Understanding protein sequence and structure is essential for understanding protein-protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth, with rational drug design is promising. Rational drug design uses structural data and computational tools to study protein binding sites and protein interfaces to design inhibitors that can change these interactions, thereby potentially leading to therapeutic approaches. Artificial intelligence (AI), such as machine learning (ML) and deep learning (DL), has advanced drug discovery and design by providing computational resources and methods. Quantum chemistry is essential for drug reactivity, toxicology, drug screening, and quantitative structure-activity relationship (QSAR) properties. This review discusses the methodologies and challenges of identifying and characterizing hot spots and binding sites. It also explores the strategies and applications of artificial-intelligence-based rational drug design technologies that target proteins and protein-protein interaction (PPI) binding hot spots. It provides valuable insights for drug design with therapeutic implications. We have also demonstrated the pathological conditions of heat shock protein 27 (HSP27) and matrix metallopoproteinases (MMP2 and MMP9) and designed inhibitors of these proteins using the drug discovery paradigm in a case study on the discovery of drug molecules for cancer treatment. Additionally, the implications of benzothiazole derivatives for anticancer drug design and discovery are deliberated.

Integrins are heterodimeric glycoproteins crucial to the physiology and pathology of many biological functions. As adhesion molecules, they mediate immune cell trafficking, migration, and immunological synapse formation during inflammation and cancer. The recognition of the vital roles of integrins in various diseases revealed their therapeutic potential. Despite the great effort in the last thirty years, up to now, only seven integrin-based drugs have entered the market. Recent progress in deciphering integrin functions, signaling, and interactions with ligands, along with advancement in rational drug design strategies, provide an opportunity to exploit their therapeutic potential and discover novel agents. This review will discuss the molecular modeling methods used in determining integrins\' dynamic properties and in providing information toward understanding their properties and function at the atomic level. Then, we will survey the relevant contributions and the current understanding of integrin structure, activation, the binding of essential ligands, and the role of molecular modeling methods in the rational design of antagonists. We will emphasize the role played by molecular modeling methods in progress in these areas and the designing of integrin antagonists.

Artificial intelligence (AI) renders cutting-edge applications in diverse sectors of society. Due to substantial progress in high-performance computing, the development of superior algorithms, and the accumulation of huge biological and chemical data, computer-assisted drug design technology is playing a key role in drug discovery with its advantages of high efficiency, fast speed, and low cost. Over recent years, due to continuous progress in machine learning (ML) algorithms, AI has been extensively employed in various drug discovery stages. Very recently, drug design and discovery have entered the big data era. ML algorithms have progressively developed into a deep learning technique with potent generalization capability and more effectual big data handling, which further promotes the integration of AI technology and computer-assisted drug discovery technology, hence accelerating the design and discovery of the newest drugs. This review mainly summarizes the application progression of AI technology in the drug discovery process, and explores and compares its advantages over conventional methods. The challenges and limitations of AI in drug design and discovery have also been discussed.

BACKGROUND: Prolylcarboxypeptidase (PrCP) is a serine protease that produces or degrades signaling proteins in several important pathways including the renin-angiotensin system (RAS), kallikrein-kinin system (KKS) and pro-opiomelanocortin (POMC) system. PrCP has the potential to be a therapeutic target for cardiovascular, inflammatory and metabolic diseases. Numerous classes of PrCP inhibitors have been developed by rational drug design and from high-throughput screening hits. These inhibitors have been tested in mouse models to assess their potential as new therapeutics. Areas Covered: This review covers the relevant studies that support PrCP as a target for drug discovery. All the significant patent applications and primary literature concerning the development of PrCP inhibitors are discussed. Expert Opinion: The pathways where PrCP is known to operate are complex and many aspects remain to be characterized. Many potent inhibitors of PrCP have been tested in vivo. The variable results obtained from in vivo studies with PrCP inhibitors suggest that additional understanding of the biochemistry and the required therapeutic inhibitor levels is necessary. Additional fundamental research into the signaling pathways is likely required before the true therapeutic potential of PrCP inhibition will be realized.

Peptides and proteins are attractive initial leads for the rational design of bioactive molecules. Several natural cyclic peptides have recently emerged as templates for drug design due to their resistance to chemical or enzymatic hydrolysis and high selectivity to receptors. The development of practical protocols that mimic the power of nature\'s strategies remains paramount for the advancement of novel peptide-based drugs. The de novo design of peptide mimetics (nonpeptide molecules or cyclic peptides) for the synthesis of linear or cyclic peptides has enhanced the progress of therapeutics and diverse areas of science and technology. In the case of metabolically unstable peptide ligands, the rational design and synthesis of cyclic peptide analogues has turned into an alternative approach for improved biological activity.