arXiv Machine Learning June 5, 2026 By Hanqun Cao, Zachary Quinn, Aastha Pal, Sumi Kimura, Jingjie Zhang, Pheng Ann Heng, Pranam Chatterjee

AlloGen: Conformation-Selective Binder Generation with Differential State Scoring

Read the original on arXiv Machine Learning →

arXiv:2606. 05474v1 Announce Type: cross Abstract: Protein binder design has largely optimized for affinity alone, leaving conformational selectivity unaddressed: for allosteric targets such as kinases, nuclear receptors, and GPCRs, a binder that engages both active and inactive states provides no functional specificity regardless of how tightly it binds.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

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Curvature-Informed Potential Energy Surface for Protein-Ligand Binding Affinity Prediction

arXiv:2606. 14217v1 Announce Type: new Abstract: Accurate prediction of protein-ligand binding affinity is essential for structure-based drug discovery.

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benchmarks safety

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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.

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arXiv Machine Learning

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PRIME: Protein Representation via Physics-Informed Multiscale Equivariant Hierarchies

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TriGlue: a Biology-Inspired Generative Model for Generating Molecular Glue-Induced Ternary Complex

arXiv:2607. 22143v1 Announce Type: new Abstract: Molecular glue degraders have emerged as a promising strategy for targeted protein degradation by inducing ternary complex formation between an E3 ubiquitin ligase and a target protein.

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arXiv Machine Learning

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RAVEN: Frozen Random Graph Reservoirs with Physics-Informed Interaction Fingerprints for Protein-Ligand Binding Affinity Prediction

arXiv:2608. 09099v1 Announce Type: new Abstract: Quantitative estimation of protein-ligand binding affinity from three-dimensional complex structures is a fundamental task in structure-based computational chemistry and molecular modeling.

By Qingyang Zou, Jiaye Huang, Hangbo Xie, Jiayue Yin, Youyi Song, Jinfeng Liu

arXiv Machine Learning

Jul 24

Graph Learning on Ensembles of Cyclic Peptides: An Investigation of Molecular Ensemble Modeling

arXiv:2607. 21561v1 Announce Type: new Abstract: Molecular property prediction from structure often uses a single representative conformation, even though many molecules exist as conformational ensembles in solution.

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Related stories

Curvature-Informed Potential Energy Surface for Protein-Ligand Binding Affinity Prediction

Neural spectroscopy of AlphaFold2 reveals encoded protein conformational landscapes

PRIME: Protein Representation via Physics-Informed Multiscale Equivariant Hierarchies

TriGlue: a Biology-Inspired Generative Model for Generating Molecular Glue-Induced Ternary Complex

RAVEN: Frozen Random Graph Reservoirs with Physics-Informed Interaction Fingerprints for Protein-Ligand Binding Affinity Prediction

Graph Learning on Ensembles of Cyclic Peptides: An Investigation of Molecular Ensemble Modeling