Deep learning has disrupted nearly every field of research, including those of direct
importance to drug discovery, such as medicinal chemistry and pharmacology. This …

Developments in computational omics technologies have provided new means to access
the hidden diversity of natural products, unearthing new potential for drug discovery. In …

Background A fused method using a combination of multi-omics data enables a
comprehensive study of complex biological processes and highlights the interrelationship of …

Streamlined data-driven drug discovery remains challenging, especially in resource-limited
settings. We present ZairaChem, an artificial intelligence (AI)-and machine learning (ML) …

Quantitative activity and species source data of natural products (NPs) are important for drug
discovery, medicinal plant research, and microbial investigations. Activity values of NPs …

Various sources of information can be used to better understand and predict compound
activity and safety-related endpoints, including biological data such as gene expression and …

Through the representation of small molecule structures as numerical descriptors and the
exploitation of the similarity principle, chemoinformatics has made paramount contributions …

Identification of endocrine-disrupting chemicals (EDCs) is crucial in the reduction of human
health risks. However, it is hard to do so because of the complex mechanisms of the EDCs …

Only around 20% of the human proteome is considered to be druggable with small-molecule
antagonists. This leaves some of the most compelling therapeutic targets outside the reach …

The growing demand for sustainable solutions in agriculture, which are critical for crop
productivity and food quality in the face of climate change and the need to reduce …