ArticleACS omega2026
MolQuery: Prediction of Lipid Synthesizability Using Active Learning.
Article in ACS omega, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.