Similarity has always been a key aspect in computer science and statistics. Any time two
element vectors are compared, many different similarity approaches can be used …

As expenditure on drug development increases exponentially, the overall drug discovery
process requires a sustainable revolution. Since artificial intelligence (AI) is leading the …

Alzheimer's disease (AD) is a neurodegenerative disease that causes irreversible damage
to several brain regions, including the hippocampus causing impairment in cognition …

Synergistic drug combinations can improve the therapeutic effect and reduce the drug
dosage to avoid toxicity. In previous years, an in vitro approach was utilized to screen …

There has recently been a rapid progress in computational methods for determining protein
targets of small molecule drugs, which will be termed as compound protein interaction (CPI) …

Introduction and methodology Pairs of similar compounds that only differ by a small
structural modification but exhibit a large difference in their binding affinity for a given target …

Due to issues such as limited variability in monitoring data across different tool wear
conditions and interference during the machining process, data-driven monitoring models …

The drug response prediction problem arises from personalized medicine and drug
discovery. Deep neural networks have been applied to the multi-omics data being available …

The quest for generating novel chemistry knowledge is critical in scientific advancement,
and machine learning (ML) has emerged as an asset in this pursuit. Through interpolation …

The rapid development of machine learning and deep learning algorithms in the recent
decade has spurred an outburst of their applications in many research fields. In the …