Identification of protein–protein interactions (PPIs) is at the center of molecular biology
considering the unquestionable role of proteins in cells. Combinatorial interactions result in …

In recent years, there has been a surge of interest in using machine learning algorithms
(MLAs) in oncology, particularly for biomedical applications such as drug discovery, drug …

Deep learning on graphs has become one of the hottest topics in machine learning. The
book consists of four parts to best accommodate our readers with diverse backgrounds and …

We consider the prediction of interfaces between proteins, a challenging problem with
important applications in drug discovery and design, and examine the performance of …

Motivation Protein–protein interactions drive wide-ranging molecular processes, and
characterizing at the atomic level how proteins interact (beyond just the fact that they …

Directed evolution has emerged as a powerful strategy to engineer various properties of
proteins. Traditional methods to construct libraries such as error-prone PCR and DNA …

Reliably pinpointing which specific amino acid residues form the interface (s) between a
protein and its binding partner (s) is critical for understanding the structural and …

Motivation Accurate predictions of protein-binding residues (PBRs) enhances understanding
of molecular-level rules governing protein–protein interactions, helps protein–protein …

The advent of advanced molecular modeling software, big data analytics, and high-speed
processing units has led to the exponential evolution of modern drug discovery and better …

Abstract Motivation Protein–Protein Interactions (PPI) are essentials for most cellular
processes and thus, unveiling how proteins interact is a crucial question that can be better …