MolFinder: An Efficient Global Molecular Property Optimization and Search Algorithm Using SMILES

19 October 2020, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


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.


Global Optimization Algorithm
Molecular Property Spaces
Molecular Property Optimization
Evolutionary Algorithms
Chemical Space Search


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