Application of deep learning approaches in modelling cheminformatics data and discovery of novel therapeutic agents for prostate cancer
The recent explosion of chemical and biological information calls for fundamentally novel ways of dealing with big data in the life sciences. This problem can potentially be addressed by the latest technological breakthroughs on both software and hardware frontiers. In particular, the latest advances in artificial intelligence (AI) enable cognitive data processing at very large-scale by means of deep learning (DL). This project will develop a deep neural network (DNN) environment with a re-enforced learning component that will utilize GNU power to capture all available information on 100s of millions of existing small molecules (including their interactions with proteins and other cell components). The ultimate goal is to develop an “all chemistry on one chip” expert system that can accurately generate structures of a small molecule with user-defined biological, physical and chemical properties. Such a cognitive AI platform can be integrated with already existing technologies of high-throughput synthesis (click-chemistry) to yield a paradigm-shifting ‘molecular printer’ that will revolutionize life science.