Machine learning methods for pKa prediction of small molecules: Advances and challenges. Issue 12 (December 2022) - Wu, Jialu; Kang, Yu; Pan, Peichen; Hou, Tingjun
Highlights: QSAR models for p K a prediction comprise descriptor-based and graph-based approaches. Data scarcity and the intrinsic complexity of p K a are major challenges for prediction. Graph neural networks combined with domain knowledge are powerful and promising. p K a prediction tools will hopefully be a significant component in AI-driven drug design. Abstract : The acid–base dissociation constant (p K a ) is a fundamental property influencing many ADMET properties of small molecules. However, rapid and accurate p K a prediction remains a great challenge. In this review, we outline the current advances in machine-learning-based QSAR models for p K a 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 p K a and in silico prediction tools. We hope that this review can provide a practical guidance for the follow-up studies.