Deep learning bears promise for drug discovery, including advanced image analysis,
prediction of molecular structure and function, and automated generation of innovative …

This two‐part Review examines how automation has contributed to different aspects of
discovery in the chemical sciences. In this first part, we describe a classification for …

This paper is an overview of the most significant and impactful interpretation approaches of
quantitative structure–activity relationship (QSAR) models, their development, and …

The prediction of compound properties from chemical structure is a main task for machine
learning (ML) in medicinal chemistry. ML is often applied to large data sets in applications …

Graph neural networks are able to solve certain drug discovery tasks such as molecular
property prediction and de novo molecule generation. However, these models are …

Chemists can be skeptical in using deep learning (DL) in decision making, due to the lack of
interpretability in “black-box” models. Explainable artificial intelligence (XAI) is a branch of …

As the world struggles against current global pandemic of novel coronavirus disease
(COVID-19), it is challenging to trigger drug discovery efforts to search broad-spectrum …

Abstract Interpretation of QSAR models is useful to understand the complex nature of
biological or physicochemical processes, guide structural optimization or perform …

The ability to interpret the predictions made by quantitative structure–activity relationships
(QSARs) offers a number of advantages. While QSARs built using nonlinear modeling …

Feature attribution techniques are popular choices within the explainable artificial
intelligence toolbox, as they can help elucidate which parts of the provided inputs used by …