ArticleJournal of chemical information and modeling2021
SolTranNet-A Machine Learning Tool for Fast Aqueous Solubility Prediction.
Article in Journal of chemical information and modeling, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Conformer-covariance diagnostics for aqueous solubility.Journal of computer-aided molecular design · 2026Article
- 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
- Physics-informed deep learning for molecular solubility prediction: integrating thermodynamic constraints with neural network architectures.Scientific reports · 2026Article
- 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
- 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
- A unified approach to inferring chemical compounds with the desired aqueous solubility.Journal of cheminformatics · 2025Article
- Article
- Development of Drug-Induced Gene Expression Ranking Analysis (DIGERA) and Its Application to Virtual Screening for Poly (ADP-Ribose) Polymerase 1 Inhibitor.International journal of molecular sciences · 2024Article
- Multimodal fused deep learning for drug property prediction: Integrating chemical language and molecular graph.Computational and structural biotechnology journal · 2024Article
- Effect of Data Quality and Data Quantity on the Estimation of Intrinsic Solubility: Analysis Based on a Single-Source Data Set.Molecular pharmaceutics · 2024Article
- What can attribution methods show us about chemical language models?Digital discovery · 2024Article
- Revisiting the Application of Machine Learning Approaches in Predicting Aqueous Solubility.ACS omega · 2024Article
- Predicting ADMET Properties from Molecule SMILE: A Bottom-Up Approach Using Attention-Based Graph Neural Networks.Pharmaceutics · 2024Article
- Development of scoring-assisted generative exploration (SAGE) and its application to dual inhibitor design for acetylcholinesterase and monoamine oxidase B.Journal of cheminformatics · 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
- Will we ever be able to accurately predict solubility?Scientific data · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
2 authors.
Funding
Abstract
While accurate prediction of aqueous solubility remains a challenge in drug discovery, machine learning (ML) approaches have become increasingly popular for this task. For instance, in the Second Challenge to Predict Aqueous Solubility (SC2), all groups utilized machine learning methods in their submissions. We present SolTranNet, a molecule attention transformer to predict aqueous solubility from a molecule's SMILES representation. Atypically, we demonstrate that larger models perform worse at this task, with SolTranNet's final architecture having 3,393 parameters while outperforming linear ML approaches. SolTranNet has a 3-fold scaffold split cross-validation root-mean-square error (RMSE) of 1.459 on AqSolDB and an RMSE of 1.711 on a withheld test set. We also demonstrate that, when used as a classifier to filter out insoluble compounds, SolTranNet achieves a sensitivity of 94.8% on the SC2 data set and is competitive with the other methods submitted to the competition. SolTranNet is distributed via pip, and its source code is available at https://github.com/gnina/SolTranNet.
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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.