3 papers
cs.LG2026
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…
cs.LG2026
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…
cs.LG2024
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…