science", "Biology" ]	2025-07-07T00:00:00	https://arxiv.org/abs/2507.05101	https://arxiv.org/pdf/2507.05101v2	2507.05101	10.48550/arXiv.2507.05101	3	0	true	https://github.com/SophieSarceau/PRING	arXiv.org	0.3418
35416db0adec377947ad37adff664fc4425409a9b9b9c59febe15c247c6a8dee	[ "arxiv", "semantic_scholar" ]	ElliottAgents: A Natural Language-Driven Multi-Agent System for Stock Market Analysis and Prediction	This paper presents ElliottAgents, a multi-agent system leveraging natural language processing (NLP) and large language models (LLMs) to analyze complex stock market data. The system combines AI-driven analysis with the Elliott Wave Principle to generate human-comprehensible predictions and explanations. A key feature ...	[ "Jarosław A. Chudziak", "Michał Wawer" ]	[ "cs.CE" ]	[ "Computer Science" ]	2025-07-04T00:00:00	https://arxiv.org/abs/2507.03435	https://arxiv.org/pdf/2507.03435v1	2507.03435	10.48550/arXiv.2507.03435	13	0	false	null	Pacific Asia Conference on Language, Information and Computation	0.2865
3f560b9c9b5d5696bdd8ef45f40c21973f45bc05886dd0a5b1b53c387d3b358d	[ "arxiv", "semantic_scholar" ]	DeSTA2.5-Audio: Toward General-Purpose Large Audio Language Model with Self-Generated Cross-Modal Alignment	We introduce DeSTA2.5-Audio, a general-purpose Large Audio Language Model (LALM) designed for robust auditory perception and instruction-following. Recent LALMs augment Large Language Models (LLMs) with auditory capabilities by training on large-scale audio-instruction datasets. However, existing LALMs have often suffe...	[ "Ke-Han Lu", "Zhehuai Chen", "Szu-Wei Fu", "Chao-Han Huck Yang", "Sung-Feng Huang", "Chih-Kai Yang", "Chee-En Yu", "Chun-Wei Chen", "Wei-Chih Chen", "Chien-yu Huang", "Yi-Cheng Lin", "Yu-Xiang Lin", "Chi-An Fu", "Chun-Yi Kuan", "Wenze Ren", "Xuanjun Chen", "Wei-Ping Huang", "En-Pei...	[ "eess.AS", "cs.CL", "cs.SD" ]	[ "Computer Science", "Engineering" ]	2025-07-03T00:00:00	https://arxiv.org/abs/2507.02768	https://arxiv.org/pdf/2507.02768v2	2507.02768	10.1109/TASLPRO.2026.3675792	50	4	true	https://github.com/kehanlu/DeSTA2.5-Audio	IEEE Transactions on Audio, Speech, and Language Processing	0.4269
837045204085ddf966e85102120ce95abc72efd910dd2e71936a2eba0e8aa5f9	[ "arxiv", "semantic_scholar" ]	Steering Protein Language Models	Protein Language Models (PLMs), pre-trained on extensive evolutionary data from natural proteins, have emerged as indispensable tools for protein design. While powerful, PLMs often struggle to produce proteins with precisely specified functionalities or properties due to inherent challenges in controlling their outputs...	[ "Long-Kai Huang", "Rongyi Zhu", "Bing He", "Jianhua Yao" ]	[ "q-bio.BM", "cs.LG" ]	[ "Computer Science", "Biology" ]	2025-07-01T00:00:00	https://arxiv.org/abs/2509.07983	https://arxiv.org/pdf/2509.07983v2	2509.07983	10.48550/arXiv.2509.07983	5	2	false	null	International Conference on Machine Learning	0.2386
992599a4c51365c71a416b8dffabf0fb43df0e7ad045cc9da447222ebe05dfa3	[ "arxiv", "semantic_scholar" ]	Self-Organizing Language	We introduce a novel paradigm of emergent local memory. It is a continuous-learning completely-parallel content-addressable memory encoding global order. It demonstrates how local constraints on uncoordinated learning can produce topologically protected memories realizing emergent symbolic order. It is therefore a neur...	[ "P. Myles Eugenio", "Anthony Beavers" ]	[ "cs.CL", "cs.AI", "cs.LG", "q-bio.NC" ]	[ "Computer Science", "Biology" ]	2025-06-29T00:00:00	https://arxiv.org/abs/2506.23293	https://arxiv.org/pdf/2506.23293v2	2506.23293	null	0	0	false	null	null	0.1349
