953 citations · 3.5k across the 41 of their papers we have counts for
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cs.LG2019★ 79 cited
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…
cs.LG2019★ 165 cited
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…
cs.LG2018★ 56 cited
Energy-entropy competition and the effectiveness of stochastic gradient descent in machine learning
Yao Zhang, Andrew M. Saxe, Madhu S. Advani +1
Finding parameters that minimise a loss function is at the core of many machine learning methods. The Stochastic Gradient Descent algorithm is widely used and delivers state of the…