SVM Model for Virtual Screening of Lck Inhibitors

• Published in: Journal of Chemical Information and Modeling Vol. 49; no. 4; pp. 877 - 885
• Main Authors: Liew, Chin Y, Ma, Xiao H, Liu, Xianghui, Yap, Chun W
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 27.04.2009
• Subjects: Analytical chemistry; Artificial Intelligence; Chemical compounds; Computational Chemistry; Computer Simulation; Drug Design; Drug Evaluation, Preclinical - methods; Enzyme Inhibitors - chemical synthesis; Enzyme Inhibitors - chemistry; Enzyme Inhibitors - pharmacology; Kinases; Logistic Models; Lymphocyte Specific Protein Tyrosine Kinase p56(lck) - antagonists & inhibitors; Lymphocytes; Models, Chemical; Neural Networks (Computer); Proteins; Reproducibility of Results; Structure-Activity Relationship; Subject Headings
• ISSN: 1549-9596; 1520-5142; 1549-960X
• DOI: 10.1021/ci800387z

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 IC50 ≤ 10 μM. 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.