Scaffold-Constrained Molecular Generation - Sorbonne Université
Journal Articles Journal of Chemical Information and Modeling Year : 2020

Scaffold-Constrained Molecular Generation

Abstract

One of the major applications of generative models for drug discovery targets the leadoptimization phase. During the optimization of a lead series, it is common to have scaffold constraints imposed on the structure of the molecules designed. Without enforcing such constraints, the probability of generating molecules with the required scaffold is extremely low and hinders the practicality of generative models for de-novo drug design. To tackle this issue, we introduce a new algorithm to perform scaffold-constrained in-silico molecular design. We build on the well-known SMILES-based Recurrent Neural Network (RNN) generative model, with a modified sampling procedure to achieve scaffold-constrained generation. We directly benefit from the associated reinforcement learning methods, allowing to 1 arXiv:2009.07778v3 [q-bio.QM] 5 Oct 2020 design molecules optimized for different properties while exploring only the relevant chemical space. We showcase the method's ability to perform scaffold-constrained generation on various tasks: designing novel molecules around scaffolds extracted from SureChEMBL chemical series, generating novel active molecules on the Dopamine Receptor D2 (DRD2) target, and finally, designing predicted actives on the MMP-12 series, an industrial lead-optimization project.
Fichier principal
Vignette du fichier
Langevin et al. - 2020 - Scaffold-Constrained Molecular Generation.pdf (1.28 Mo) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-03375168 , version 1 (12-10-2021)

Identifiers

Cite

Maxime Langevin, Hervé Minoux, Maximilien Levesque, Marc Bianciotto. Scaffold-Constrained Molecular Generation. Journal of Chemical Information and Modeling, 2020, 60 (12), pp.5637-5646. ⟨10.1021/acs.jcim.0c01015⟩. ⟨hal-03375168⟩
49 View
163 Download

Altmetric

Share

More