Ligand-based drug design (LBDD)

Yadav, Vivek; Reang, Jurnal; Vinita, -; Tonk, Rajiv Kumar

doi:10.1016/b978-0-323-90608-1.00009-5

Cited by 2 publications

(4 citation statements)

References 27 publications

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“…These techniques are frequently more scalable and computationally efficient than traditional docking methods. 195,196 LBDD is a robust mechanism for discovering diverse and novel compounds that have utility in a variety of therapeutic domains, such as anticancer, anti-inflammatory, antiviral, and antibacterial drug development. By streamlining data analysis, making predictions about ligand behavior, and speeding up the identification of novel compounds, AI and ML improve ligandbased drug design (LBDD).…”

Section: Molecular Dynamic Simulationmentioning

confidence: 99%

“…58 In contrast, the 3D structure of the target is not required for LBDD. 59 Drug development is directed by utilizing pre-existing ligands through the use of pharmacophore modeling, QSAR, and similarity searching. Collectively, these approaches have many benefits over conventional drug discovery.…”

Section: Rational Drug Design Technologiesmentioning

confidence: 99%

“…This strategy is based on molecular docking, de novo design, and fragment-based drug design (FBDD), all of which provide alternative ways to find new therapeutic agents . In contrast, the 3D structure of the target is not required for LBDD . Drug development is directed by utilizing pre-existing ligands through the use of pharmacophore modeling, QSAR, and similarity searching.…”

Section: Rational Drug Design Technologiesmentioning

confidence: 99%

“…Similarity searching is a popular strategy that employs molecular descriptors, such as molecular weight, logP, EHOMO, ELUMO, and charges, as well as 2D fingerprint and 3D shape similarity searches. These techniques are frequently more scalable and computationally efficient than traditional docking methods. , …”

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%

Section: Rational Drug Design Technologiesmentioning

confidence: 99%

Section: Rational Drug Design Technologiesmentioning

confidence: 99%

Section: Rational Drug Design Technologiesmentioning

confidence: 99%

See 2 more Smart Citations