bf2b044f029bcd29e9063502a72385ef42bc5cf61b03375789252548e1837e8d	[ "arxiv", "semantic_scholar" ]	Toward the Explainability of Protein Language Models	Protein language models (pLMs) excel in a variety of tasks that range from structure prediction to the design of functional enzymes. However, these models operate as black boxes, and their underlying working principles remain unclear. Here, we survey emerging applications of explainable artificial intelligence (XAI) to...	[ "Andrea Hunklinger", "Noelia Ferruz" ]	[ "q-bio.BM" ]	[ "Biology" ]	2025-06-24T00:00:00	https://arxiv.org/abs/2506.19532	https://arxiv.org/pdf/2506.19532v4	2506.19532	null	4	0	true	null	null	0.2437
1c0af367aabedb2a5100ab0033b2b6aeb96bc90576c2e3bece2c20ab078b17db	[ "arxiv", "semantic_scholar" ]	From Data to Knowledge: Evaluating How Efficiently Language Models Learn Facts	Sample efficiency is a crucial property of language models with practical implications for training efficiency. In real-world text, information follows a long-tailed distribution. Yet, we expect models to learn and recall frequent and infrequent facts. Sample-efficient models are better equipped to handle this challeng...	[ "Daniel Christoph", "Max Ploner", "Patrick Haller", "Alan Akbik" ]	[ "cs.CL", "cs.LG" ]	[ "Computer Science" ]	2025-06-20T00:00:00	https://arxiv.org/abs/2506.16912	https://arxiv.org/pdf/2506.16912v1	2506.16912	10.48550/arXiv.2506.16912	1	0	false	null	null	0.1283
01e833fed03679295d74a2882911ff3d0bf08674ae9161c795494815377140bf	[ "arxiv", "semantic_scholar" ]	PL-Guard: Benchmarking Language Model Safety for Polish	Despite increasing efforts to ensure the safety of large language models (LLMs), most existing safety assessments and moderation tools remain heavily biased toward English and other high-resource languages, leaving majority of global languages underexamined. To address this gap, we introduce a manually annotated benchm...	[ "Aleksandra Krasnodębska", "Karolina Seweryn", "Szymon Łukasik", "Wojciech Kusa" ]	[ "cs.CL" ]	[ "Computer Science" ]	2025-06-19T00:00:00	https://arxiv.org/abs/2506.16322	https://arxiv.org/pdf/2506.16322v1	2506.16322	10.48550/arXiv.2506.16322	2	0	false	null	null	0.1276
7b8ee46486dc765aaba8be750a85138fb4c60b5c22eff352a3cd190d5dc50a04	[ "arxiv", "semantic_scholar" ]	Can structural correspondences ground real world representational content in Large Language Models?	Large Language Models (LLMs) such as GPT-4 produce compelling responses to a wide range of prompts. But their representational capacities are uncertain. Many LLMs have no direct contact with extra-linguistic reality: their inputs, outputs and training data consist solely of text, raising the questions (1) can LLMs repr...	[ "Iwan Williams" ]	[ "cs.CL", "cs.AI" ]	[ "Computer Science" ]	2025-06-19T00:00:00	https://arxiv.org/abs/2506.16370	https://arxiv.org/pdf/2506.16370v1	2506.16370	10.1111/mila.70018	6	1	false	null	Mind & Language (2026)	0.2113
