Abstract Machine learning (ML) promises to tackle the grand challenges in chemistry and
speed up the generation, improvement and/or ordering of research hypotheses. Despite the …

The use of nanomaterials in medicine depends largely on nanotoxicological evaluation in
order to ensure safe application on living organisms. Artificial intelligence (AI) and machine …

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 …

In modern drug discovery, the combination of chemoinformatics and quantitative structure–
activity relationship (QSAR) modeling has emerged as a formidable alliance, enabling …

Recent years have seen a substantial growth in the adoption of machine learning
approaches for the purposes of quantitative structure-activity relationship (QSAR) …

Academic and pharmaceutical industry research are both key for progresses in the field of
molecular machine learning. Despite common open research questions and long-term …

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 …

In chemoinformatics and medicinal chemistry, machine learning has evolved into an
important approach. In recent years, increasing computational resources and new deep …

Despite the fact that coronavirus disease 2019 (COVID-19), caused by severe acute
respiratory syndrome coronavirus 2 (SARS-CoV-2), has been disrupting human life and …

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 …