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

Japanese

実用的なサンプル効率化(PMO)ベンチマークは分子最適化における標準化評価ですが、構造ベースの薬物設計をキャプチャしない単純なプロパティベースのオラクルに基づいて構築されています。フィールドがドッキングベースの目標に移行するにつれて、ベンチマークとベンチマークベースの評価プロトコルが非標準化され、異なる方法が異なるドッキングタスクをカバーする25の結果を報告し、しばしば公平な比較やサンプル効率の制約を共有するための新たな特異性のタスクを必要としません。これに対処するために、我々は実用的な分子効率化ドッキング(PMO-Dock)のための実用的な

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.

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

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