arXiv:2606. 00555v1 Announce Type: new Abstract: Structure-based drug design increasingly employs LLM agents to iteratively refine ligands against a target pocket, yet a viable ligand must satisfy two often-conflicting objectives -- binding affinity and druggability -- which single optimization steps rarely improve together.
By Zaifei Yang, Weiyu Chen, Yaqing Wang, James Kwok
TopU-LBVS is a new multi‑target benchmark for ligand‑based virtual screening that addresses shortcomings of existing datasets by using hard‑negative decoys and a fixed 1:40 active‑to‑decoy ratio. It covers 93 protein targets across seven classes, provides three evaluation protocols (full, low‑data, and mini), and includes curated ChEMBL‑35 bioactivity data with property‑matched, structurally similar decoys. The benchmark demonstrates that performance drops sharply when moving from random‑decoy to hard‑negative evaluation, and it releases data, splits, code, and baseline implementations for reproducible comparison.
By Surbhi Kumar, Yuhe Zhou, Varun Shiralkar, Niu Huang, Baris Coskunuzer
TopU-LBVS is a new multi‑target benchmark for ligand‑based virtual screening that addresses shortcomings of previous datasets by using hard‑negative decoys and a fixed 1:40 active‑to‑decoy ratio. It covers 93 protein targets across seven classes, provides three evaluation protocols (full, low‑data, and mini), and includes curated ChEMBL‑35 bioactivity data with property‑matched, structurally similar decoys to reduce shortcut learning. The benchmark comes with released data, fixed splits, evaluation code, and baseline implementations for reproducible comparison of LBVS and molecular representation methods.
PMO-Dock introduces a standardized benchmark for docking‑based molecular optimization, comprising 25 tasks that span hit generation, lead optimization, and a specificity task that rewards strong on‑target binding while penalizing off‑target interactions. The protocol separates model development from final evaluation, using validation tasks for hyperparameter tuning and held‑out test tasks evaluated under a strict oracle budget to mimic realistic drug‑discovery constraints. Four diverse methods—Saturn, GenMol, Genetic‑guided GFlowNet, and ChemLlama—were benchmarked, revealing no single method dominates across all tasks and highlighting limited hyperparameter transferability, which underscores the need for method‑dependent selection strategies.
By Gor Simonyan, Tatevik Abrahamyan, Narek Abelyan, Tigran Fahradyan, Hrant Khachatrian
arXiv:2512. 02328v2 Announce Type: replace-cross Abstract: Selecting an effective docking algorithm is highly context-dependent, and no single method performs reliably across structural, chemical, and protocol regimes.
By Jiabao Brad Wang, Siyuan Cao, Hongxuan Wu, Yiliang Yuan, Mustafa Misir
Budgeted Quotient-Residual Guidance (QRG) is an inference-time correction that enables quotient-based lead‑optimization objectives—such as distances, contacts, and anchored substructures—to be actively used with frozen pocket‑conditioned molecular diffusion models without retraining. QRG lifts quotient covectors to metric‑horizontal ambient directions and delivers them through a trust budget set by the sampler’s step norm, providing a closed‑form sampler‑budget update and a product‑budget split for section and residual controls. Experiments on frozen TargetDiff backbones show that QRG improves ligand‑generation quality on fragment growing, scaffold hopping, and linker design while preserving novelty and diversity.
By Xinyu Wang, Jinbo Bi, Minghu Song