Quantitative Structure Activity Relationships Study of Soluble Epoxide Hydrolase Inhibitors Using MLR, ANN, CoMFA and CoMSIA Methods

Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence

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.

Discovery of phthalimide derivatives as novel inhibitors of a soluble epoxide hydrolase

Soluble epoxide hydrolase (sEH) inhibitors are effective in reducing blood pressure, inflammation, and pain in a number of mammalian disease models. As most classical urea-based sEH inhibitors suffer from poor solubility and pharmacokinetic properties, the development of novel sEH inhibitors with an improved pharmacokinetic specification has received a great deal of attention. In this study, a series of amide-based sEH inhibitors bearing a phthalimide ring as the novel secondary pharmacophore (P₂ ) was designed, synthesized, and evaluated. Docking results illustrated that the amide group as the primary pharmacophore (P₁ ) was placed at a suitable distance from the three key amino acids (Tyr383, Tyr466, and Asp335) for an effective hydrogen bonding. In agreement with these findings, most of the newly synthesized compounds demonstrated moderate to high sEH inhibitory activities, relative to 12-(3-adamantan-1-yl-ureido)dodecanoic acid as the reference standard. Compound 12e with a 4-methoxybenzoyl substituent exhibited the highest sEH inhibitory activity, with an IC₅₀ value of 1.06 nM. Moreover, the ADME properties of the compounds were evaluated in silico, and the results revealed appropriate predictions.