75a55fff656c471766778a9e496b285ba5ba3581e9e1cab37cd1ff9bb027f2f0	[ "arxiv", "semantic_scholar" ]	DISPROTBENCH: Uncovering the Functional Limits of Protein Structure Prediction Models in Intrinsically Disordered Regions	Intrinsically disordered regions (IDRs) play central roles in cellular function, yet remain poorly evaluated by existing protein structure prediction benchmarks. Current evaluations largely focus on well-folded domains, overlooking three fundamental challenges in realistic biological settings: the structural complexity...	[ "Xinyue Zeng", "Tuo Wang", "Adithya Kulkarni", "Alexander Lu", "Alexandra Ni", "Phoebe Xing", "Junhan Zhao", "Siwei Chen", "Dawei Zhou" ]	[ "q-bio.BM", "cs.LG" ]	[ "Biology", "Computer Science" ]	2025-06-18T00:00:00	https://arxiv.org/abs/2507.02883	https://arxiv.org/pdf/2507.02883v2	2507.02883	null	1	0	true	https://github.com/Susan571/DisProtBench	null	0.2356
aedb9150be631477545f2e7296c2361e0785321ca04414fac3e3b31ad0a3f244	[ "arxiv", "semantic_scholar" ]	InstructPro: Natural Language Guided Ligand-Binding Protein Design	The de novo design of ligand-binding proteins with tailored functions is essential for advancing biotechnology and molecular medicine, yet existing AI approaches are limited by scarce protein-ligand complex data. To circumvent this data bottleneck, we leverage the abundant natural language descriptions characterizing p...	[ "Zhenqiao Song", "Ramith Hettiarachchi", "Chuan Li", "Jianwen Xie", "Lei Li" ]	[ "cs.LG", "cs.CE", "cs.CL" ]	[ "Computer Science" ]	2025-06-11T00:00:00	https://arxiv.org/abs/2506.09332	https://arxiv.org/pdf/2506.09332v3	2506.09332	null	4	1	false	null	null	0.1747
24ec0895e13954f36f7dc4f6657cc93c8536513335b13c531818dbc89383a157	[ "arxiv", "semantic_scholar" ]	BioLangFusion: Multimodal Fusion of DNA, mRNA, and Protein Language Models	We present BioLangFusion, a simple approach for integrating pre-trained DNA, mRNA, and protein language models into unified molecular representations. Motivated by the central dogma of molecular biology (information flow from gene to transcript to protein), we align per-modality embeddings at the biologically meaningfu...	[ "Amina Mollaysa", "Artem Moskale", "Pushpak Pati", "Tommaso Mansi", "Mangal Prakash", "Rui Liao" ]	[ "cs.LG" ]	[ "Computer Science" ]	2025-06-10T00:00:00	https://arxiv.org/abs/2506.08936	https://arxiv.org/pdf/2506.08936v1	2506.08936	10.48550/arXiv.2506.08936	4	1	false	null	arXiv.org	0.1902
9421819fef23fac9c08cb493a8b23e745d222e0dbeb894e66f36a485fdc7eaf2	[ "arxiv", "semantic_scholar" ]	AlphaFold Database Debiasing for Robust Inverse Folding	The AlphaFold Protein Structure Database (AFDB) offers unparalleled structural coverage at near-experimental accuracy, positioning it as a valuable resource for data-driven protein design. However, its direct use in training deep models that are sensitive to fine-grained atomic geometry, such as inverse folding, expose...	[ "Cheng Tan", "Zhenxiao Cao", "Zhangyang Gao", "Siyuan Li", "Yufei Huang", "Stan Z. Li" ]	[ "cs.LG", "q-bio.BM" ]	[ "Computer Science", "Biology" ]	2025-06-10T00:00:00	https://arxiv.org/abs/2506.08365	https://arxiv.org/pdf/2506.08365v1	2506.08365	10.48550/arXiv.2506.08365	2	0	false	null	arXiv.org	0.1902
b6f9da9b8836d99fefd98883d1732d2e02cc5ef600038430c9cd640a94a337c8	[ "arxiv", "semantic_scholar" ]	Into the Unknown: From Structure to Disorder in Protein Function Prediction	Intrinsically disordered regions (IDRs) account for one-third of the human proteome and play essential biological roles. However, predicting the functions of IDRs remains a major challenge due to their lack of stable structures, rapid sequence evolution, and context-dependent behavior. Many predictors of protein functi...	[ "Đesika Kolarić", "Chi Fung Willis Chow", "Rita Zi Zhu", "Agnes Toth-Petroczy", "T. Reid Alderson", "Iva Pritišanac" ]	[ "q-bio.BM" ]	[ "Biology" ]	2025-06-06T00:00:00	https://arxiv.org/abs/2506.06004	https://arxiv.org/pdf/2506.06004v2	2506.06004	null	0	0	false	null	null	0.1181
