Developing drug candidates from scratch has turned into a billion-dollar expense that is not delivering enough profitable products to market. Novel approaches which merge chemistry with biology and informatics contribute to the development of selective lifesaving drugs needed by patients. We implement machine learning classifiers for HTS Data Analysis, Screening and drug repurposing with high probability of selecting drug candidates eligible for Phase II of clinical study free from ADME/Tox-related problems.We used small molecule bioactivity data for HCV RNA Polymerase to train and test QSAR models and apply these robust models for compound ranking and hit identification in drug repositioning techniques. Random Forest and kNN algorithms were used with Morgan fingerprints of 679 small molecules with curated IC50 values. After filtering various drug-like databases (DrugBank, MDL, NIAID-NIH, ComGenex) compounds were selected and tested against HCV.We discuss the challenges in drug repositioning faced in academia, government and pharmaceutical industry.

Improving drug development by connecting medicinal chemistry with drug repositioning and modern machine learning methods

244th National Meeting (2012)

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