Machine learning has shown enormous potential for computer-aided drug discovery. Here
we show how modern convolutional neural networks (CNNs) can be applied to structure-…

Rational compound design remains a challenging problem for both computational methods
and medicinal chemists. Computational generative methods have begun to show promising …

Generative models have increasingly been proposed as a solution to the molecular design
problem. However, it has proved challenging to control the design process or incorporate …

Diagnostic and prognostic models are increasingly important in medicine and inform many
clinical decisions. Recently, machine learning approaches have shown improvement over …

Modern machine learning models are complicated. Most of them rely on convoluted latent
representations of their input to issue a prediction. To achieve greater transparency than a …

Applications of machine learning are becoming increasingly common in medicine and
healthcare, enabling more accurate predictive models. However, this often comes at the cost of …

Estimating counterfactual outcomes over time has the potential to unlock personalized
healthcare by assisting decision-makers to answer ''what-iF'' questions. Existing causal inference …

Motivation An essential step in the development of virtual screening methods is the use of
established sets of actives and decoys for benchmarking and training. However, the decoy …

Deep reinforcement learning methods have been shown to be potentially powerful tools for
de novo design. Recurrent-neural-network-based techniques are the most widely used …

Evaluating the performance of machine learning models on diverse and underrepresented
subgroups is essential for ensuring fairness and reliability in real-world applications. However…