Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach
This work addresses the challenge of synthesizability in lipid design for drug delivery, offering a domain-specific incremental improvement over existing generative methods.
The paper tackles the problem of generating synthesizable ionizable lipids for mRNA delivery by proposing a Monte Carlo tree search-based generative model, which produces new lipids with available synthesis pathways using a policy network and specialized predictors.
Ionizable lipids are essential in developing lipid nanoparticles (LNPs) for effective messenger RNA (mRNA) delivery. While traditional methods for designing new ionizable lipids are typically time-consuming, deep generative models have emerged as a powerful solution, significantly accelerating the molecular discovery process. However, a practical challenge arises as the molecular structures generated can often be difficult or infeasible to synthesize. This project explores Monte Carlo tree search (MCTS)-based generative models for synthesizable ionizable lipids. Leveraging a synthetically accessible lipid building block dataset and two specialized predictors to guide the search through chemical space, we introduce a policy network guided MCTS generative model capable of producing new ionizable lipids with available synthesis pathways.