Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

Highlights•Review of the state-of-the-art in uncertainty quantification in drug
design.•Examples from drug-design settings are provided.•Impact on decision making is …

Measurements of protein–ligand interactions have reproducibility limits due to experimental
errors. Any model based on such assays will consequentially have such unavoidable errors …

In the healthcare landscape, data science (DS) methods have emerged as indispensable
tools to harness real-world data (RWD) from various data sources such as electronic health …

This paper aims to provide a compact but accessible introduction to Conformal Predictors
(CP), a Machine Learning method with the distinguishing property of producing predictions …

In the context of bioactivity prediction, the question of how to calibrate a score produced by a
machine learning method into a probability of binding to a protein target is not yet …

The use of machine learning (ML) and artificial intelligence (AI) applications in medicine has
attracted a great deal of attention in the medical literature, but little is known about how to …

In the context of dementia care, Artificial Intelligence (AI) powered clinical decision support
systems have the potential to enhance diagnosis and management. However, the scope …

Quantifying the uncertainty of a predictive model output is of essential importance in learning
scenarios involving critical applications. As the learning task becomes more complex, so …

Prediction is the key objective of many machine learning applications. Accurate, reliable and
robust predictions are essential for optimal and fair decisions by downstream components of …