Genetic Algorithm for Constrained Molecular Inverse Design
This work addresses a domain-specific challenge in computational chemistry for drug discovery, but it is incremental as it builds on existing genetic algorithm methods.
The authors tackled the problem of optimizing pharmacological properties while maintaining molecular substructure in molecular inverse design using a genetic algorithm, and their proposed constrained algorithm successfully produced valid molecules and effectively found ones that satisfy specific properties under structural constraints.
A genetic algorithm is suitable for exploring large search spaces as it finds an approximate solution. Because of this advantage, genetic algorithm is effective in exploring vast and unknown space such as molecular search space. Though the algorithm is suitable for searching vast chemical space, it is difficult to optimize pharmacological properties while maintaining molecular substructure. To solve this issue, we introduce a genetic algorithm featuring a constrained molecular inverse design. The proposed algorithm successfully produces valid molecules for crossover and mutation. Furthermore, it optimizes specific properties while adhering to structural constraints using a two-phase optimization. Experiments prove that our algorithm effectively finds molecules that satisfy specific properties while maintaining structural constraints.