ArticleACS omega2022
Novel Solubility Prediction Models: Molecular Fingerprints and Physicochemical Features vs Graph Convolutional Neural Networks.
Article in ACS omega, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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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.
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Who cites it
23 citing papers in PubMed.
- Machine learning-enhanced QSAR modeling for predicting drug efficacy against the RET V804M kinase domain mutation.Molecular diversity · 2026Article
- Multi-Solvent Graph Neural Network for Reduction Potential Prediction Across the Chemical Space.Journal of chemical information and modeling · 2026Article
- Leveraging Artificial Intelligence for Advancements in Liquid Dosage Formulations in the Pharmaceutical Industry.Therapeutic innovation & regulatory science · 2025Review
- Data-driven organic solubility prediction at the limit of aleatoric uncertainty.Nature communications · 2025Article
- Physics-Based Solubility Prediction for Organic Molecules.Chemical reviews · 2025Review
- BigSolDB 2.0, dataset of solubility values for organic compounds in different solvents at various temperatures.Scientific data · 2025Article
- Machine Learning-Based Prediction of Drug Solubility in Lipidic Environments: The Sol_ME Tool for Optimizing Lipid-Based Formulations with a Preliminary Apalutamide Case Study.AAPS PharmSciTech · 2025Article
- Predictive Machine Learning Models for Olfaction.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Artificial intelligence in antidiabetic drug discovery: The advances in QSAR and the prediction of α-glucosidase inhibitors.Computational and structural biotechnology journal · 2024Review
- Effect of Data Quality and Data Quantity on the Estimation of Intrinsic Solubility: Analysis Based on a Single-Source Data Set.Molecular pharmaceutics · 2024Article
- The Artificial Intelligence-Powered New Era in Pharmaceutical Research and Development: A Review.AAPS PharmSciTech · 2024Review
- Prediction method of pharmacokinetic parameters of small molecule drugs based on GCN network model.Journal of molecular modeling · 2024Article
- Experimental and Theoretical Insights into the Intermolecular Interactions in Saturated Systems of Dapsone in Conventional and Deep Eutectic Solvents.Molecules (Basel, Switzerland) · 2024Article
- Will we ever be able to accurately predict solubility?Scientific data · 2024Article
- Article
- Designing solvent systems using self-evolving solubility databases and graph neural networks.Chemical science · 2024Article
- Intermolecular Interactions as a Measure of Dapsone Solubility in Neat Solvents and Binary Solvent Mixtures.Materials (Basel, Switzerland) · 2023Article
- Extended study on atomic featurization in graph neural networks for molecular property prediction.Journal of cheminformatics · 2023Article
- Review
- Blinded Predictions and Post Hoc Analysis of the Second Solubility Challenge Data: Exploring Training Data and Feature Set Selection for Machine and Deep Learning Models.Journal of chemical information and modeling · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting both accurate and reliable solubility values has long been a crucial but challenging task. In this work, surrogated model-based methods were developed to accurately predict the solubility of two molecules (solute and solvent) through machine learning and deep learning. The current study employed two methods: (1) converting molecules into molecular fingerprints and adding optimal physicochemical properties as descriptors and (2) using graph convolutional network (GCN) models to convert molecules into a graph representation and deal with prediction tasks. Then, two prediction tasks were conducted with each method: (1) the solubility value (regression) and (2) the solubility class (classification). The fingerprint-based method clearly demonstrates that high performance is possible by adding simple but significant physicochemical descriptors to molecular fingerprints, while the GCN method shows that it is possible to predict various properties of chemical compounds with relatively simplified features from the graph representation. The developed methodologies provide a comprehensive understanding of constructing a proper model for predicting solubility and can be employed to find suitable solutes and solvents.
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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.