Scaffold-constrained molecular generation
arXiv:2009.07778 · doi:10.1021/acs.jcim.0c01015
Abstract
One of the major applications of generative models for drug Discovery targets the lead-optimization 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 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.
References in corpus (9)
- Automatic chemical design using a data-driven continuous representation of molecules
- Deep Reinforcement Learning for De-Novo Drug Design
- GuacaMol: Benchmarking Models for De Novo Molecular Design
- Junction Tree Variational Autoencoder for Molecular Graph Generation
- Deep learning for molecular design - a review of the state of the art
- MolGAN: An implicit generative model for small molecular graphs
- Optimization of Molecules via Deep Reinforcement Learning
- Population-based de novo molecule generation, using grammatical evolution
- Computer Assisted Composition with Recurrent Neural Networks