Link-INVENT: generative linker design with reinforcement learning

In this work, we present Link-INVENT as an extension to the existing de novo molecular design platform REINVENT. We provide illustrative examples on how Link-INVENT can be applied to fragment linking, scaffold hopping, and PROTAC design case studies where the desirable molecules should satisfy a combination of different criteria. With the help of reinforcement learning, the agent used by Link-INVENT learns to generate favourable linkers connecting molecular subunits that satisfy diverse objectives, facilitating practical application of the model for real-world drug discovery projects. We also introduce a range of linker-specific objectives in the Scoring Function of REINVENT. The code is freely available at https://github.com/MolecularAI/Reinvent. Link-INVENT enables design of PROTACs, fragment linking, and scaffold hopping while satisfying multiple optimization criteria.

Automated summary

This work presents Link-INVENT as an extension to the existing de novo molecular design platform REINVENT. Illustrative examples demonstrate its applications in fragment linking, scaffold hopping, and PROTAC design case studies. Link-INVENT, with the help of reinforcement learning, allows the agent to generate favorable linkers that connect molecular subunits satisfying diverse objectives, making it practical for real-world drug discovery projects.