Dataset Information
Francoeur2021 - SolTranNet–A Machine Learning Tool for Fast Aqueous Solubility Prediction
Ontology highlight
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). Implementation of this model code by Ersilia is available here:
https://github.com/ersilia-os/eos6oli
SUBMITTER: Zainab Ashimiyu-Abdusalam
PROVIDER: MODEL2406030002 | biostudies-other |
SECONDARY ACCESSION(S): 34038123
REPOSITORIES: biostudies-other
Publications
SolTranNet-A Machine Learning Tool for Fast Aqueous Solubility Prediction.
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]
