Recently, using artificial intelligence (AI) in drug discovery has received much attention
since it significantly shortens the time and cost of developing new drugs. Deep learning (DL) …

Computing is a critical driving force in the development of human civilization. In recent years,
we have witnessed the emergence of intelligent computing, a new computing paradigm that …

Accurate prediction of binding affinities from protein–ligand atomic coordinates remains a
major challenge in early stages of drug discovery. Using modular message passing graph …

In drug design, compound potency prediction is a popular machine learning application.
Graph neural networks (GNNs) predict ligand affinity from graph representations of protein …

In drug discovery, drug-target interaction (DTI) plays a crucial role. Identifying DTI in a wet-
lab experiment is time-consuming, labor-intensive, and costly. Using reliable computational …

The rapid evolution of artificial intelligence in drug discovery encounters challenges with
generalization and extensive training, yet Large Language Models (LLMs) offer promise in …

Abstract Background Traditional Chinese medicine (TCM), as a significant part of the global
pharmaceutical science, the abundant molecular compounds it contains is a valuable …

The field of computational chemistry has seen a significant increase in the integration of
machine learning concepts and algorithms. In this Perspective, we surveyed 179 open …

Designing and generating new data under targeted properties has been attracting various
critical applications such as molecule design, image editing and speech synthesis …

The field of oncology has witnessed an extraordinary surge in the application of big data and
artificial intelligence (AI). AI development has made multiscale and multimodal data fusion …