DrugSynthMC: An Atom-Based Generation of Drug-like Molecules with Monte Carlo Search.

ABSTRACT: A growing number of deep learning (DL) methodologies have recently been developed to design novel compounds and expand the chemical space within virtual libraries. Most of these neural network approaches design molecules to specifically bind a target based on its structural information and/or knowledge of previously identified binders. Fewer attempts have been made to develop approaches for de novo design of virtual libraries, as synthesizability of generated molecules remains a challenge. In this work, we developed a new Monte Carlo Search (MCS) algorithm, DrugSynthMC (Drug Synthesis using Monte Carlo), in conjunction with DL and statistical-based priors to generate thousands of interpretable chemical structures and novel drug-like molecules per second. DrugSynthMC produces

SUBMITTER: Roucairol M 

PROVIDER: S-EPMC11423341 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature