Machine learning methods for pKa prediction of small molecules: Advances and challenges.
Drug Discov Today
; 27(12): 103372, 2022 12.
Article
em En
| MEDLINE
| ID: mdl-36167281
ABSTRACT
The acid-base dissociation constant (pKa) is a fundamental property influencing many ADMET properties of small molecules. However, rapid and accurate pKa prediction remains a great challenge. In this review, we outline the current advances in machine-learning-based QSAR models for pKa 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 pKa and in silico prediction tools. We hope that this review can provide a practical guidance for the follow-up studies.
Palavras-chave
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Aprendizado de Máquina
Tipo de estudo:
Guideline
/
Observational_studies
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Prognostic_studies
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Risk_factors_studies
Idioma:
En
Revista:
Drug Discov Today
Assunto da revista:
FARMACOLOGIA
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TERAPIA POR MEDICAMENTOS
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
China