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 the ever-evolving field of drug discovery, the integration of Artificial Intelligence (AI) and
Machine Learning (ML) with cheminformatics has proven to be a powerful combination …

Background Explainable artificial intelligence (XAI) methods have shown increasing
applicability in chemistry. In this context, visualization techniques can highlight regions of a …

With the increased availability of chemical data in public databases, innovative techniques
and algorithms have emerged for the analysis, exploration, visualization, and extraction of …

ABSTRACT Introduction The application of Artificial Intelligence (AI) to predictive toxicology
is rapidly increasing, particularly aiming to develop non-testing methods that effectively …

This systematic literature review employs the Preferred Reporting Items for Systematic
Reviews and Meta-Analyses (PRISMA) methodology to investigate recent applications of …

While high lipophilicity tends to improve potency, its effects on pharmacokinetics (PK) are
complex and often unfavorable. To predict clinical PK in early drug discovery, we built …

The global cost-benefit analysis of pesticide use during the last 30 years has been
characterized by a significant increase during the period from 1990 to 2007 followed by a …

In data science and visualization, dimensionality reduction techniques have been
extensively employed for exploring large datasets. These techniques involve the …

Explainable machine learning is increasingly used in drug discovery to help rationalize
compound property predictions. Feature attribution techniques are popular choices to …