Computer-aided drug discovery has been around for decades, although the past few years
have seen a tectonic shift towards embracing computational technologies in both academia …

Networks—or graphs—are universal descriptors of systems of interacting elements. In
biomedicine and healthcare, they can represent, for example, molecular interactions …

Ninety percent of clinical drug development fails despite implementation of many successful
strategies, which raised the question whether certain aspects in target validation and drug …

Machine learning (ML) is increasingly used in environmental research to process large data
sets and decipher complex relationships between system variables. However, due to the …

The expanding scale and inherent complexity of biological data have encouraged a growing
use of machine learning in biology to build informative and predictive models of the …

Despite advances in molecular biology, genetics, computation, and medicinal chemistry,
infectious disease remains an ominous threat to public health. Addressing the challenges …

Abstract Machine learning has been widely adopted for improving building energy efficiency
and flexibility in the past decade owing to the ever-increasing availability of massive building …

Drug designing and development is an important area of research for pharmaceutical
companies and chemical scientists. However, low efficacy, off-target delivery, time …

Artificial intelligence (AI) and machine learning (ML) have recently been radically improved
and are now being employed in almost every application domain to develop automated or …

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) …