The primary goal of rational drug discovery is the identification of selective ligands which act on single or multiple drug targets to achieve the desired clinical outcome through the exploration of total chemical space. To identify such desired compounds, computational approaches are necessary in predicting their drug-like properties. G Protein-Coupled Receptors (GPCRs) represent one of the largest and most important integral membrane protein families. These receptors serve as increasingly attractive drug targets due to their relevance in the treatment of various diseases, such as inflammatory disorders, metabolic imbalances, cardiac disorders, cancer, monogenic disorders, etc. In the last decade, multitudes of three-dimensional (3D) structures were solved for diverse GPCRs, thus referring to this period as the "golden age for GPCR structural biology." Moreover, accumulation of data about the chemical properties of GPCR ligands has garnered much interest toward the exploration of GPCR chemical space. Due to the steady increase in the structural, ligand, and functional data of GPCRs, several cheminformatics approaches have been implemented in its drug discovery pipeline. In this review, we mainly focus on the cheminformatics-based paradigms in GPCR drug discovery. We provide a comprehensive view on the ligand- and structure-based cheminformatics approaches which are best illustrated GPCR case studies. Furthermore, an appropriate combination of ligand-based knowledge with structure-based ones, i.e., integrated approach, which is emerging as a promising strategy for cheminformatics-based GPCR drug design is also discussed.
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http://dx.doi.org/10.3389/fphar.2018.00128 | DOI Listing |
Mol Pharm
December 2024
School of Pharmacy, University College Cork, College Road, Cork T12 K8AF, Ireland.
Advanced predictive modeling approaches have harnessed data to fuel important innovations at all stages of drug development. However, the need for a machine-readable drug product library which consolidates many aspects of formulation design and performance remains largely unmet. This study presents a scripted, reproducible approach to database curation and explores its potential to streamline oral medicine development.
View Article and Find Full Text PDFJ Genet Eng Biotechnol
December 2024
Biochemistry Department, Faculty of Science, Alexandria University, El-Shatbi, 21568, Alexandria, Egypt; Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, United States.
Purpose: Hepatocellular carcinoma (HCC) resistance to sorafenib treatment and other treatment strategies causes a higher mortality rate in patients diagnosed with HCC.
Research Question: HCC often develops resistance to sorafenib treatment and other therapies, leading to increased mortality rates in diagnosed patients. Herein, we propose a combined therapeutic approach using rosiglitazone, a key factor in cellular differentiation, along with adipogenesis inducers such as dexamethasone, IBMX, and insulin.
Chemosphere
December 2024
Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India. Electronic address:
Regulatory authorities frequently need information on a chemical's capacity to produce acute systemic toxicity in humans. Due to concerns about animal welfare, human relevance, and reproducibility, numerous international initiatives have centered on finding a substitute for using animals in acute systemic lethality testing. These substitutes include the more current in-silico and in vitro techniques.
View Article and Find Full Text PDFJ Mol Model
December 2024
Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics, and Atmospheric Sciences, Jackson State University, Jackson, MS, 39217, USA.
Chem Res Toxicol
December 2024
Center for Computational Toxicology & Exposure (CCTE), U.S. Environmental Protection Agency, Research Triangle Park, Durham, North Carolina 27709, United States.
The Toxic Substances Control Act (TSCA) requires the US EPA to evaluate the hazard and exposure of new and existing chemicals. New chemical notifications are typically data-poor and EPA has historically relied upon approaches including chemical categories to fill data gaps. As part of a multi-year Research Program, opportunities are being explored to leverage New Approach Methods (NAMs) in hazard and exposure assessments.
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