{"title":"Targeting the Aryl Hydrocarbon Receptor (AhR): A Review of the In-Silico Screening Approaches to Identify AhR Modulators","authors":"F. E. Mosa, A. El-Kadi, K. Barakat","doi":"10.5772/intechopen.99228","DOIUrl":"https://doi.org/10.5772/intechopen.99228","url":null,"abstract":"Aryl hydrocarbon receptor (AhR) is a biological sensor that integrates environmental, metabolic, and endogenous signals to control complex cellular responses in physiological and pathophysiological functions. The full-length AhR encompasses various domains, including a bHLH, a PAS A, a PAS B, and transactivation domains. With the exception of the PAS B and transactivation domains, the available 3D structures of AhR revealed structural details of its subdomains interactions as well as its interaction with other protein partners. Towards screening for novel AhR modulators homology modeling was employed to develop AhR-PAS B domain models. These models were validated using molecular dynamics simulations and binding site identification methods. Furthermore, docking of well-known AhR ligands assisted in confirming these binding pockets and discovering critical residues to host these ligands. In this context, virtual screening utilizing both ligand-based and structure-based methods screened large databases of small molecules to identify novel AhR agonists or antagonists and suggest hits from these screens for validation in an experimental biological test. Recently, machine-learning algorithms are being explored as a tool to enhance the screening process of AhR modulators and to minimize the errors associated with structure-based methods. This chapter reviews all in silico screening that were focused on identifying AhR modulators and discusses future perspectives towards this goal.","PeriodicalId":198100,"journal":{"name":"High-Throughput Screening for Drug Discovery [Working Title]","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132963712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Design and Implementation of High Throughput Screening Assays for Drug Discoveries","authors":"F. Bokhari, A. Albukhari","doi":"10.5772/intechopen.98733","DOIUrl":"https://doi.org/10.5772/intechopen.98733","url":null,"abstract":"The process of drug discovery is challenging and a costly affair. It takes about 12 to 15 years and costs over $1 billion dollars to develop a new drug and introduce the finished product in the market. With the increase in diseases, virus spread, and patients, it has become essential to invent new medicines. Consequently, today researchers are becoming interested in inventing new medicines faster by adopting higher throughput screening methods. One avenue of approach to discovering drugs faster is the High-Throughput Screening (HTS) method, which has gained a lot of attention in the previous few years. Today, High-Throughput Screening (HTS) has become a standard method for discovering drugs in various pharmaceutical industries. This review focuses on the advancement of technologies in High-Throughput Screening (HTS) methods, namely fluorescence resonance energy transfer (FRET), biochemical assay, fluorescence polarization (FP), homogeneous time resolved fluorescence (HTRF), Fluorescence correlation spectroscopy (FCS), Fluorescence intensity distribution analysis (FIDA), Nuclear magnetic resonance (NMR), and research advances in three major technology areas including miniaturization, automation and robotics, and artificial intelligence, which promises to help speed up the discovery of medicines and its development process.","PeriodicalId":198100,"journal":{"name":"High-Throughput Screening for Drug Discovery [Working Title]","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123917666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Unbiased Identification of Extracellular Protein–Protein Interactions for Drug Target and Biologic Drug Discovery","authors":"Shengya Cao, N. Martinez-Martin","doi":"10.5772/intechopen.97310","DOIUrl":"https://doi.org/10.5772/intechopen.97310","url":null,"abstract":"Technological improvements in unbiased screening have accelerated drug target discovery. In particular, membrane-embedded and secreted proteins have gained attention because of their ability to orchestrate intercellular communication. Dysregulation of their extracellular protein–protein interactions (ePPIs) underlies the initiation and progression of many human diseases. Practically, ePPIs are also accessible for modulation by therapeutics since they operate outside of the plasma membrane. Therefore, it is unsurprising that while these proteins make up about 30% of human genes, they encompass the majority of drug targets approved by the FDA. Even so, most secreted and membrane proteins remain uncharacterized in terms of binding partners and cellular functions. To address this, a number of approaches have been developed to overcome challenges associated with membrane protein biology and ePPI discovery. This chapter will cover recent advances that use high-throughput methods to move towards the generation of a comprehensive network of ePPIs in humans for future targeted drug discovery.","PeriodicalId":198100,"journal":{"name":"High-Throughput Screening for Drug Discovery [Working Title]","volume":"439 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134063680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}