ArticleFrontiers in oncology2020
Improved Prediction of Aqueous Solubility of Novel Compounds by Going Deeper With Deep Learning.
Article in Frontiers in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- Data-Driven Critical Evaluation of the General Solubility Equation.Journal of chemical information and modeling · 2026Article
- Oral bioavailability property prediction based on task similarity transfer learning.Molecular diversity · 2026Article
- Perspectives Towards AI and ML.Advances in biochemical engineering/biotechnology · 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
- Artificial intelligence modelling of tyrosine kinase inhibitors at risk of malabsorption and bioavailability-enhancing strategies.British journal of clinical pharmacology · 2025Article
- Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development.Drug development research · 2025Review
- 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
- Refined ADME Profiles for ATC Drug Classes.Pharmaceutics · 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
- FormulationBCS: A Machine Learning Platform Based on Diverse Molecular Representations for Biopharmaceutical Classification System (BCS) Class Prediction.Molecular pharmaceutics · 2025Article
- MolGraph: a Python package for the implementation of molecular graphs and graph neural networks with TensorFlow and Keras.Journal of computer-aided molecular design · 2024Article
- Towards the prediction of drug solubility in binary solvent mixtures at various temperatures using machine learning.Journal of cheminformatics · 2024Article
- PharmaBench: Enhancing ADMET benchmarks with large language models.Scientific data · 2024Article
- Revisiting the Application of Machine Learning Approaches in Predicting Aqueous Solubility.ACS omega · 2024Article
- Predicting small molecules solubility on endpoint devices using deep ensemble neural networks.Digital discovery · 2024Article
- Designing solvent systems using self-evolving solubility databases and graph neural networks.Chemical science · 2024Article
- SolPredictor: Predicting Solubility with Residual Gated Graph Neural Network.International journal of molecular sciences · 2024Article
- Evaluating uncertainty-based active learning for accelerating the generalization of molecular property prediction.Journal of cheminformatics · 2023Article
- Multi-channel GCN ensembled machine learning model for molecular aqueous solubility prediction on a clean dataset.Molecular diversity · 2023Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aqueous solubility is an important physicochemical property of compounds in anti-cancer drug discovery. Artificial intelligence solubility prediction tools have scored impressive performances by employing regression, machine learning, and deep learning methods. The reported performances vary significantly partly because of the different datasets used. Solubility prediction on novel compounds needs to be improved, which may be achieved by going deeper with deep learning. We constructed deeper-net models of ~20-layer modified ResNet convolutional neural network architecture, which were trained and tested with 9,943 compounds encoded by molecular fingerprints. Retrospectively tested by 62 recently-published novel compounds, one deeper-net model outperformed four established tools, shallow-net models, and four human experts. Deeper-net models also outperformed others in predicting the solubility values of a series of novel compounds newly-synthesized for anti-cancer drug discovery. Solubility prediction may be improved by going deeper with deep learning. Our deeper-net models are accessible at http://www.npbdb.net/solubility/index.jsp.
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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.