# MolQuery: Prediction of Lipid Synthesizability Using Active Learning

## TL;DR

MolQuery is a new tool that uses active learning to accurately predict whether lipid molecules used in mRNA delivery can be synthesized.

## Contribution

MolQuery introduces an active learning-based pipeline to improve lipid synthesizability prediction for mRNA delivery systems.

## Key findings

- MolQuery improves synthesizability predictions using active learning with small datasets.
- The tool provides highly accurate predictions for lipid molecules in LNP systems.
- It enables efficient filtering of synthetic lipid datasets for practical molecular design.

## Abstract

In the field of molecular
design, Generative Artificial Intelligence
(GenAI) has the potential to create extensive synthetic data sets
encompassing a wide range of chemical properties. However, the practical
application of these data sets is often constrained by the synthesizability
of the molecules within. To address this, it is essential to develop
a robust platform for assessing synthesizability, which is crucial
for constructing effective GenAI-based models for molecular systems.
Here, we introduces MolQuery, a comprehensive pipeline that integrates
active learning (AL) to improve the accuracy of chemical synthesizability
predictions for lipid molecules designed for mRNA delivery via lipid
nanoparticles (LNPs). By leveraging AL, MolQuery efficiently trains
machine learning models using small data sets which greatly improves
upon current solutions for this tasks. Our results demonstrate that
MolQuery produces highly accurate predictions of lipid synthesizability,
making it a valuable tool for filtering synthetic LNP data sets.