Improvement in ADMET Prediction with Multitask Deep Featurization.
J Med Chem
; 63(16): 8835-8848, 2020 08 27.
Article
en En
| MEDLINE
| ID: mdl-32286824
ABSTRACT
The absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties of drug candidates are important for their efficacy and safety as therapeutics. Predicting ADMET properties has therefore been of great interest to the computational chemistry and medicinal chemistry communities in recent decades. Traditional cheminformatics approaches, using learners such as random forests and deep neural networks, leverage fingerprint feature representations of molecules. Here, we learn the features most relevant to each chemical task at hand by representing each molecule explicitly as a graph. By applying graph convolutions to this explicit molecular representation, we achieve, to our knowledge, unprecedented accuracy in prediction of ADMET properties. By challenging our methodology with rigorous cross-validation procedures and prognostic analyses, we show that deep featurization better enables molecular predictors to not only interpolate but also extrapolate to new regions of chemical space.
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Compuestos Orgánicos
/
Aprendizaje Automático Supervisado
/
Aprendizaje Profundo
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Límite:
Animals
/
Humans
Idioma:
En
Revista:
J Med Chem
Asunto de la revista:
QUIMICA
Año:
2020
Tipo del documento:
Article
País de afiliación:
Estados Unidos