Leveraging Latent Evolutionary Optimization for Targeted Molecule Generation
This work addresses the problem of efficient molecule generation for drug discovery, offering a novel method that improves performance in property-targeting tasks, though it is incremental as it builds on existing evolutionary and VAE techniques.
The paper tackles lead optimization in drug design by introducing LEOMol, a generative framework that uses evolutionary algorithms to search the latent space of a VAE for targeted molecule generation, outperforming previous state-of-the-art models across all four sub-tasks in constrained molecule generation tasks.
Lead optimization is a pivotal task in the drug design phase within the drug discovery lifecycle. The primary objective is to refine the lead compound to meet specific molecular properties for progression to the subsequent phase of development. In this work, we present an innovative approach, Latent Evolutionary Optimization for Molecule Generation (LEOMol), a generative modeling framework for the efficient generation of optimized molecules. LEOMol leverages Evolutionary Algorithms, such as Genetic Algorithm and Differential Evolution, to search the latent space of a Variational AutoEncoder (VAE). This search facilitates the identification of the target molecule distribution within the latent space. Our approach consistently demonstrates superior performance compared to previous state-of-the-art models across a range of constrained molecule generation tasks, outperforming existing models in all four sub-tasks related to property targeting. Additionally, we suggest the importance of including toxicity in the evaluation of generative models. Furthermore, an ablation study underscores the improvements that our approach provides over gradient-based latent space optimization methods. This underscores the effectiveness and superiority of LEOMol in addressing the inherent challenges in constrained molecule generation while emphasizing its potential to propel advancements in drug discovery.