SVM Model for Virtual Screening of Lck Inhibitors

Abstract

Lymphocyte-specific protein tyrosine kinase (Lck) inhibitors have treatment potential for autoimmune diseases and transplant rejection. A support vector machine (SVM) model trained with 820 positive compounds (Lck inhibitors) and 70 negative compounds (Lck noninhibitors) combined with 65 142 generated putative negatives was developed for predicting compounds with a Lck inhibitory activity of IC(50) < or = 10 microM. The SVM model, with an estimated sensitivity of greater than 83% and specificity of greater than 99%, was used to screen 168 014 compounds in the MDDR and was found to have a yield of 45.8% and a false positive rate of 0.52%. The model was also able to identify novel Lck inhibitors and distinguish inhibitors from structurally similar noninhibitors at a false positive rate of 0.27%. To the best of our knowledge, the SVM model developed in this work is the first model with a broad applicability domain and low false positive rate, which makes it very suitable for the virtual screening of chemical libraries for Lck inhibitors.

Prediction of novel and selective TNF-alpha converting enzyme (TACE) inhibitors and characterization of correlative molecular descriptors by machine learning approaches

“…We divided more than 150,000 compounds from MDDR (removing those entries that have invalid structures or molecular descriptors) which have not been reported to have TACE binding activity into 759 clusters based on 189 calculated descriptors (vide infra). The scale of generated compound clusters in this work is consistent with that reported in other studies [50,51]. For each cluster, the compound closest to the centroid of the corresponding cluster was selected.…”

Virtual Screening Strategies in Drug Design

“…Além disso, observa-se também o emprego do SVM de forma combinada ao k-NN na classificação de inibidores e não inibidores de CYP1A2 (uma isoforma do citocromo P450), alcançando níveis de precisão de 73% a 76% sobre o conjunto de teste. 170 Liew e colaboradores 171 propuseram criar um modelo de SVM para realização de triagem virtual de inibidores de Lck (Lymphocyte-specific protein tyrosine kinase). Para tal, foram utilizados 66.032 compostos de 8.423 famílias químicas, reunidas a partir de estudos publicados na literatura e das bases de dados de estruturas e atividades biológicas do PUBCHEM e do MDDR.…”

Consensus model for identification of novel PI3K inhibitors in large chemical library

“…2, resulting in a model that cannot identify an active compound that has similar structure to the putative negative compounds. The extent of this risk is unknown but the results of this work and two other studies [23,27] have shown that such unwanted effect is expected to be relatively small and it was still possible for a substantial proportion of positive compounds to be classified correctly despite their membership in negative families. Nonetheless, the search for known PI3K inhibitors in this work was carried out to be as extensive as possible to minimize this risk.…”

“…Thus, this study has adopted the approach by Han et al [19] to generate putative inactive compounds to augment the negative training set. This method can generate putative negatives without requiring the knowledge of actual inactive compounds and studies had shown that classification models derived from these putative negatives can perform reasonably well in virtual screening [23,27]. Nonetheless, the effects of using a large number of putative negatives was examined to ensure that the change is not unacceptably detrimental to the identification of potential inhibitors.…”