Multi-Objective Molecular Design in Constrained Latent Space

In recent times, molecular design has undergone significant advancements, particularly with the integration of artificial intelligence (AI) techniques for discovering molecules with various desired attributes. The use of generative models, especially variational autoencoders (VAEs), has proven to be a potent and efficient method. These models facilitate the rapid identification of new molecules that align with specific research goals. However, sequence-based generative models, like SMILES-based VAEs, often generate invalid molecules, presenting substantial challenges due to the inherent constraints in their latent spaces. To overcome this issue, we introduce a novel multi-objective molecular design approach that incorporates a corrector-based constraint handling technique. This technique employs a transformer as a corrector to convert invalid molecules into valid ones during the search process. Following correction, the latent space is segmented into distinct zones, organized via a spatial partition tree. Utilizing Monte Carlo tree search, we pinpoint the most promising zones for evolutionary-based sampling. Our method applies multi-objective molecular design within these constrained latent spaces. Our experimental results demonstrate that this approach markedly improves the quality of molecules generated from the latent space.