177a284d804e1f6fd8193aa1754044be2fe8d467b2ef2b396b9ce295df9570d1	[ "arxiv", "semantic_scholar" ]	Multiscale guidance of protein structure prediction with heterogeneous cryo-EM data	Protein structure prediction models are now capable of generating accurate 3D structural hypotheses from sequence alone. However, they routinely fail to capture the conformational diversity of dynamic biomolecular complexes, often requiring heuristic MSA subsampling approaches for generating alternative states. In para...	[ "Rishwanth Raghu", "Axel Levy", "Gordon Wetzstein", "Ellen D. Zhong" ]	[ "cs.LG", "q-bio.BM" ]	[ "Computer Science", "Biology" ]	2025-06-04T00:00:00	https://arxiv.org/abs/2506.04490	https://arxiv.org/pdf/2506.04490v2	2506.04490	null	10	1	true	https://github.com/ml-struct-bio/cryoboltz	null	0.2603
541b8d667afef65e6b76a169bafb9914c98341ee055cde8e5622457859c6a733	[ "arxiv", "semantic_scholar" ]	Trajectory Prediction Meets Large Language Models: A Survey	Recent advances in large language models (LLMs) have sparked growing interest in integrating language-driven techniques into trajectory prediction. By leveraging their semantic and reasoning capabilities, LLMs are reshaping how autonomous systems perceive, model, and predict trajectories. This survey provides a compreh...	[ "Yi Xu", "Ruining Yang", "Yitian Zhang", "Jianglin Lu", "Mingyuan Zhang", "Yizhou Wang", "Lili Su", "Yun Fu" ]	[ "cs.CL", "cs.CV" ]	[ "Computer Science" ]	2025-06-03T00:00:00	https://arxiv.org/abs/2506.03408	https://arxiv.org/pdf/2506.03408v2	2506.03408	10.48550/arXiv.2506.03408	15	0	true	https://github.com/colorfulfuture/Awesome-Trajectory-Motion-Prediction-Papers	arXiv.org	0.301
d2584d7ddd76ec6b7043b482c64f9367d8ea70d3ba478faa9cb3446c0be966bb	[ "arxiv", "semantic_scholar" ]	Protein Language Model Zero-Shot Fitness Predictions are Improved by Inference-only Dropout	Protein Language Models (PLMs) such as ESM2 have been shown to be capable of zero-shot prediction of critical scalar properties of proteins (fitness). In this work, we show that injecting a dropout layer at inference time between a PLM's featurizer/embedding layer and its transformer, and averaging its output akin to M...	[ "Aditya Ravuri", "Neil D. Lawrence" ]	[ "cs.LG" ]	[ "Computer Science" ]	2025-05-31T00:00:00	https://arxiv.org/abs/2506.14793	https://arxiv.org/pdf/2506.14793v1	2506.14793	10.48550/arXiv.2506.14793	0	0	false	null	arXiv.org	0.1788
2e6bfe02b9c252b7da6e6a04262ddef85921fd4a3f74ec224c7399df50c2b55f	[ "arxiv", "semantic_scholar" ]	Aligning Proteins and Language: A Foundation Model for Protein Retrieval	This paper aims to retrieve proteins with similar structures and semantics from large-scale protein dataset, facilitating the functional interpretation of protein structures derived by structural determination methods like cryo-Electron Microscopy (cryo-EM). Motivated by the recent progress of vision-language models (V...	[ "Qifeng Wu", "Zhengzhe Liu", "Han Zhu", "Yizhou Zhao", "Daisuke Kihara", "Min Xu" ]	[ "q-bio.BM", "cs.AI", "cs.CE", "cs.CV", "cs.LG" ]	[ "Computer Science", "Biology" ]	2025-05-27T00:00:00	https://arxiv.org/abs/2506.08023	https://arxiv.org/pdf/2506.08023v1	2506.08023	10.48550/arXiv.2506.08023	1	0	false	null	arXiv.org	0.1742
