Link-INVENT: generative linker design with reinforcement learning. (13th February 2023)

• Record Type: Journal Article
• Authors: Guo, Jeff; Knuth, Franziska; Margreitter, Christian; Janet, Jon Paul; Papadopoulos, Kostas; Engkvist, Ola; Patronov, Atanas
• Is Part Of: Digital discovery. Volume 2:Number 2(2023)
• Page Start: 392
• Page End: 408
• Publication Date: 2023-02-13
• Subjects: Chemistry -- Data processing -- Periodicals; Medical sciences -- Data processing -- Periodicals; Machine learning -- Periodicals; 542.85
• DOI: 10.1039/d2dd00115b
• Languages: English
• ISSNs: 2635-098X

Abstract

Abstract : Link-INVENT enables design of PROTACs, fragment linking, and scaffold hopping while satisfying multiple optimization criteria. 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 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 .