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

The application of Explainable Artificial Intelligence (XAI) in the field of chemistry has
garnered growing interest for its potential to justify the prediction of black-box machine …

Without any means of interpretation, neural networks that predict molecular properties and
bioactivities are merely black boxes. We will unravel these black boxes and will demonstrate …

Artificial intelligence (AI) is having a growing impact in many areas related to drug discovery.
However, it is still critical for their adoption by the medicinal chemistry community to achieve …

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a
driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted …

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 …

In recent years, machine learning (ML) has become a key enabling technology for the
sciences and industry. Especially through improvements in methodology, the availability of …

Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

The number of 'small'molecules that may be of interest to chemical biologists—chemical
space—is enormous, but the fraction that have ever been made is tiny. Most strategies are …

In this Viewpoint, we discuss the current progress in applications of machine learning (ML)
and artificial intelligence (AI) to meet the challenges of computational drug discovery. We …