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

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhao, Jingyi, Ou, Yuxuan, Tripp, Austin, Rasoulianboroujeni, Morteza, Hernández-Lobato, José Miguel
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence Biomolecules Quantitative Methods
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

_version_	1866910722704801792
author	Zhao, Jingyi Ou, Yuxuan Tripp, Austin Rasoulianboroujeni, Morteza Hernández-Lobato, José Miguel
author_facet	Zhao, Jingyi Ou, Yuxuan Tripp, Austin Rasoulianboroujeni, Morteza Hernández-Lobato, José Miguel
contents	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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2412_00807
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Zhao, Jingyi Ou, Yuxuan Tripp, Austin Rasoulianboroujeni, Morteza Hernández-Lobato, José Miguel Machine Learning Artificial Intelligence Biomolecules Quantitative Methods 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.
title	Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach
topic	Machine Learning Artificial Intelligence Biomolecules Quantitative Methods
url	https://arxiv.org/abs/2412.00807