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Francoeur2021 - SolTranNet–A Machine Learning Tool for Fast Aqueous Solubility Prediction

ABSTRACT: Fast aqueous solubility prediction based on the Molecule Attention Transformer (MAT). The authors used AqSolDB to fine-tune the MAT network to solubility prediction, achieving competitive scores in the Second Challenge to Predict Aqueous Solubility (SC2). Model Type: Predictive machine learning model. Model Relevance: Predicts log of the solubility of small molecules. Model Encoded by: Miquel Duran Frigola (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos6oli

SUBMITTER: Zainab Ashimiyu-Abdusalam

PROVIDER: MODEL2406030002 | BioModels | 2024-06-03

REPOSITORIES: BioModels

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SolTranNet-A Machine Learning Tool for Fast Aqueous Solubility Prediction.

Francoeur Paul G PG Koes David R DR

Journal of chemical information and modeling 20210526 6

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 ...[more]

PMID: 34038123

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