Best of both worlds: combining pharma data and state of the art modeling technology to improve in Silico pKa prediction.
J Chem Inf Model
; 55(2): 389-97, 2015 Feb 23.
Article
em En
| MEDLINE
| ID: mdl-25514239
ABSTRACT
In a unique collaboration between a software company and a pharmaceutical company, we were able to develop a new in silico pKa prediction tool with outstanding prediction quality. An existing pKa prediction method from Simulations Plus based on artificial neural network ensembles (ANNE), microstates analysis, and literature data was retrained with a large homogeneous data set of drug-like molecules from Bayer. The new model was thus built with curated sets of â¼14,000 literature pKa values (â¼11,000 compounds, representing literature chemical space) and â¼19,500 pKa values experimentally determined at Bayer Pharma (â¼16,000 compounds, representing industry chemical space). Model validation was performed with several test sets consisting of a total of â¼31,000 new pKa values measured at Bayer. For the largest and most difficult test set with >16,000 pKa values that were not used for training, the original model achieved a mean absolute error (MAE) of 0.72, root-mean-square error (RMSE) of 0.94, and squared correlation coefficient (R(2)) of 0.87. The new model achieves significantly improved prediction statistics, with MAE = 0.50, RMSE = 0.67, and R(2) = 0.93. It is commercially available as part of the Simulations Plus ADMET Predictor release 7.0. Good predictions are only of value when delivered effectively to those who can use them. The new pKa prediction model has been integrated into Pipeline Pilot and the PharmacophorInformatics (PIx) platform used by scientists at Bayer Pharma. Different output formats allow customized application by medicinal chemists, physical chemists, and computational chemists.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Simulação por Computador
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Bases de Dados Factuais
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Modelos Químicos
Tipo de estudo:
Prognostic_studies
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Risk_factors_studies
Idioma:
En
Revista:
J Chem Inf Model
Assunto da revista:
INFORMATICA MEDICA
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QUIMICA
Ano de publicação:
2015
Tipo de documento:
Article
País de afiliação:
Estados Unidos