Newest papers

PMO-Dock: Benchmarking Docking, Specificity, and Generalization in Molecular Optimization

Gor Simonyan, Tatevik Abrahamyan, Narek Abelyan, Tigran Fahradyan, Hrant Khachatrian · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.19011 · cited by 0 works (OpenAlex) · open-access PDF

Korean

실용적인 샘플 효율성 최적화 (PMO) 벤치마크는 분자 최적화에서 표준화된 평가이지만, 구조 기반 약물 디자인을 캡처하지 않는 단순한 속성 기반 오라클을 기반으로합니다. 필드가 도킹 기반 목표로 이동함에 따라, 벤치마크 및 벤치마크 기반 최적화를위한 프로토콜은 표준화되었으며, 종종 공정한 비교 또는 샘플 전송 효율성 제약을위한 공유 프로토콜이 없습니다.이 문제를 해결하기 위해, 우리는 실용적인 분자 최적화 (PMO-Dock)를 도킹을위한 벤치마크 및 벤치마크 기반 최적화를위한 프로토콜을 소개합니다. 25 개의 작업이 히트 생성, 리드 최적화

Machine translation (Meta M2M100); the English original follows.

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.

Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese

Topics: Computer science, Benchmark (surveying), Oracle, Benchmarking, Machine learning, Generalization

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange