arXiv:2601.04506v1 Announce Type: cross Abstract: Therapeutic peptides show promise in targeting previously undruggable binding sites, with recent advancements in deep generative models enabling full-atom peptide co-design for specific protein receptors. However, the critical role of molecular surfaces in protein-protein interactions (PPIs) has been underexplored. To bridge this gap, we propose an omni-design peptides generation paradigm, called SurfFlow, a novel surface-based generative algorithm that enables comprehensive co-design of sequence, structure, and surface for peptides. SurfFlow employs a multi-modality conditional flow matching (CFM) architecture to learn distributions of surface geometries and biochemical properties, enhancing peptide binding accuracy. Evaluated on the comprehensive PepMerge benchmark, SurfFlow consistently outperforms full-atom baselines across all metrics. These results highlight the advantages of considering molecular surfaces in de novo peptide discovery and demonstrate the potential of integrating multiple protein modalities for more effective therapeutic peptide discovery.

Advances in AI and omics enable the creation of AI virtual cells (AIVCs)—multi-scale, multimodal neural network models that simulate molecules, cells, and tissues across diverse states. This vision outlines their design and collaborative development, promising to transform biological research through high-fidelity simulations, accelerating discoveries, and fostering interdisciplinary open science collaborations.

Nature, Published online: 02 August 2023; doi:10.1038/s41586-023-06221-2

The advances in artificial intelligence over the past decade are examined, with a discussion on how artificial intelligence systems can aid the scientific process and the central issues that remain despite advances.