SolTranNet-A Machine Learning Tool for Fast Aqueous Solubility Prediction.
J Chem Inf Model
; 61(6): 2530-2536, 2021 06 28.
Article
em En
| MEDLINE
| ID: mdl-34038123
ABSTRACT
While accurate prediction of aqueous solubility remains a challenge in drug discovery, machine learning (ML) approaches have become increasingly popular for this task. For instance, in the Second Challenge to Predict Aqueous Solubility (SC2), all groups utilized machine learning methods in their submissions. We present SolTranNet, a molecule attention transformer to predict aqueous solubility from a molecule's SMILES representation. Atypically, we demonstrate that larger models perform worse at this task, with SolTranNet's final architecture having 3,393 parameters while outperforming linear ML approaches. SolTranNet has a 3-fold scaffold split cross-validation root-mean-square error (RMSE) of 1.459 on AqSolDB and an RMSE of 1.711 on a withheld test set. We also demonstrate that, when used as a classifier to filter out insoluble compounds, SolTranNet achieves a sensitivity of 94.8% on the SC2 data set and is competitive with the other methods submitted to the competition. SolTranNet is distributed via pip, and its source code is available at https//github.com/gnina/SolTranNet.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Água
/
Aprendizado de Máquina
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article