Publications
Detailed Information
MolFinder: an evolutionary algorithm for the global optimization of molecular properties and the extensive exploration of chemical space using SMILES
Cited 25 time in
Web of Science
Cited 29 time in Scopus
- Authors
- Issue Date
- 2021-03
- Publisher
- BMC
- Citation
- Journal of Cheminformatics, Vol.13 No.1, p. 24
- Abstract
- Here, we introduce a new molecule optimization method, MolFinder, based on an efficient global optimization algorithm, the conformational space annealing algorithm, and the SMILES representation. MolFinder finds diverse molecules with desired properties efficiently without any training and a large molecular database. Compared with recently proposed reinforcement-learning-based molecule optimization algorithms, MolFinder consistently outperforms in terms of both the optimization of a given target property and the generation of a set of diverse and novel molecules. The efficiency of MolFinder demonstrates that combinatorial optimization using the SMILES representation is a promising approach for molecule optimization, which has not been well investigated despite its simplicity. We believe that our results shed light on new possibilities for advances in molecule optimization methods.
- ISSN
- 1758-2946
- Files in This Item:
- There are no files associated with this item.
Related Researcher
- Graduate School of Convergence Science & Technology
- Dept. of Molecular and Biopharmaceutical Sciences
Item View & Download Count
Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.