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 …

Recent progress in engineering highly promising biocatalysts has increasingly involved
machine learning methods. These methods leverage existing experimental and simulation …

Machine learning (ML) models to predict the toxicity of small molecules have garnered great
attention and have become widely used in recent years. Computational toxicity prediction is …

Predictive modeling for toxicity can help reduce risks in a range of applications and
potentially serve as the basis for regulatory decisions. However, the utility of these …

Protein–protein interactions (PPIs) play a significant role in nearly all cellular and biological
activities. Data-driven machine learning models have demonstrated great power in PPIs …

Unpredicted human organ level toxicity remains one of the major reasons for drug clinical
failure. There is a critical need for cost-efficient strategies in the early stages of drug …

Pain is a significant global health issue, and the current treatment options for pain
management have limitations in terms of effectiveness, side effects, and potential for …

The development of new drugs is time-consuming and expensive, and as such, accurately
predicting the potential toxicity of a drug candidate is crucial in ensuring its safety and …

Adverse drug reactions (ADRs) and drug-induced toxicity are major challenges in drug
discovery, threatening patient safety and dramatically increasing healthcare expenditures …

The development of new, effective, and safe drugs to treat cancer remains a challenging and
time-consuming task due to limited hit rates, restraining subsequent development efforts …