Prediction of Small-Molecule Developability Using Large-Scale In Silico ADMET Models.
J Med Chem
; 66(20): 14047-14060, 2023 10 26.
Article
em En
| MEDLINE
| ID: mdl-37815201
ABSTRACT
Early in silico assessment of the potential of a series of compounds to deliver a drug is one of the major challenges in computer-assisted drug design. The goal is to identify the right chemical series of compounds out of a large chemical space to then subsequently prioritize the molecules with the highest potential to become a drug. Although multiple approaches to assess compounds have been developed over decades, the quality of these predictors is often not good enough and compounds that agree with the respective estimates are not necessarily druglike. Here, we report a novel deep learning approach that leverages large-scale predictions of â¼100 ADMET assays to assess the potential of a compound to become a relevant drug candidate. The resulting score, which we termed bPK score, substantially outperforms previous approaches and showed strong discriminative performance on data sets where previous approaches did not.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Simulação por Computador
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
J Med Chem
Assunto da revista:
QUIMICA
Ano de publicação:
2023
Tipo de documento:
Article
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