de Novo Drug Design Using Reinforcement Learning
| Title: |
de Novo Drug Design Using Reinforcement Learning |
| DNr: |
Berzelius-2024-147 |
| Project Type: |
LiU Berzelius |
| Principal Investigator: |
Hampus Gummesson Svensson <hamsven@chalmers.se> |
| Affiliation: |
Chalmers tekniska högskola |
| Duration: |
2024-04-09 – 2024-11-01 |
| Classification: |
10201 |
| Keywords: |
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Abstract
De novo drug design is the design of novel chemical entities that fit certain constraints. De novo drug design is a major challenge in pharmacology and a new focus in
AI for science research. There has been recent success in using reinforcement learning and generative models for de novo drug design.
This work aims to further investigate and improve the use of reinforcement learning for de novo drug design to steer better the generation of novel chemical entities that fit certain constraints. This could ultimately increase the productivity of de novo drug design to search the chemical space for new drugs more effectively.
