用蒙特卡洛树搜索生成可合成的可电离脂质，加速mRNA递送载体研发。
Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach
- 基于蒙特卡洛树搜索和合成可行构建块库生成分子。
- 生成的脂质均具备已知合成路径，提升实际应用可行性。
- 适合药物设计与分子生成领域研究者参考。
可电离脂质在开发用于有效信使RNA（mRNA）递送的脂质纳米颗粒（LNPs）中至关重要。传统设计新可电离脂质的方法通常耗时较长，而深度生成模型已成为加速分子发现的有力工具。然而，实际挑战在于生成的分子结构往往难以或无法合成。本项目探索基于蒙特卡洛树搜索（MCTS）的生成模型，用于设计可合成的可电离脂质。利用一个合成可行的脂质构建块数据集以及两个专用预测器引导化学空间搜索，我们提出一种由策略网络指导的MCTS生成模型，能够产生具有明确合成路径的新可电离脂质。
原文摘要 · Abstract (English)
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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