生物信息学
广告
化学空间
计算机科学
药物发现
数量结构-活动关系
虚拟筛选
生化工程
计算生物学
组合化学
机器学习
药品
化学
生物信息学
生物
工程类
药理学
生物化学
基因
作者
Brian B. Masek,David Baker,Roman J. Dorfman,Karen Dubrucq,Victoria C. Francis,Stephan Nagy,Bree L. Richey,Farhad Soltanshahi
标识
DOI:10.1021/acs.jcim.5b00697
摘要
We describe a “multistep reaction driven” evolutionary algorithm approach to de novo molecular design. Structures generated by the approach include a proposed synthesis path intended to aid the chemist in assessing the synthetic feasibility of the ideas that are generated. The methodology is independent of how the design ideas are scored, allowing multicriteria drug design to address multiple issues including activity at one or more pharmacological targets, selectivity, physical and ADME properties, and off target liabilities; the methods are compatible with common computer-aided drug discovery “scoring” methodologies such as 2D- and 3D-ligand similarity, docking, desirability functions based on physiochemical properties, and/or predictions from 2D/3D QSAR or machine learning models and combinations thereof to be used to guide design. We have performed experiments to assess the extent to which known drug space can be covered by our approach. Using a library of 88 generic reactions and a database of ∼20 000 reactants, we find that our methods can identify “close” analogs for ∼50% of the known small molecule drugs with molecular weight less than 300. To assess the quality of the in silico generated synthetic pathways, synthesis chemists were asked to rate the viability of synthesis pathways: both “real” and in silico generated. In silico reaction schemes generated by our methods were rated as very plausible with scores similar to known literature synthesis schemes.
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