OPDiv：Top-K 高评分多样化化合物的最优选择
OPDiv: Optimal Selection of Top-K High-Scoring, Diverse Compounds
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中文总结 AI 辅助
OPDiv 通过整数优化在虚拟筛选中平衡评分与多样性，实现 top-k 高评分多样化化合物的最优选择，并提供公平基准。
中文摘要 AI 辅助
虚拟筛选活动可能产生数千个有希望的候选化合物，但只有少数可以被购买、合成或测试。实际问题是，如何选择一组既排名靠前又足够多样化的化合物：这构成了一个真正的权衡，其中选择评分最高的分子会导致多样性有限，而多样性选择则会牺牲一些评分高的分子。我们提出了 OPDiv，一种通过整数优化寻找最优分子子集来解决这一权衡的多样性选择与评估算法。我们通过指纹距离、形状和静电多样性在实践中展示了该选择算法，并比较了由此产生的多样性谱。我们认为，虚拟筛选不仅仅是一个排序问题，也是一个隐式的约束优化任务：当冗余的化学类型不受欢迎时，应基于满足所需多样性约束的 top-k 化合物选择来比较不同流程。OPDiv 使得在给定多样性阈值下高效地找到最优化合物集合成为可能，并为给定基于结构或基于配体的虚拟筛选流程、分子搜索或生成模型所能实现的最佳多样化选择提供了一个公平的基准。
英文摘要
A virtual screening campaign may produce thousands of promising candidates, but only a small number can be purchased, synthesized, or tested. The practical question is how to select a set of compounds that both rank well and are diverse enough: this poses a genuine tradeoff, where selecting the highest-scoring molecules yields limited diversity, while diversity selection sacrifices some well-scoring molecules. We introduce OPDiv, a diversity selection and evaluation algorithm solving this tradeoff by finding an optimal subset of molecules using integer optimization. We demonstrate the selection algorithm in practice with fingerprint distance, shape and electrostatic diversity and compare the resulting diversity spectra. We argue that virtual screening is not merely a ranking problem, but also an implicit constrained optimization task: when redundant chemotypes are undesirable, pipelines should be compared based on the top-k compound selections satisfying the desired diversity constraints. OPDiv makes it possible to find the optimal compound set under a given diversity threshold efficiently and serves as a fair benchmark of the best diverse selection achievable by a given structure-based or ligand-based virtual screening pipeline, molecular search or generative model.
发表机构
- Deep MedChem
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