arXiv：cs.LG· Gor Simonyan, Tatevik Abrahamyan, Narek Abelyan, Tigran Fahradyan, Hrant Khachatrian·· 2 小时前
PMO-Dock：面向分子优化的对接、特异性与泛化基准
PMO-Dock: Benchmarking Docking, Specificity, and Generalization in Molecular Optimization
AI 导读
研究团队提出 PMO-Dock，一个面向对接的分子优化基准与协议，包含覆盖 hit generation、lead optimization 及新增特异性任务的 25 个任务，要求强靶向结合同时惩罚脱靶相互作用。
正文
Abstract:The Practical Molecular Optimization (PMO) benchmark standardized evaluation in molecular optimization, but it is built on simple property-based oracles that do not capture structure-based drug design. As the field has moved to docking-based objectives, evaluation practice has become unstandardized, and different methods report results on different docking tasks, often without a shared protocol for fair comparison or sample-efficiency constraints. To address this, we introduce Practical Molecular Optimization for Docking (PMO-Dock), a benchmark and protocol for docking-based optimization consisting of 25 tasks covering hit generation, lead optimization, and a new specificity task requiring strong on-target binding while penalizing off-target interactions. The protocol separates model development from final evaluation: validation tasks are used for hyperparameter tuning, and test tasks are held out for final evaluation and are not used during development. Evaluation on the test tasks is done under a strict oracle budget to reflect realistic drug-discovery settings. We benchmark four diverse high-performing methods spanning different optimization paradigms, Saturn (reinforcement learning), GenMol (discrete diffusion), Genetic-guided GFlowNet, and ChemLlama (LLM-based evolutionary search). Our results show no universally best method across tasks, additional analyses of hyperparameter transfer (cross-task and cross-category) and sensitivity show that transfer is often limited and varies by method, motivating method-dependent selection strategies. This benchmark provides a concrete standard for evaluating generalization to unseen tasks and sample efficiency under a limited test-time oracle budget.
| Subjects: | Machine Learning (cs.LG); Quantitative Methods (q-bio.QM) |
| Cite as: | arXiv:2610.19011 [cs.LG] |
| (or arXiv:2610.19011v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.19011 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Hrant Khachatrian [view email]
[v1]
Tue, 13 Oct 2026 18:07:37 UTC (1,976 KB)
来源：arXiv：cs.LG · arxiv.org