ArticleJournal of chemical information and modeling2014
Uniting cheminformatics and chemical theory to predict the intrinsic aqueous solubility of crystalline druglike molecules.
Article in Journal of chemical information and modeling, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Application of high-precision solubility prediction models in the assisted design of drug-like compounds.Molecular diversity · 2026Article
- Multiscale modeling of graphene and carbon nanostructures: advances in atomistic, coarse-grained, and machine learning approaches.RSC advances · 2026Review
- Predicting drug solubility in binary solvent mixtures using graph convolutional networks: a comprehensive deep learning approach.Scientific reports · 2025Article
- 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
- Will we ever be able to accurately predict solubility?Scientific data · 2024Article
- Targeted Protein Degradation: Advances, Challenges, and Prospects for Computational Methods.Journal of chemical information and modeling · 2023Review
- 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
- Article
- Prediction of small-molecule compound solubility in organic solvents by machine learning algorithms.Journal of cheminformatics · 2021Article
- Structure-Based Virtual Screening and Molecular Dynamics Simulation to Identify Potential SARS-CoV-2 Spike Receptor Inhibitors from Natural Compound Database.Pharmaceutical chemistry journal · 2021Article
- Identifying diverse metal oxide nanomaterials with lethal effects on embryonic zebrafish using machine learning.Beilstein journal of nanotechnology · 2021Article
- Pruned Machine Learning Models to Predict Aqueous Solubility.ACS omega · 2020Article
- Rethinking drug design in the artificial intelligence era.Nature reviews. Drug discovery · 2020Review
- Three machine learning models for the 2019 Solubility Challenge.ADMET & DMPK · 2020Article
- AqSolDB, a curated reference set of aqueous solubility and 2D descriptors for a diverse set of compounds.Scientific data · 2019Article
- The influence of solid state information and descriptor selection on statistical models of temperature dependent aqueous solubility.Journal of cheminformatics · 2018Article
- Computational prediction of drug solubility in water-based systems: Qualitative and quantitative approaches used in the current drug discovery and development setting.International journal of pharmaceutics · 2018Review
- Can human experts predict solubility better than computers?Journal of cheminformatics · 2017Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
We present four models of solution free-energy prediction for druglike molecules utilizing cheminformatics descriptors and theoretically calculated thermodynamic values. We make predictions of solution free energy using physics-based theory alone and using machine learning/quantitative structure-property relationship (QSPR) models. We also develop machine learning models where the theoretical energies and cheminformatics descriptors are used as combined input. These models are used to predict solvation free energy. While direct theoretical calculation does not give accurate results in this approach, machine learning is able to give predictions with a root mean squared error (RMSE) of ~1.1 log S units in a 10-fold cross-validation for our Drug-Like-Solubility-100 (DLS-100) dataset of 100 druglike molecules. We find that a model built using energy terms from our theoretical methodology as descriptors is marginally less predictive than one built on Chemistry Development Kit (CDK) descriptors. Combining both sets of descriptors allows a further but very modest improvement in the predictions. However, in some cases, this is a statistically significant enhancement. These results suggest that there is little complementarity between the chemical information provided by these two sets of descriptors, despite their different sources and methods of calculation. Our machine learning models are also able to predict the well-known Solubility Challenge dataset with an RMSE value of 0.9-1.0 log S units.
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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.