A unified ML framework for solubility prediction across organic solvents
AD Vassileiou, MN Robertson, BG Wareham… - Digital …, 2023 - pubs.rsc.org
We report a single machine learning (ML)-based model to predict the solubility of drug/drug-
like compounds across 49 organic solvents, extensible to more. By adopting a cross-solvent …
like compounds across 49 organic solvents, extensible to more. By adopting a cross-solvent …
Machine learning with physicochemical relationships: solubility prediction in organic solvents and water
S Boobier, DRJ Hose, AJ Blacker… - Nature communications, 2020 - nature.com
Solubility prediction remains a critical challenge in drug development, synthetic route and
chemical process design, extraction and crystallisation. Here we report a successful …
chemical process design, extraction and crystallisation. Here we report a successful …
Uniting cheminformatics and chemical theory to predict the intrinsic aqueous solubility of crystalline druglike molecules
We present four models of solution free-energy prediction for druglike molecules utilizing
cheminformatics descriptors and theoretically calculated thermodynamic values. We make …
cheminformatics descriptors and theoretically calculated thermodynamic values. We make …
Blinded predictions and post hoc analysis of the second solubility challenge data: exploring training data and feature set selection for machine and deep learning …
JGM Conn, JW Carter, JJA Conn… - Journal of Chemical …, 2023 - ACS Publications
Accurate methods to predict solubility from molecular structure are highly sought after in the
chemical sciences. To assess the state of the art, the American Chemical Society organized …
chemical sciences. To assess the state of the art, the American Chemical Society organized …
Prediction of small-molecule compound solubility in organic solvents by machine learning algorithms
Z Ye, D Ouyang - Journal of cheminformatics, 2021 - Springer
Rapid solvent selection is of great significance in chemistry. However, solubility prediction
remains a crucial challenge. This study aimed to develop machine learning models that can …
remains a crucial challenge. This study aimed to develop machine learning models that can …
Predicting absolute aqueous solubility by applying a machine learning model for an artificially liquid-state as proxy for the solid-state
In this study, we use machine learning algorithms with QM-derived COSMO-RS descriptors,
along with Morgan fingerprints, to predict the absolute solubility of drug-like compounds. The …
along with Morgan fingerprints, to predict the absolute solubility of drug-like compounds. The …
Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules
Shallow machine learning methods have been applied to chemoinformatics problems with
some success. As more data becomes available and more complex problems are tackled …
some success. As more data becomes available and more complex problems are tackled …
[HTML][HTML] The openOCHEM consensus model is the best-performing open-source predictive model in the First EUOS/SLAS joint compound solubility challenge
The EUOS/SLAS challenge aimed to facilitate the development of reliable algorithms to
predict the aqueous solubility of small molecules using experimental data from 100 K …
predict the aqueous solubility of small molecules using experimental data from 100 K …
Pushing the limits of solubility prediction via quality-oriented data selection
Accurate prediction of the solubility of chemical substances in solvents remains a challenge.
The sparsity of high-quality solubility data is recognized as the biggest hurdle in the …
The sparsity of high-quality solubility data is recognized as the biggest hurdle in the …
Evaluation of deep learning architectures for aqueous solubility prediction
Determining the aqueous solubility of molecules is a vital step in many pharmaceutical,
environmental, and energy storage applications. Despite efforts made over decades, there …
environmental, and energy storage applications. Despite efforts made over decades, there …