title: Prediction of anticancer molecules using hybrid model developed on molecules screened against NCI-60 cancer cell lines.
abstract: BACKGROUND
  In past, numerous quantitative structure-activity relationship (QSAR) based models have been developed for predicting anticancer activity for a specific class of molecules against different cancer drug targets. In contrast, limited attempt have been made to predict the anticancer activity of a diverse class of chemicals against a wide variety of cancer cell lines. In this study, we described a hybrid method developed on thousands of anticancer and non-anticancer molecules tested against National Cancer Institute (NCI) 60 cancer cell lines.
  
  
  RESULTS
  Our analysis of anticancer molecules revealed that majority of anticancer molecules contains 18-24 carbon atoms and are dominated by functional groups like R2NH, R3N, ROH, RCOR, and ROR. It was also observed that certain substructures (e.g., 1-methoxy-4-methylbenzene, 1-methoxy benzene, Nitrobenzene, Indole, Propenyl benzene) are more abundant in anticancer molecules. Next, we developed anticancer molecule prediction models using various machine-learning techniques and achieved maximum matthews correlation coefficient (MCC) of 0.81 with 90.40% accuracy using support vector machine (SVM) based models. In another approach, a novel similarity or potency score based method has been developed using selected fragments/fingerprints and achieved maximum MCC of 0.82 with 90.65% accuracy. Finally, we combined the strength of above methods and developed a hybrid method with maximum MCC of 0.85 with 92.47% accuracy.
  
  
  CONCLUSIONS
  We developed a hybrid method utilizing the best of machine learning and potency score based method. The highly accurate hybrid method can be used for classification of anticancer and non-anticancer molecules. In order to facilitate scientific community working in the field of anticancer drug discovery, we integrate hybrid and potency method in a web server CancerIN. This server provides various facilities that includes; virtual screening of anticancer molecules, analog based drug design, and similarity with known anticancer molecules ( http://crdd.osdd.net/oscadd/cancerin).
authors: Bioinformatics Centre, Institute of Microbial Technology, Sector 39-A, Chandigarh, India. harinder@imtech.res.in.
authors: Harinder
authors: H
authors: Singh
authors: Bioinformatics Centre, Institute of Microbial Technology, Sector 39-A, Chandigarh, India. rahulk@imtech.res.in.
authors: Rahul
authors: R
authors: Kumar
authors: Bioinformatics Centre, Institute of Microbial Technology, Sector 39-A, Chandigarh, India. sandysingh@imtech.res.in.
authors: Sandeep
authors: S
authors: Singh
authors: Bioinformatics Centre, Institute of Microbial Technology, Sector 39-A, Chandigarh, India. kumardeep@imtech.res.in.
authors: Kumardeep
authors: K
authors: Chaudhary
authors: Bioinformatics Centre, Institute of Microbial Technology, Sector 39-A, Chandigarh, India. ankurgautam@imtech.res.in.
authors: Ankur
authors: A
authors: Gautam
authors: Bioinformatics Centre, Institute of Microbial Technology, Sector 39-A, Chandigarh, India. raghava@imtech.res.in.
authors: Gajendra P S
authors: GP
authors: Raghava
mesh_terms: D016588:Anticarcinogenic Agents / Q000737:chemistry* / Q000494:pharmacology; D002244:Carbon / Q000737:chemistry; D045744:Cell Line, Tumor / Q000187:drug effects*; D019295:Computational Biology; D004353:Drug Evaluation, Preclinical; D006801:Humans; D008958:Models, Molecular; D009369:Neoplasms / Q000188:drug therapy* / Q000473:pathology; D012984:Software
chemical_list: D016588:Anticarcinogenic Agents; D002244:Carbon