Toxicity prediction is a critical step in the drug discovery process that helps identify and
prioritize compounds with the greatest potential for safe and effective use in humans, while …

Highlights•Drug discovery has been advanced to a big data era with a large amount of
public data sources available.•Ten V features (volume, velocity, variety, veracity, validity …

The identification of therapeutic biomarkers predictive of drug response is crucial in
personalized medicine. A number of computational models to predict response of anti …

There is a growing public concern about the lack of reproducibility of experimental data
published in peer-reviewed scientific literature. Herein, we review the most recent alerts …

Over the past decade we have witnessed the increasing sophistication of machine learning
algorithms applied in daily use from internet searches, voice recognition, social network …

Idiosyncratic drug-induced liver injury (DILI) is a rare disease that develops independently of
drug dose, route, or duration of administration. Furthermore, idiosyncratic DILI is not a single …

The modulation of PPIs by low molecular weight chemical compounds, particularly by orally
bioavailable molecules, would be very valuable in numerous disease indications. However …

Abstract Structure–activity relationship modelling is frequently used in the early stage of drug
discovery to assess the activity of a compound on one or several targets, and can also be …

There are numerous small molecular compounds around us to affect our health, such as
drugs, pesticides, food additives, industrial chemicals, and environmental pollutants. Over …

The median lethal dose for rodent oral acute toxicity (LD50) is a standard piece of
information required to categorize chemicals in terms of the potential hazard posed to …