Comparison of random forest and Pipeline Pilot Naïve Bayes in prospective QSAR predictions.
J Chem Inf Model
; 52(3): 792-803, 2012 Mar 26.
Article
em En
| MEDLINE
| ID: mdl-22360769
ABSTRACT
Random forest is currently considered one of the best QSAR methods available in terms of accuracy of prediction. However, it is computationally intensive. Naïve Bayes is a simple, robust classification method. The Laplacian-modified Naïve Bayes implementation is the preferred QSAR method in the widely used commercial chemoinformatics platform Pipeline Pilot. We made a comparison of the ability of Pipeline Pilot Naïve Bayes (PLPNB) and random forest to make accurate predictions on 18 large, diverse in-house QSAR data sets. These include on-target and ADME-related activities. These data sets were set up as classification problems with either binary or multicategory activities. We used a time-split method of dividing training and test sets, as we feel this is a realistic way of simulating prospective prediction. PLPNB is computationally efficient. However, random forest predictions are at least as good and in many cases significantly better than those of PLPNB on our data sets. PLPNB performs better with ECFP4 and ECFP6 descriptors, which are native to Pipeline Pilot, and more poorly with other descriptors we tried.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Árvores de Decisões
/
Relação Quantitativa Estrutura-Atividade
Tipo de estudo:
Clinical_trials
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
J Chem Inf Model
Assunto da revista:
INFORMATICA MEDICA
/
QUIMICA
Ano de publicação:
2012
Tipo de documento:
Article
País de afiliação:
Estados Unidos