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arXiv · 2412.00807

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach

Jingyi Zhao·Yuxuan Ou·Austin Tripp·Morteza Rasoulianboroujeni·José Miguel Hernández-Lobato
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Abstract

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.

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Jingyi Zhao, Yuxuan Ou, Austin Tripp, Morteza Rasoulianboroujeni, José Miguel Hernández-Lobato. 2024-12-01. Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach. https://arxiv.org/abs/2412.00807

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