Machine learning methods for pK prediction of small molecules: Advances and challenges.

Journal: Drug discovery today
Published Date: Sep 24, 2022

Abstract

The acid-base dissociation constant (pK) is a fundamental property influencing many ADMET properties of small molecules. However, rapid and accurate pK prediction remains a great challenge. In this review, we outline the current advances in machine-learning-based QSAR models for pK prediction, including descriptor-based and graph-based approaches, and summarize their pros and cons. Moreover, we highlight the current challenges and future directions regarding experimental data, crucial factors influencing pK and in silico prediction tools. We hope that this review can provide a practical guidance for the follow-up studies.

Authors

  • Jialu Wu
    Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang, China.
  • Yu Kang
    College of Pharmaceutical Sciences, Zhejiang University , Hangzhou, Zhejiang 310058, P. R. China.
  • Peichen Pan
    Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang, China. Electronic address: panpeichen@zju.edu.cn.
  • Tingjun Hou
    College of Pharmaceutical Sciences, Zhejiang University , Hangzhou, Zhejiang 310058, China.

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