arXiv Machine Learning
Jun 25
Two Stages of Folding: Convergent Mechanisms in AI Protein Folding Trunks
arXiv:2602. 06020v3 Announce Type: replace Abstract: How do protein structure prediction models fold proteins?
Protein Structure Prediction: From Evolutionary Constraints to Generative Modeling
arXiv:2608. 16094v1 Announce Type: new Abstract: Accurate protein structure prediction is fundamental to structural biology because protein structure underlies molecular function and provides a basis for mechanistic interpretation.
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arXiv Machine Learning
Jul 20
Neural spectroscopy of AlphaFold2 reveals encoded protein conformational landscapes
arXiv:2607. 16087v1 Announce Type: new Abstract: AlphaFold2's 93 million parameters, shaped by the evolutionary record of protein structure encoded in the Protein Data Bank and in sequence alignments, are conventionally treated only as machinery for converting sequence to structure.
arXiv AI
Jun 12
HD-Prot: A Protein Language Model for Joint Sequence-Structure Modeling with Continuous Structure Tokens
arXiv:2512. 15133v3 Announce Type: replace-cross Abstract: Proteins inherently possess a consistent sequence-structure duality.
arXiv Machine Learning
Jul 15
PRIME: Protein Representation via Physics-Informed Multiscale Equivariant Hierarchies
arXiv:2605. 01625v3 Announce Type: replace Abstract: Proteins are inherently multiscale physical systems whose functional properties emerge from coordinated structural organization across multiple spatial resolutions, ranging from atomic interactions to global fold topology.
arXiv Machine Learning
Jul 14
Vilya-1: An all-atom foundation model for macrocycle structure prediction and design
arXiv:2607. 09998v1 Announce Type: new Abstract: Macrocyclic peptides are an increasingly important therapeutic modality, but existing computational methods for modeling their structures and properties are limited in scope and do not generalize well across the synthetically accessible chemical space.
arXiv AI
Jul 7
Folding, Reasoning, and Scaling with Open-source Drug Discovery Engine
arXiv:2607. 03787v1 Announce Type: new Abstract: Accurately modeling biomolecular interactions is a central bottleneck in biology and therapeutic discovery.
arXiv Machine Learning
Jun 26
Training-Free Generation of Protein Sequences from Small Family Alignments via Stochastic Attention
arXiv:2603. 14717v2 Announce Type: replace Abstract: Generating novel protein sequences that respect a family's statistical constraints typically requires training deep generative models on thousands to millions of examples.
arXiv Machine Learning
Jul 22
DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models
arXiv:2607. 19237v1 Announce Type: new Abstract: Designing small molecule ligands that bind with high affinity to specific protein pockets is a fundamental goal in drug discovery, as small molecules constitute a major fraction of approved therapeutics.
arXiv AI
Jun 9
AMix-1: A Pathway to Test-Time Scalable Protein Foundation Model
arXiv:2507. 08920v4 Announce Type: replace-cross Abstract: We introduce AMix-1, a powerful protein foundation model built on Bayesian Flow Networks and empowered by a systematic training methodology, encompassing pretraining scaling laws, emergent capability analysis, in-context learning mechanism, and test-time scaling algorithm.
arXiv Machine Learning
Jun 30
Inference-time optimization for experiment-grounded protein ensemble generation
arXiv:2602. 24007v3 Announce Type: replace-cross Abstract: Protein function relies on dynamic conformational ensembles, yet current generative models like AlphaFold3 often fail to produce ensembles that match experimental data.
arXiv AI
Aug 5
Learning Molecular Representations from Cellular Phenotypes with Structure Preservation
arXiv:2608. 02688v1 Announce Type: cross Abstract: Phenotypic drug discovery enables the discovery of functional relationships between molecular structures and cellular responses.
arXiv AI
Jul 28
LC-SEPLM: long-range contact-supervised adaptation for sequence-only protein representation learning
arXiv:2607. 22777v1 Announce Type: cross Abstract: Protein language models learn transferable sequence representations.