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.
@article{arxiv.2412.00807,
title = {Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach},
author = {Jingyi Zhao and Yuxuan Ou and Austin Tripp and Morteza Rasoulianboroujeni and José Miguel Hernández-Lobato},
journal= {arXiv preprint arXiv:2412.00807},
year = {2024}
}