Department of Pharmacy, Xiangya Hospital, Central South University and the Xiangya School of Pharmaceutical Sciences, Central South University, Sichuan, China.
Xiangya School of Pharmaceutical Sciences, Central South University, Hunan, China.
Brief Bioinform. 2021 Sep 2;22(5). doi: 10.1093/bib/bbab017.
Substructure screening is widely applied to evaluate the molecular potency and ADMET properties of compounds in drug discovery pipelines, and it can also be used to interpret QSAR models for the design of new compounds with desirable physicochemical and biological properties. With the continuous accumulation of more experimental data, data-driven computational systems which can derive representative substructures from large chemical libraries attract more attention. Therefore, the development of an integrated and convenient tool to generate and implement representative substructures is urgently needed.
In this study, PySmash, a user-friendly and powerful tool to generate different types of representative substructures, was developed. The current version of PySmash provides both a Python package and an individual executable program, which achieves ease of operation and pipeline integration. Three types of substructure generation algorithms, including circular, path-based and functional group-based algorithms, are provided. Users can conveniently customize their own requirements for substructure size, accuracy and coverage, statistical significance and parallel computation during execution. Besides, PySmash provides the function for external data screening.
PySmash, a user-friendly and integrated tool for the automatic generation and implementation of representative substructures, is presented. Three screening examples, including toxicophore derivation, privileged motif detection and the integration of substructures with machine learning (ML) models, are provided to illustrate the utility of PySmash in safety profile evaluation, therapeutic activity exploration and molecular optimization, respectively. Its executable program and Python package are available at https://github.com/kotori-y/pySmash.
子结构筛选被广泛应用于评估药物发现管道中化合物的分子效力和 ADMET 性质，也可用于解释 QSAR 模型，以设计具有理想物理化学和生物性质的新化合物。随着越来越多的实验数据的不断积累，能够从大型化学库中提取代表性子结构的数据驱动计算系统受到了更多的关注。因此，迫切需要开发一种集成和方便的工具来生成和实施代表性子结构。
本研究开发了一种用户友好且功能强大的生成不同类型代表性子结构的工具 PySmash。当前版本的 PySmash 提供了 Python 包和独立的可执行程序，实现了操作简便和流水线集成。提供了三种子结构生成算法，包括循环、基于路径和基于官能团的算法。用户可以在执行过程中方便地自定义对子结构大小、准确性和覆盖率、统计显著性和并行计算的要求。此外，PySmash 还提供了外部数据筛选功能。
本研究提出了一种用户友好且集成的代表性子结构自动生成和实施工具 PySmash。提供了三个筛选示例，包括毒性原子推导、特权基检测以及子结构与机器学习（ML）模型的集成，分别说明了 PySmash 在安全性评估、治疗活性探索和分子优化方面的应用。其可执行程序和 Python 包可在 https://github.com/kotori-y/pySmash 上获得。
