953 citations · 3.5k across the 41 of their papers we have counts for
4 papers · 1 filter
Achieving Robustness to Aleatoric Uncertainty with Heteroscedastic Bayesian Optimisation
Ryan-Rhys Griffiths, Alexander A. Aldrick, Miguel Garcia-Ortegon +2
Bayesian optimisation is a sample-efficient search methodology that holds great promise for accelerating drug and materials discovery programs. A frequently-overlooked modelling co…
Predicting materials properties without crystal structure: Deep representation learning from stoichiometry
Rhys E. A. Goodall, Alpha A. Lee
Machine learning has the potential to accelerate materials discovery by accurately predicting materials properties at a low computational cost. However, the model inputs remain a k…
Validating the Validation: Reanalyzing a large-scale comparison of Deep Learning and Machine Learning models for bioactivity prediction
Matthew C. Robinson, Robert C. Glen, Alpha A. Lee
Machine learning methods may have the potential to significantly accelerate drug discovery. However, the increasing rate of new methodological approaches being published in the lit…
Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning
Yao Zhang, Alpha A. Lee
Predicting bioactivity and physical properties of small molecules is a central challenge in drug discovery. Deep learning is becoming the method of choice but studies to date focus…