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Development and experimental test of support vector machines virtual screening method for searching Src inhibitors
Bucong Han1, Xiaohua Ma, Ruiying Zhao
1The Key Laboratory of Chemical Biology, Guangdong Province, The Graduate School at Shenzhen, Tsinghua University, Shenzhen, Guangdong, 518055, People's Republic of China. jiangyy@sz.tsinghua.edu.cn.
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.
Insights
Support vector machines (SVM) effectively identify Src inhibitors in large compound libraries, offering a promising in-silico method with reduced false hits for cancer drug discovery.
Area of Science:
- Biochemistry and Molecular Biology
- Computational Chemistry
- Pharmacology
Background:
- Src kinase is a key target in cancer progression, invasion, and metastasis.
- Drug resistance necessitates the discovery of novel Src inhibitors.
- In-silico methods are crucial for efficient screening of potential inhibitors.
Purpose of the Study:
- To evaluate Support Vector Machines (SVM) as a virtual screening tool for identifying Src inhibitors.
- To assess SVM's performance in searching large compound libraries for potential Src inhibitors.
- To compare SVM with other machine-learning and similarity-based virtual screening methods.
Main Methods:
- Trained and tested SVM models using a dataset of 1,703 known Src inhibitors and 63,318 non-inhibitors.
- Performed 5-fold cross-validation to assess model accuracy.
- Screened large compound databases (PubChem, MDDR) using the trained SVM models.
Main Results:
- SVM achieved high accuracy, correctly identifying 93.53%–95.01% of inhibitors and 99.81%–99.90% of non-inhibitors in cross-validation.
- SVM successfully identified 70.45% of newly reported Src inhibitors (since 2011).
- SVM predicted a low percentage of false positives when screening large compound libraries.
Conclusions:
- SVM demonstrates comparable yield and reduced false-hit rates compared to other virtual screening methods.
- SVM shows potential for efficiently searching large compound libraries for Src inhibitors.
- Experimental validation of SVM-identified compounds confirmed moderate activity for one novel scaffold, supporting SVM's utility.
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