arXiv:2606. 23856v1 Announce Type: new Abstract: Generative molecular models for drug design are a promising direction with much active research.
By Konstantin Yatsenko, Arvind Thiagarajan
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.
By Yiming Qin, Kai Yi, Miruna Cretu, Sjors H. W. Scheres, Pietro Li\`o, Pascal Frossard
arXiv:2607. 18144v1 Announce Type: cross Abstract: Structure-based drug design (SBDD) leverages the 3D structure of protein targets, often complemented by other spatial constraints, to generate candidate binding molecules.
By Thomas MacDougall, Maksim Kuznetsov, Roman Schutski, Rim Shayakhmetov, Maxim Malkov, Vladimir Aladinskiy, Alex Aliper, Alex Zhavoronkov
FuseDiff is an end‑to‑end diffusion model designed for dual‑target structure‑based drug design, jointly generating a ligand graph and two pocket‑specific binding poses conditioned on both target pockets. It employs a message‑passing backbone with Dual‑target Local Context Fusion (DLCF) to fuse ligand atom contexts from both pockets, preserving symmetry while enabling expressive joint modeling. The model enforces topological consistency across the two poses and allows target‑specific geometric adaptation, achieving state‑of‑the‑art docking performance and enabling systematic assessment of dual‑target pose quality before docking‑based pose search.
By Jianliang Wu, Anjie Qiao, Zhen Wang, Zhewei Wei, Sheng Chen
NEAT-POCKET is a pocket‑conditioned extension of the autoregressive NEAT model that generates 3D molecules atom by atom within protein binding pockets, maintaining atom permutation invariance and explicitly modeling hydrogen atoms. It outperforms existing baselines on the CrossDocked and SPINDR datasets, achieving competitive structure‑based generation performance while sampling significantly faster. The model also supports pocket‑conditioned fragment completion, a capability directly useful for lead optimization and scaffold elaboration in drug design.
By Roxane Axel Jacob, Daniel Rose, Thierry Langer, Johannes Kirchmair
arXiv:2606. 14159v1 Announce Type: new Abstract: Protein-ligand binding affinity (PLA) prediction is critical in drug discovery.
By Shuai Li, Chuan-Xian Ren, Yuhao Li, Ziqi Huang, Yue Pan, Mingzhe Tang, Hong Yan