add8b8512e20d5ecc2e9f9bbbc2767b3acb548b225aad58f3d9fa66911e73d79	[ "arxiv", "semantic_scholar" ]	AlphaFold's Bayesian Roots in Probability Kinematics	The seminal breakthrough of AlphaFold in protein structure prediction relied on a learned potential energy function parameterized by deep models, in contrast to its successors AlphaFold2 and AlphaFold3, which lack an explicit probabilistic interpretation. While AlphaFold's potential was originally justified by heuristi...	[ "Thomas Hamelryck", "Kanti V. Mardia" ]	[ "cs.LG" ]	[ "Computer Science" ]	2025-05-26T00:00:00	https://arxiv.org/abs/2505.19763	https://arxiv.org/pdf/2505.19763v3	2505.19763	null	2	0	false	null	null	0.1193
f90c949ade15ba2f4c9e0bd5e1fdeb023da3cf4dd97ba383fdfa8e6c1a128b9d	[ "arxiv", "semantic_scholar" ]	From Alignment to Advancement: Bootstrapping Audio-Language Alignment with Synthetic Data	Audio-aware large language models (ALLMs) have recently made great strides in understanding and processing audio inputs. These models are typically adapted from text-based large language models (LLMs) through additional training on audio-related tasks. This adaptation process presents two major limitations. First, ALLM...	[ "Chun-Yi Kuan", "Hung-yi Lee" ]	[ "eess.AS", "cs.AI", "cs.CL", "cs.LG", "cs.SD" ]	[ "Computer Science", "Engineering" ]	2025-05-26T00:00:00	https://arxiv.org/abs/2505.20166	https://arxiv.org/pdf/2505.20166v3	2505.20166	10.1109/TASLPRO.2025.3626233	4	0	false	null	IEEE Transactions on Audio, Speech, and Language Processing	0.1747
fc09c3be373f4e44bae269a7a4096faf762b965be76abf58376cdb758f052856	[ "arxiv", "semantic_scholar" ]	Prot2Token: A Unified Framework for Protein Modeling via Next-Token Prediction	The diverse nature of protein prediction tasks has traditionally necessitated specialized models, hindering the development of broadly applicable and computationally efficient Protein Language Models (PLMs). In this work, we introduce Prot2Token, a unified framework that overcomes these challenges by converting a wide ...	[ "Mahdi Pourmirzaei", "Farzaneh Esmaili", "Salhuldin Alqarghuli", "Mohammadreza Pourmirzaei", "Ye Han", "Kai Chen", "Mohsen Rezaei", "Duolin Wang", "Dong Xu" ]	[ "cs.LG", "q-bio.QM" ]	[ "Computer Science", "Biology" ]	2025-05-26T00:00:00	https://arxiv.org/abs/2505.20589	https://arxiv.org/pdf/2505.20589v2	2505.20589	10.48550/arXiv.2505.20589	4	0	true	https://github.com/mahdip72/prot2token	arXiv.org	0.2674
c3a06ff816af0f5fd2eb043768765ca7388db572603b0eb914ce8b4ddd548e6d	[ "arxiv", "semantic_scholar" ]	Rethinking Text-based Protein Understanding: Retrieval or LLM?	In recent years, protein-text models have gained significant attention for their potential in protein generation and understanding. Current approaches focus on integrating protein-related knowledge into large language models through continued pretraining and multi-modal alignment, enabling simultaneous comprehension of...	[ "Juntong Wu", "Zijing Liu", "He Cao", "Hao Li", "Bin Feng", "Zishan Shu", "Ke Yu", "Li Yuan", "Yu Li" ]	[ "cs.CL", "cs.AI" ]	[ "Computer Science" ]	2025-05-26T00:00:00	https://arxiv.org/abs/2505.20354	https://arxiv.org/pdf/2505.20354v4	2505.20354	10.48550/arXiv.2505.20354	7	0	true	https://github.com/IDEA-XL/RAPM	Conference on Empirical Methods in Natural Language Processing	0.2674

