arXiv:2606. 08375v1 Announce Type: new Abstract: All-atom generative modeling of 3D biomolecular complexes has emerged as the dominant paradigm for predicting the structure of proteins and protein-ligand systems.
By Gianluca Scarpellini, Ron Shprints, Peter Holderrieth, Juno Nam, Pranav Murugan, Rafael G\'omez-Bombarelli, Tommi Jaakola, Maruan Al-Shedivat, Nicholas Matthew Boffi, Avishek Joey Bose
MLIPDiff is an inference‑time guidance technique that enhances pretrained denoising diffusion models for ligand generation by incorporating physics‑informed guidance from a machine‑learned interatomic potential (UMA). It combines four weighted energy terms—protein‑ligand interaction, ligand strain, steric clash, and pocket anchoring—to eliminate protein‑ligand collisions while preserving local geometry, without requiring model retraining. On the CrossDocked2020 benchmark, MLIPDiff produces clash‑free, physically plausible ligand poses and achieves superior binding‑affinity metrics compared to both the base diffusion model and NucleusDiff.
By Yiyan Liao, Amin Tavakoli, Yu Zhang, Anima Anandkumar
The paper introduces two closed‑form projection operators that enforce physical validity in biomolecular diffusion models during inference. One operator resolves inter‑chain van der Waals clashes, while the other restores ligand geometry, including bond lengths, angles, planarity, and chirality. These lightweight, model‑agnostic corrections preserve structural accuracy and ligand placement across multiple benchmarks without adding runtime or memory overhead.
By Qurat-ul-ain, Yee Whye Teh, Charlotte M. Deane, Matteo Cagiada
CryoAtlas is a large, rigorously curated dataset and unified benchmark for density‑guided atomic model building of protein chains from cryo‑EM maps. It compiles 17,809 map‑model‑sequence entries from EMDB and PDB (up to March 2026), with a 16,657‑entry training split, and defines a 618‑target benchmark that evaluates structural accuracy, map fit, and stereochemical validity. The resource enables systematic comparison of learning‑based methods with traditional tools like Phenix and AlphaFold3, and demonstrates that retraining a CNN component on CryoAtlas improves performance.
By Mingrui Li, Minzhang Li, Weichen Qin, Yufan Xie, Zhangzhi Xiong, Sixian Shen, Jiakai Zhang, Yuan Pei, Jingyi Yu
arXiv:2606. 19377v1 Announce Type: cross Abstract: Computational enzyme design requires generating proteins that scaffold catalytic residues and ligands, a task that demands both geometric accuracy and structural diversity from the underlying generative model.
By Nicholas J. Williams, Ward Haddadin, Matteo P. Ferla, Constantin Schneider, Nicholas B. Woodall, Ruby Sedgwick, Christian D. Madsen, Andrew L. Hopkins, Edward O. Pyzer-Knapp
arXiv:2608. 11475v1 Announce Type: cross Abstract: AlphaFold3-class structure predictors pair a representational trunk, which processes sequence and context, with a diffusion module, which generates atomic coordinates.
By Piotr Jedryszek, Tongmeng Xie, Adam Winnifrith, Alexander Hasson, Weronika \'Slesak, George Wicks, Toby Winnifrith, Oliver M. Crook