ArticleACS omega2022
Evaluation of Deep Learning Architectures for Aqueous Solubility Prediction.
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 31 papers.
What it found
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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.
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Who cites it
31 citing papers in PubMed.
- Deciphering Molecular and Solvent Effects on Aqueous and Organic Solubility through Interpretable Machine Learning Approaches.ACS omega · 2026Article
- When Does Machine Learning Add Value over Theory? Predicting API Solubility in Binary Mixtures with COSMO-RS and DOOIT2 Across Diverse and Homogeneous Systems.Molecules (Basel, Switzerland) · 2026Article
- Out-of-distribution evaluation of active learning pipelines for molecular property prediction.RSC advances · 2026Article
- Article
- 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
- Computational and experimental design of L-amino acid-based alternatives to ketorolac.Scientific reports · 2025Article
- Duality of Simplicity and Accuracy in QSPR: A Machine Learning Framework for Predicting Solubility of Selected Pharmaceutical Acids in Deep Eutectic Solvents.Molecules (Basel, Switzerland) · 2025Article
- Artificial intelligence modelling of tyrosine kinase inhibitors at risk of malabsorption and bioavailability-enhancing strategies.British journal of clinical pharmacology · 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
- Natural Language Processing Methods for the Study of Protein-Ligand Interactions.Journal of chemical information and modeling · 2025Review
- TCN-Transformer Deep Network with Random Forest for Prediction of the Chemical Synthetic Ammonia Process.ACS omega · 2025Article
- Article
- Effect of Data Quality and Data Quantity on the Estimation of Intrinsic Solubility: Analysis Based on a Single-Source Data Set.Molecular pharmaceutics · 2024Article
- Revisiting the Application of Machine Learning Approaches in Predicting Aqueous Solubility.ACS omega · 2024Article
- Correction: Predicting small molecules solubility on endpoint devices using deep ensemble neural networks.Digital discovery · 2024Article
- Predicting small molecules solubility on endpoint devices using deep ensemble neural networks.Digital discovery · 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
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Determining the aqueous solubility of molecules is a vital step in many pharmaceutical, environmental, and energy storage applications. Despite efforts made over decades, there are still challenges associated with developing a solubility prediction model with satisfactory accuracy for many of these applications. The goals of this study are to assess current deep learning methods for solubility prediction, develop a general model capable of predicting the solubility of a broad range of organic molecules, and to understand the impact of data properties, molecular representation, and modeling architecture on predictive performance. Using the largest currently available solubility data set, we implement deep learning-based models to predict solubility from the molecular structure and explore several different molecular representations including molecular descriptors, simplified molecular-input line-entry system strings, molecular graphs, and three-dimensional atomic coordinates using four different neural network architectures-fully connected neural networks, recurrent neural networks, graph neural networks (GNNs), and SchNet. We find that models using molecular descriptors achieve the best performance, with GNN models also achieving good performance. We perform extensive error analysis to understand the molecular properties that influence model performance, perform feature analysis to understand which information about the molecular structure is most valuable for prediction, and perform a transfer learning and data size study to understand the impact of data availability on model performance.
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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.