Mothra: Multiobjective de novo Molecular Generation Using Monte Carlo Tree Search.

ABSTRACT: In the field of drug discovery, identifying compounds that satisfy multiple criteria, such as target protein affinity, pharmacokinetics, and membrane permeability, is challenging because of the vast chemical space. Until now, multiobjective optimization via generative models has often involved linear combinations of different reward functions. Linear combinations solve multiobjective optimization problems by turning multiobjective optimization into a single-objective task and causing problems with weighting for each objective. Herein, we propose a scalable multiobjective molecular generative model developed using deep learning techniques. This model integrates the capabilities of recurrent neural networks for molecular generation and Pareto multiobjective Monte Carlo tree search to determi

SUBMITTER: Suzuki T 

PROVIDER: S-EPMC11481094 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature