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

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 …

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 …

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

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 …

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 …

The problem of human trust is one of the most fundamental problems in applied artificial
intelligence in drug discovery. In silico models have been widely used to accelerate the …

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 …

Graph neural networks (GNNs) have been widely used for predicting molecular properties,
especially for single molecules. However, when treating multi-component systems, GNNs …