Abstract
BackgroundSrc plays various roles in tumour progression, invasion, metastasis, angiogenesis and survival. It is one of the multiple targets of multi-target kinase inhibitors in clinical uses and trials for the treatment of leukemia and other cancers. These successes and appearances of drug resistance in some patients have raised significant interest and efforts in discovering new Src inhibitors. Various in-silico methods have been used in some of these efforts. It is desirable to explore additional in-silico methods, particularly those capable of searching large compound libraries at high yields and reduced false-hit rates.ResultsWe evaluated support vector machines (SVM) as virtual screening tools for searching Src inhibitors from large compound libraries. SVM trained and tested by 1,703 inhibitors and 63,318 putative non-inhibitors correctly identified 93.53%~ 95.01% inhibitors and 99.81%~ 99.90% non-inhibitors in 5-fold cross validation studies. SVM trained by 1,703 inhibitors reported before 2011 and 63,318 putative non-inhibitors correctly identified 70.45% of the 44 inhibitors reported since 2011, and predicted as inhibitors 44,843 (0.33%) of 13.56M PubChem, 1,496 (0.89%) of 168 K MDDR, and 719 (7.73%) of 9,305 MDDR compounds similar to the known inhibitors.ConclusionsSVM showed comparable yield and reduced false hit rates in searching large compound libraries compared to the similarity-based and other machine-learning VS methods developed from the same set of training compounds and molecular descriptors. We tested three virtual hits of the same novel scaffold from in-house chemical libraries not reported as Src inhibitor, one of which showed moderate activity. SVM may be potentially explored for searching Src inhibitors from large compound libraries at low false-hit rates.
Highlights
Src plays various roles in tumour progression, invasion, metastasis, angiogenesis and survival
Performance of Support vector machines (SVM), k nearest neighbour (kNN) and probabilistic neural network (PNN) identification of Src inhibitors based on 5-fold cross validation test The parameters of our SVM, kNN and PNN models were determined by 5-fold cross-validation studies of Src inhibitors and non-inhibitors
The sensitivity of SVM, kNN and PNN is in the range of 93.53%~95.01%, 88.56%~92.94% and 93.53%~97.06%, the specificity in the range of 99.81%~99.90%, 99.57%~99.77% and 97.76% ~98.03%, and overall accuracy Q in the range of 99.67% ~99.76%, 99.35%~99.48% and 97.69%~97.91% respectively
Summary
Src plays various roles in tumour progression, invasion, metastasis, angiogenesis and survival It is one of the multiple targets of multi-target kinase inhibitors in clinical uses and trials for the treatment of leukemia and other cancers. It is one of the multiple kinase targets of While these in-silico methods have shown impressive capability in the identification of potential Src inhibitors, their applications may be affected by such problems as the vastness and sparse nature of chemical space needing to be searched, complexity and flexibility of target structures, difficulties in accurately estimating binding affinity and solvation effects on molecular binding, and limited representativeness of training active compounds [10,11,12]. SVM may be a potentially useful VS tool to complement other in-silico methods for searching Src inhibitors from large libraries
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