Advances in Drug Discovery based on Genomics, Proteomics and Bioinformatics

Gupta, Satya P.

doi:10.2174/156802662220220908152340

Cited by 4 publications

(1 citation statement)

References 4 publications

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“…The advancement in structure-based drug discovery is propelled by accessible multiomic, genomic, and proteomic data, which facilitates a faster, cost-efficient lead discovery. These multiomics technologies are unveiling novel druggable targets and enhancing the understanding of disease mechanisms, thereby broadening the spectrum of opportunities in drug discovery. , 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. , The process of screening active ligands involves several phases.…”

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%

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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Bridging the gap: From petri dish to patient - Advancements in translational drug discovery

El-Tanani,

Rabbani,

El-Tanani

et al. 2025

Heliyon

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Unravelling potential biomarkers for acute and chronic brucellosis through proteomic and bioinformatic approaches

Yang

Qiao

et al. 2023

Front. Cell. Infect. Microbiol.

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IntroductionThis study aimed to identify biomarkers for acute and chronic brucellosis using advanced proteomic and bioinformatic methods.MethodsBlood samples from individuals with acute brucellosis, chronic brucellosis, and healthy controls were analyzed. Proteomic techniques and differential expression analysis were used to identify differentially expressed proteins. Co-expression modules associated with brucellosis traits were identified using weighted gene co-expression network analysis (WGCNA).Results763 differentially expressed proteins were identified, and two co-expression modules were found to be significantly associated with brucellosis traits. 25 proteins were differentially expressed in all three comparisons, and 20 hub proteins were identified. Nine proteins were found to be both differentially expressed and hub proteins, indicating their potential significance. A random forest model based on these nine proteins showed good classification performance.DiscussionThe identified proteins are involved in processes such as inflammation, coagulation, extracellular matrix regulation, and immune response. They provide insights into potential therapeutic targets and diagnostic biomarkers for brucellosis. This study improves our understanding of brucellosis at the molecular level and paves the way for further research in targeted therapies and diagnostics.

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Advances in Drug Discovery based on Genomics, Proteomics and Bioinformatics

Cited by 4 publications

References 4 publications

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

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

Bridging the gap: From petri dish to patient - Advancements in translational drug discovery

Unravelling potential biomarkers for acute and chronic brucellosis through proteomic and bioinformatic approaches

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