Link-INVENT: generative linker design with reinforcement learning
Journal article, 2023
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.
Author
Jeff Guo
AstraZeneca AB
Franziska Knuth
AstraZeneca AB
Norwegian University of Science and Technology (NTNU)
Christian Margreitter
AstraZeneca AB
Jon Paul Janet
AstraZeneca AB
Kostas Papadopoulos
AstraZeneca AB
Ola Engkvist
Chalmers, Computer Science and Engineering (Chalmers)
AstraZeneca AB
Atanas Patronov
AstraZeneca AB
Digital Discovery
2635098X (eISSN)
Vol. 2 2 392-408Subject Categories
Computer Science
Computer Systems
DOI
10.1039/d2dd00115b