药效团
开源
选择(遗传算法)
集合(抽象数据类型)
特征(语言学)
特征选择
计算机科学
化学空间
特征向量
计算生物学
虚拟筛选
数据挖掘
药物发现
情报检索
人工智能
机器学习
生物信息学
生物
程序设计语言
软件
语言学
哲学
作者
Francesco Rianjongdee,David Scott Palmer,Stephen D. Pickett,Péter Pogány,Nicholas C. O. Tomkinson,Darren V. S. Green
标识
DOI:10.1021/acs.jcim.4c00453
摘要
The design of compounds during hit-to-lead often seeks to explore a vector from a core scaffold to form additional interactions with the target protein. A rational approach to this is to probe the region of a protein accessed by a vector with a systematic placement of pharmacophore features in 3D, particularly when bound structures are not available. Herein, we present bbSelect, an open-source tool built to map the placements of pharmacophore features in 3D Euclidean space from a library of R-groups, employing partitioning to drive a diverse and systematic selection to a user-defined size. An evaluation of bbSelect against established methods exemplified the superiority of bbSelect in its ability to perform diverse selections, achieving high levels of pharmacophore feature placement coverage with selection sizes of a fraction of the total set and without the introduction of excess complexity. bbSelect also reports visualizations and rationale to enable users to understand and interrogate results. This provides a tool for the drug discovery community to guide their hit-to-lead activities.
科研通智能强力驱动
Strongly Powered by AbleSci AI