(209e) Accelerating Molecular Discovery with Machine Learning and AI

Deep learning is revolutionizing many areas of science and technology, particularly in natural language processing, speech recognition and computer vision. In this talk, we will provide an overview into latest developments of machine learning and AI methods and application to the problem of drug discovery and development at Isayev’s Lab at CMU. We identify several areas where existing methods have the potential to accelerate pharmaceutical research and disrupt more traditional approaches. First we will present a deep learning model that approximate solution of Schrodinger equation. Focusing on parametrization for drug-like organic molecules and proteins, we have developed a single ‘universal’ model which is highly accurate compared to reference quantum mechanical calculations at speeds 10^6 faster. Second, we proposed a novel computational method for de-novo design of molecular compounds with desired biological and physical properties.