Work

BioMolFormer: A Novel Approach to Molecular Generation with SELFIES and Monte Carlo Tree Search

Year: 2024

Type: conference-paper

Abstract: In this study, we present BioMolFormer, a novel model to enhance the generation and optimization of drug candidates, especially for their binding affinity to target proteins. BioMolFormer integrates Transformer networks and MCTS to improve the power of exploring chemical space by deploying robust molecular representation based on using SELFIES. Results: Experiments show that BioMolFormer excels traditional methods in both molecular validity and diversity, with 100% chemical validities and an average docking score of 9.9; they highlighted the promise of dramatically improving drug discovery with this model.

DOI: 10.1145/3715931.3715941

Authors: Yuntao Zhang, Lei Liu, Yunting Liu, Fengwei Wang, Leqi Shi

Institutions: The University of Sydney, New York University, Sun Yat-sen University, University of Wisconsin–Madison

Language: English

Topic: Computational Drug Discovery Methods

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

OpenAlex ID: W4409181594

FWCI: 2.119

Cites: 9

Cited by: 3

Related to: 10