Abstract
The integration of computational techniques into drug development has led to a substantial increase in the knowledge of structural, chemical, and biological data. These techniques are useful for handling the big data generated by empirical and clinical studies. Over the last few years, computer-aided drug discovery methods such as virtual screening, pharmacophore modeling, quantitative structure-activity relationship analysis, and molecular docking have been employed by pharmaceutical companies and academic researchers for the development of pharmacologically active drugs. Toll-like receptors (TLRs) play a vital role in various inflammatory, autoimmune, and neurodegenerative disorders such as sepsis, rheumatoid arthritis, inflammatory bowel disease, Alzheimer’s disease, multiple sclerosis, cancer, and systemic lupus erythematosus. TLRs, particularly TLR4, have been identified as potential drug targets for the treatment of these diseases, and several relevant compounds are under preclinical and clinical evaluation. This review covers the reported computational studies and techniques that have provided insights into TLR4-targeting therapeutics. Furthermore, this article provides an overview of the computational methods that can benefit a broad audience in this field and help with the development of novel drugs for TLR-related disorders.
Highlights
Toll-like receptor 4 (TLR4) belongs to the pattern recognition receptor family, which plays a key role in the human defense mechanism and responds to invading pathogens with high selectivity and sensitivity [1,2]
This study suggests that TLR4 along with the other four genes is a potential drug target in ovarian cancer and that their expression is related to patient survival [70]
TLR4 plays a key role in the host defense mechanism and is capable of recognizing a variety of
Summary
Toll-like receptor 4 (TLR4) belongs to the pattern recognition receptor family, which plays a key role in the human defense mechanism and responds to invading pathogens with high selectivity and sensitivity [1,2]. CADD is a cost-effective method that has accelerated the drug development process by reducing the total timespan from target identification to hit discovery and optimization[7]. Structure, the LBDD method is employed, which takes into account the information on known ligands for the specific target. In SAR analysis, the chemical structure and biological activity of known ligands of LBDD method. In SAR analysis, the chemical structure and biological activity of known ligands can can be utilized to design new drugs or optimize known drugs to increase their efficacy [10]. The step is target which classifies the classifies molecular and evaluates it is whether it is suitable for drug development by various in vivo and in vitro methods. FDA and becomes available on the market for clinical use
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