Speaker
Prof.
Woo Youn Kim
(Chemistry, KAIST)
Description
The ultimate goal of chemistry is to make new molecules with desired properties. It is challenging because chemical 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. High-throughput virtual screening approach has attracted great attention but still requires large costs and time. In this talk, we propose to use a molecular generative model based on deep learning algorithm as an alternative. 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.
