Molecular reinforcement learning
Project description
Molecular Reinforcement Learning
Unlocking reinforcement learning for drug design
MRL is an open source python library designed to unlock the potential of drug design with reinforcement learning.
MRL bridges the gap between generative models and practical drug discovery by enabling fine-tuned control over chemical spaces. Control what structures are generated and where they occur.
MRL is suitable for all stages of drug discovery, from high diversity hit expansion screens to hyper-focused lead optimization
Use Cases
MRL can be applied to:
- Small molecule design
- Peptide design
View our tutorials for more examples
Install
Package coming soon
How to use
Contributing
Acknowledgements
Project details
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