ArticleACS omega2024
Predictions of Colloidal Molecular Aggregation Using AI/ML Models.
Article in ACS omega, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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Who cites it
3 citing papers in PubMed.
- Physicochemical Aspects of Mixed Micelle Formation Between Amphiphilic Drugs and Surfactants.International journal of molecular sciences · 2026Review
- Discovery and optimization of a guanylhydrazone-based small molecule to replace bFGF for cell culture applications.Biochemistry and biophysics reports · 2025Article
- Physics-Based Solubility Prediction for Organic Molecules.Chemical reviews · 2025Review
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To facilitate the triage of hits from small molecule screens, we have used various AI/ML techniques and experimentally observed data sets to build models aimed at predicting colloidal aggregation of small organic molecules in aqueous solution. We have found that Naïve Bayesian and deep neural networks outperform logistic regression, recursive partitioning tree, support vector machine, and random forest techniques by having the lowest balanced error rate (BER) for the test set. Derived predictive classification models consistently and successfully discriminated aggregator molecules from nonaggregator hits. An analysis of molecular descriptors in favor of colloidal aggregation confirms previous observations (hydrophobicity, molecular weight, and solubility) in addition to undescribed molecular descriptors such as the fraction of sp
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Registered trials
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