End of preview. Expand in Data Studio

Protein Language Models Papers — FineSet

A research-paper dataset on Protein Language Models Papers, assembled, deduplicated, and quality-scored by FineSet from arXiv and Semantic Scholar.

📸 This is a dated snapshot — generated 2026-06-19. It is not auto-updated. Research on Protein Language Models Papers moves fast — new papers land on arXiv every week. Want this same dataset refreshed daily, on a topic you choose? See the bottom. ↓

Why this dataset

Quality-scored: quality_score float (0–1), blends citations with recency + code/venue signals — filter out the noise
Papers with code: 100 flagged via has_code — find reproducible work fast
Deduplicated: arXiv + Semantic Scholar cross-referenced, duplicate records merged
Clean JSONL: 408 records, one per line, normalized fields — no encoding garbage

Dataset details

Records: 408
Date range: 2019–2026
Snapshot date: 2026-06-19 (frozen — see note above)
Sources: arXiv, Semantic Scholar (cross-referenced, duplicates merged)
arXiv categories: cs.LG, q-bio.BM, q-bio.QM
Quality scoring: citations + recency + code/venue blend, 0–1 (p50=0.293, p90=0.511)
Format: JSONL, one record per line

Fields

Field	Type	Description
id	string	Deterministic SHA256 record id
sources	list	Which sources contributed (`arxiv`, `semantic_scholar`)
title	string	Paper title
abstract	string	Full abstract
authors	list	Author names
categories	list	arXiv category codes
fields_of_study	list	Semantic Scholar field tags
published_date	string	ISO 8601 date
url	string	arXiv abstract URL
pdf_url	string\|null	Open-access PDF if available
arxiv_id	string\|null	arXiv identifier
doi	string\|null	DOI if available
citation_count	int	Citation count (Semantic Scholar)
influential_citation_count	int	Influential citations (Semantic Scholar)
has_code	bool	Code repo detected in the arXiv comment
code_url	string\|null	GitHub URL if detected
venue	string\|null	Publication venue
quality_score	float	0–1, blended (citations + recency + code/venue)

Quality score methodology

quality_score = max(impact, freshness), clamped to [0, 1], where:

impact = max( log10(citations+1)/4 , log10(influential_citations+1)/2 ) — realized impact (0.5 at 100 citations, ~0.75 at 1,000, 1.0 at 10,000+).
freshness = recency × (0.35 + 0.30·has_code + 0.20·has_venue) — a baseline for recent papers (so a strong paper published this week isn't scored 0 just for lacking citations), where recency is 1.0 for papers ≤60 days old and decays linearly to 0 by ~18 months.

Old highly-cited papers score on impact; brand-new papers score on freshness; old uncited papers score ~0. Useful for filtering training data by quality, not just age.

👉 Want this on YOUR topic, updated daily?

This snapshot is frozen at 2026-06-19. The live FineSet pipeline keeps a dataset like this refreshed every day on whatever topic you describe — new papers in, dedup and quality scoring automatic, export as JSONL/Parquet or push straight to the Hub.

Tell me the topic you'd want and I'll run the pipeline on it — open a discussion on this dataset, it's free and it's how I decide what to build next.

→ fineset.io — describe what you want to train on, get a dataset. Early-access waitlist open (referral skip available).

Downloads last month: 29