Theoretical and Computational Chemistry

Link-INVENT: Generative Linker Design with Reinforcement Learning

Authors

Abstract

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 on fragment linking, scaffold hopping, and PROTACs 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.

Content

Thumbnail image of LinkINVENT-Manuscript.pdf

Supplementary material

Thumbnail image of LinkINVENT-Supporting-Information.pdf
Supporting Information
• Details related to the data preparation • Details on the vocabulary of the Link-INVENT model • Details on the new linker specific components implemented in Link-INVENT • Details on the docking protocol used including parameters • Hardware information and experiment computation times • All training plots for the experiments presented in this work • More example binding poses for experiments 1 and 2

Supplementary weblinks

Tutorial Code
Jupyter notebook tutorial for Link-INVENT