Molecular docking and molecular dynamics in natural products-based drug discovery

Rakshit, Gourav; Komal,; Dagur, Pankaj; Biswas, Abanish; Murtuja, Sheikh; Jayaprakash, Venkatesan

doi:10.1016/b978-0-323-90593-0.00018-6

Cited by 5 publications

(1 citation statement)

References 18 publications

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“…These multiomics technologies are unveiling novel druggable targets and enhancing the understanding of disease mechanisms, thereby broadening the spectrum of opportunities in drug discovery. 62,63 Predicting the free binding energy (affinity) from the protein−ligand atomic coordinates remains a challenge to date in computational chemistry. Drug discovery is surely going to benefit from the advances in this field of chemistry, thereby providing viable chemical compounds from the ever-evolving chemical spaces during virtual screening and thus advancing hit-to-lead optimization.…”

Section: Rational Drug Design Technologiesmentioning

confidence: 99%

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: Rational Drug Design Technologiesmentioning

confidence: 99%