Quantitative structural assessments of potential meprin β inhibitors by non-linear QSAR approaches and validation by binding mode of interaction analysis

Banerjee, S.; Baidya, Sandip Kumar; Ghosh, Balaram; Nandi, Suvendu; Mandal, Mahitosh; Jha, Tarun; Adhikari, Nilanjan

doi:10.1039/d2nj04753e

Cited by 17 publications

(2 citation statements)

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“…MM-PBSA simplifies structure−activity relationships and lead optimization studies. 157 RMSD, RMSF, R g , and MM-PBSA enrich the understanding of protein−ligand interactions, conformational dynamics, and binding affinities, which are crucial for advancing drug design and discovery. 158,159 ML and DL frameworks, such as TorchMD, have been developed to improve the quality and transferability of empirical potentials used in molecular dynamics simulations, including the extraction of free energy surfaces and kinetics, which assist in the analysis of molecular events and thermodynamics.…”

Section: Molecular Dynamic Simulationmentioning

confidence: 99%

“…To determine binding, the Molecular Mechanics Poisson–Boltzmann Surface Area (MM-PBSA) method estimates the binding free energy difference between a biomolecular complex and its unbound components. MM-PBSA simplifies structure–activity relationships and lead optimization studies . RMSD, RMSF, R g , and MM-PBSA enrich the understanding of protein–ligand interactions, conformational dynamics, and binding affinities, which are crucial for advancing drug design and discovery. , …”

Section: Rational Drug Design Technologiesmentioning

confidence: 99%

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

Nandi,

Bhaduri,

Das

et al. 2024

Mol. Pharmaceutics

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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.

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Section: Molecular Dynamic Simulationmentioning

confidence: 99%