Semi-automated workflow for molecular pair analysis and QSAR-assisted transformation space expansion

Zi Yi Yang, Li Fu, Ai Ping Lu, Shao Liu, Ting Jun Hou*, Dong Sheng Cao*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

In the process of drug discovery, the optimization of lead compounds has always been a challenge faced by pharmaceutical chemists. Matched molecular pair analysis (MMPA), a promising tool to efficiently extract and summarize the relationship between structural transformation and property change, is suitable for local structural optimization tasks. Especially, the integration of MMPA with QSAR modeling can further strengthen the utility of MMPA in molecular optimization navigation. In this study, a new semi-automated procedure based on KNIME was developed to support MMPA on both large- and small-scale datasets, including molecular preparation, QSAR model construction, applicability domain evaluation, and MMP calculation and application. Two examples covering regression and classification tasks were provided to gain a better understanding of the importance of MMPA, which has also shown the reliability and utility of this MMPA-by-QSAR pipeline.

Original languageEnglish
Article number86
JournalJournal of Cheminformatics
Volume13
Issue number1
DOIs
Publication statusPublished - 13 Nov 2021

Scopus Subject Areas

  • Computer Science Applications
  • Physical and Theoretical Chemistry
  • Computer Graphics and Computer-Aided Design
  • Library and Information Sciences

User-Defined Keywords

  • Lead optimization
  • Medicinal chemical rule
  • MMPA
  • MMPA-by-QSAR pipeline
  • QSAR

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