ArticleJournal of chemical information and modeling2013
Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules.
Article in Journal of chemical information and modeling, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 166 papers.
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Who cites it
166 citing papers in PubMed.
- Data-Driven Critical Evaluation of the General Solubility Equation.Journal of chemical information and modeling · 2026Article
- Deciphering Molecular and Solvent Effects on Aqueous and Organic Solubility through Interpretable Machine Learning Approaches.ACS omega · 2026Article
- Prediction of intrinsic solubility for drug-like organic compounds using automated network optimizer (ANO) for physicochemical feature and hyperparameter optimization.Journal of cheminformatics · 2026Article
- Synthesis and evaluation of 14β-acyl substituted 17-cyclopropylmethyl-7,8-dihydromorphinone derivatives: mixed partial agonists at mu opioid and nociception/orphanin FQ peptide receptors.RSC medicinal chemistry · 2026Article
- Multimodal system for automated medical documentation and clinical decision support integrating contact center solutions.Scientific reports · 2026Article
- Graph-based transformer to predict the octanol-water partition coefficient.Journal of cheminformatics · 2026Article
- Multiscale modeling of graphene and carbon nanostructures: advances in atomistic, coarse-grained, and machine learning approaches.RSC advances · 2026Review
- Redefining drug discovery: AI-driven strategies for efficient cancer therapeutics.Frontiers in oncology · 2026Review
- The Evolution of Machine Learning in Medicinal Chemistry: A Comprehensive Bibliometric Analysis.Current neuropharmacology · 2026Review
- Rethinking the AI Paradigm for Solubility Prediction of Drug‑Like Compounds with Dual-Perspective Modeling and Experimental Validation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Improved estimation of intrinsic solubility of drug-like molecules through multi-task graph transformer.Journal of cheminformatics · 2025Article
- Advancing Aqueous Solubility Prediction: A Machine Learning Approach for Organic Compounds Using a Curated Data Set.Journal of chemical information and modeling · 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
- MEN: leveraging explainable multimodal encoding network for precision prediction of CYP450 inhibitors.Scientific reports · 2025Article
- Chemically Informed Deep Learning for Interpretable Radical Reaction Prediction.Journal of chemical information and modeling · 2025Article
- hERGAT: predicting hERG blockers using graph attention mechanism through atom- and molecule-level interaction analyses.Journal of cheminformatics · 2025Article
- Article
- Role of Artificial Intelligence in Drug Discovery to Revolutionize the Pharmaceutical Industry: Resources, Methods and Applications.Recent patents on biotechnology · 2025Review
- Effect of Data Quality and Data Quantity on the Estimation of Intrinsic Solubility: Analysis Based on a Single-Source Data Set.Molecular pharmaceutics · 2024Article
106 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
Funding
Abstract
Shallow machine learning methods have been applied to chemoinformatics problems with some success. As more data becomes available and more complex problems are tackled, deep machine learning methods may also become useful. Here, we present a brief overview of deep learning methods and show in particular how recursive neural network approaches can be applied to the problem of predicting molecular properties. However, molecules are typically described by undirected cyclic graphs, while recursive approaches typically use directed acyclic graphs. Thus, we develop methods to address this discrepancy, essentially by considering an ensemble of recursive neural networks associated with all possible vertex-centered acyclic orientations of the molecular graph. One advantage of this approach is that it relies only minimally on the identification of suitable molecular descriptors because suitable representations are learned automatically from the data. Several variants of this approach are applied to the problem of predicting aqueous solubility and tested on four benchmark data sets. Experimental results show that the performance of the deep learning methods matches or exceeds the performance of other state-of-the-art methods according to several evaluation metrics and expose the fundamental limitations arising from training sets that are too small or too noisy. A Web-based predictor, AquaSol, is available online through the ChemDB portal ( cdb.ics.uci.edu ) together with additional material.
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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.