The problem of human trust is one of the most fundamental problems in applied artificial
intelligence in drug discovery. In silico models have been widely used to accelerate the …

Established molecular machine learning models process individual molecules as inputs to
predict their biological, chemical, or physical properties. However, such algorithms require …

ABSTRACT A reliable uncertainty estimator is a key ingredient in the successful use of
machine-learning force fields for predictive calculations. Important considerations are …

Relations among multiple entities are prevalent in many fields, and hypergraphs are widely
used to represent such group relations. Hence, machine learning on hypergraphs has …

With the increasingly more important role of machine learning (ML) models in chemical
research, the need for putting a level of confidence to the model predictions naturally arises …

Abstract Structure-based lead optimization is an open challenge in drug discovery, which is
still largely driven by hypotheses and depends on the experience of medicinal chemists …

Artificial intelligence-enhanced quantum mechanical method 1 (AIQM1) is a general-
purpose method that was shown to achieve high accuracy for many applications with a …

The bioactivity of compounds plays an important role in drug development and discovery.
Existing machine learning approaches have poor generalizability in bioactivity prediction …

Validation of prediction uncertainty (PU) is becoming an essential task for modern
computational chemistry. Designed to quantify the reliability of predictions in meteorology …

Determining sample similarity underlies many foundational principles in analytical
chemistry. For example, calibration models are unsuitable to predict outliers. Calibration …