Over the past decade, the amount of biomedical data available has grown at unprecedented
rates. Increased automation technology and larger data volumes have encouraged the use …

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

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 …

Artificial intelligence (AI)-aided drug design has demonstrated unprecedented effects on
modern drug discovery, but there is still an urgent need for user-friendly interfaces that …

Artificial intelligence (AI) is a rapidly growing discipline in the field of chemical toxicology.
Herein, we provide a broad overview of research presented at the Fall 2022 American …

Traditional drug development heavily relies on human-derived rational and effort to detect
the functional mechanisms of diseases, identify druggable targets, and design lead …

Chemical and biological data are the cornerstone of modern drug discovery programs.
Finding qualitative yet better quantitative relationships between chemical structures and …

The integration of artificial intelligence (AI) into an evidence-based modern drug discovery
platform will likely result in revolutionary advancement. Although modern medicine remains …

Difficulties in interpreting machine learning (ML) models and their predictions limit the
practical applicability of and confidence in ML in pharmaceutical research. There is a need …

The purpose of this study was to discuss how artificial intelligence (AI) methods have
affected the field of drug development. It looks at how AI models and data resources are …