Your browser doesn't support javascript.
loading
Interpretation of QSAR Models by Coloring Atoms According to Changes in Predicted Activity: How Robust Is It?
Sheridan, Robert P.
Afiliação
  • Sheridan RP; Modeling and Informatics , Merck & Co. Inc. , Kenilworth , New Jersey 07065 , United States.
J Chem Inf Model ; 59(4): 1324-1337, 2019 04 22.
Article em En | MEDLINE | ID: mdl-30779563
ABSTRACT
Most chemists would agree that the ability to interpret a quantitative structure-activity relationship (QSAR) model is as important as the ability of the model to make accurate predictions. One type of interpretation is coloration of atoms in molecules according to the contribution of each atom to the predicted activity, as in "heat maps". The ability to determine which parts of a molecule increase the activity in question and which decrease it should be useful to chemists who want to modify the molecule. For that type of application, we would hope the coloration to not be particularly sensitive to the details of model building. In this Article, we examine a number of aspects of coloration against 20 combinations of descriptors and QSAR methods. We demonstrate that atom-level coloration is much less robust to descriptor/method combinations than cross-validated predictions. Even in ideal cases where the contribution of individual atoms is known, we cannot always recover the important atoms for some descriptor/method combinations. Thus, model interpretation by atom coloration may not be as simple as it first appeared.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Simulação por Computador / Relação Quantitativa Estrutura-Atividade Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: J Chem Inf Model Assunto da revista: INFORMATICA MEDICA / QUIMICA Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Simulação por Computador / Relação Quantitativa Estrutura-Atividade Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: J Chem Inf Model Assunto da revista: INFORMATICA MEDICA / QUIMICA Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Estados Unidos