Development and experimental test of support vector machines virtual screening method for searching Src inhibitors from large compound libraries

Chemistry Central Journal

Bucong Han³  Xiaohua Ma³  Ruiying Zhao¹  Jingxian Zhang³  Xiaona Wei³  Xianghui Liu³  Xin Liu³  Cunlong Zhang²  Chunyan Tan²  Yuyang Jiang²  Yuzong Chen³ 

[1] Central Research Institute of China Chemical Science and Technology, 20 Xueyuan Road, Haidian District, Beijing, 100083, People’s Republic of China

[2] The Key Laboratory of Chemical Biology, Guangdong Province, The Graduate School at Shenzhen, Tsinghua University, Shenzhen, Guangdong, 518055, People’s Republic of China

[3] Bioinformatics and Drug Design Group, Department of Pharmacy, Centre for Computational Science and Engineering, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, Singapore, 117543, Singapore

关键词: Support vector machine;    Virtual screening;    Kinase inhibitor;    Computer aided drug design;    c-src;    Src;   

Others  :  788016
DOI  :  10.1186/1752-153X-6-139

 received in 2012-07-13, accepted in 2012-11-07,  发布年份 2012

【 摘 要 】

Background

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. 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.

Results

We 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.

Conclusions

SVM 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.

【 授权许可】

   
2012 Han et al.; licensee Chemistry Central Ltd.

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