4 papers
ToxLens: A Reproducible Graph-Learning Framework for Leakage-Aware, Uncertainty-Calibrated Molecular Toxicity Prediction
Magnus H. Strømme, Alex G. C. de Sá, David B. Ascher
Molecular toxicity prediction is increasingly used to prioritise compounds before experimental testing, but conventional benchmark performance can overstate practical utility when…
DPD-Cancer: Explainable Graph-Based Deep Learning for Small Molecule Anti-Cancer Activity Prediction
Magnus H. Strømme, Alex G. C. de Sá, David B. Ascher
DPD-Cancer is a graph-attention deep learning framework for predicting small-molecule DPD-Cancer is a graph-attention deep learning framework for predicting small-molecule anti-can…
Auto-ADMET: An Effective and Interpretable AutoML Method for Chemical ADMET Property Prediction
Alex G. C. de Sá, David B. Ascher
Machine learning (ML) has been playing important roles in drug discovery in the past years by providing (pre-)screening tools for prioritising chemical compounds to pass through we…
Towards Evolutionary-based Automated Machine Learning for Small Molecule Pharmacokinetic Prediction
Alex G. C. de Sá, David B. Ascher
Machine learning (ML) is revolutionising drug discovery by expediting the prediction of small molecule properties essential for developing new drugs. These properties -- including…