Lib-INVENT: Reaction Based Generative Scaffold Decoration for in silico Library Design

20 August 2021, Version 2
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

Due to the strong relationship between desired molecular activity to its structural core, screening of focused, core sharing chemical libraries is a key step in lead optimisation. Despite the plethora of current research focused on in silico methods for molecule generation, to our knowledge, no tool capable of designing such libraries has been proposed. In this work, we present a novel tool for de novo drug design called Lib-INVENT. This is capable of rapidly proposing chemical libraries of compounds sharing the same core while maximising a range of desirable properties. To further help the process of designing focused libraries, the user can list specific chemical reactions that can be used for the library creation. Lib-INVENT is therefore a flexible tool for generating virtual chemical libraries for lead optimisation in a broad range of scenarios. Additionally, the shared core ensures that the compounds in the library are similar, possessing desirable properties and can be also synthesized under the same or similar conditions.

Keywords

De Novo Design
reinforcement learning
Generative models
cheminformatics
library design
AI

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