Smart chemistry using molecular graphs and artificial intelligence

The ultimate goal of computational chemistry is to predict chemical reactions and to design new molecules with desired properties. These are obviously challenging because molecular space is very large and discrete with a wide variety of molecules. For example, there are only 108 molecules synthesized as potential drug candidates, but 1060 molecules are estimated to be existing. Computer-aided methodology is a promising solution for those challenges. For example, a fast calculation method allows us to find molecules with target properties through high-throughput virtual screening over known databases. In this talk, we shall introduce our recent efforts for the development of new computational methods specifically focused on the following two topics.

Topic 1: Efficient prediction of chemical reaction paths

We propose a novel approach to rapidly search reaction paths in a fully automated fashion by combining chemical theory and heuristics. A key idea of our method is to extract a minimal reaction network composed of only favorable reaction pathways from the complex chemical space through molecular graph and reaction network analysis. It can be done very efficiently by exploring the routes connecting reactants and products with minimum dissociation and formation of bonds. We will discuss the applicability of our method with several case studies.

Topic 2: Start molecular design using artificial intelligence.

We propose to use a molecular generative model based on artificial intelligence. It is specialized in controlling multiple molecular properties simultaneously, embedding them in namely the latent space. As a proof of concept, we will show that it can be used to generate a number of molecules as drugs with specific properties. We also apply it to design of new molecules with promising binding energy for a specific target protein and use them as potential drug candidates that are not in the database.

20180517_세미나_김우연 교수.pdf