| About this Abstract |
| Meeting |
MS&T22: Materials Science & Technology
|
| Symposium
|
AI for Big Data Problems in Advanced Imaging, Materials Modeling and Automated Synthesis
|
| Presentation Title |
De Novo Molecular Drug Design Using Deep and Reinforcement Learning |
| Author(s) |
Srilok Srinivasan, Rohit Batra, Henry Chan, Mathew Cherukara, Jonathan Steckbeck, Nicholas Nystrom, Subramanian Sankaranarayanan |
| On-Site Speaker (Planned) |
Srilok Srinivasan |
| Abstract Scope |
The astronomically large chemical space of drug discovery exceeds what can be efficiently explored by current screening approaches such as docking or high-throughput library screening, which are limited by high computational cost or the low coverage of libraries over the chemical space. We present a de novo design strategy that leverages deep learning and reinforcement learning to design compounds that effectively bind to a target protein of interest. Our workflow integrates a Monte Carlo Tree Search algorithm with deep neural network architectures to operate in a generative fashion and effectively sample the vast chemical space. We generate several new biomolecules that outperform or show competitive performance compared to, known FDA-approved and non-FDA-approved biomolecules from existing databases. |