Advancements in computational techniques have transformed glaucoma research, providing a deeper understanding of genetics, disease mechanisms, and potential therapeutic targets. Systems genetics integrates genomic and clinical data, aiding in identifying drug targets, comprehending disease mechanisms, and personalizing treatment strategies for glaucoma. Molecular dynamics simulations offer valuable molecular-level insights into glaucoma-related biomolecule behavior and drug interactions, guiding experimental studies and drug discovery efforts. Artificial intelligence (AI) technologies hold promise in revolutionizing glaucoma research, enhancing disease diagnosis, target identification, and drug candidate selection. The generalized protocols for systems genetics, MD simulations, and AI model development are included as a guide for glaucoma researchers. These computational methods, however, are not separate and work harmoniously together to discover novel ways to combat glaucoma. Ongoing research and progresses in genomics technologies, MD simulations, and AI methodologies project computational methods to become an integral part of glaucoma research in the future. 

Computational methods in glaucoma research: Current status and future outlook.

A large portion of the human GPCRome is still in the dark and understudied, consisting even of entire subfamilies of GPCRs such as odorant receptors, class A and C orphans, adhesion GPCRs, Frizzleds and taste receptors. However, it is undeniable that these GPCRs bring an untapped therapeutic potential that should be explored further. Open questions on these GPCRs span diverse topics such as deorphanisation, the development of tool compounds and tools for studying these GPCRs, as well as understanding basic signalling mechanisms. This review gives an overview of the current state of knowledge for each of the diverse subfamilies of understudied receptors regarding their physiological relevance, molecular mechanisms, endogenous ligands and pharmacological tools. Furthermore, it identifies some of the largest knowledge gaps that should be addressed in the foreseeable future and lists some general strategies that might be helpful in this process.

The dark sides of the GPCR tree - research progress on understudied GPCRs.

ADVERTISEMENT RETURN TO ISSUEEditorialNEXTAlphaFold2 in Molecular DiscoveryAriane Nunes-Alves*Ariane Nunes-AlvesInstitute of Chemistry, Technische Universität Berlin, Berlin 10623, Germany*Ariane Nunes Alves - Institute of Chemistry, Technische Universität Berlin, Berlin 10623, Germany; email: [email protected]More by Ariane Nunes-Alveshttps://orcid.org/0000-0002-5488-4732 and Kenneth MerzKenneth MerzDepartment of Chemistry, Michigan State University, East Lansing 48824, Michigan, United StatesMore by Kenneth Merzhttps://orcid.org/0000-0001-9139-5893Cite this: J. Chem. Inf. Model. 2023, 63, 19, 5947–5949Publication Date (Web):October 9, 2023Publication History Received11 September 2023Published online9 October 2023Published inissue 9 October 2023https://doi.org/10.1021/acs.jcim.3c01459Copyright © 2023 American Chemical SocietyRequest reuse permissions This publication is free to access through this site. Learn MoreArticle Views2112Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (929 KB) Get e-AlertscloseSUBJECTS:Computational modeling,Drug discovery,Homology,Molecular structure,Protein structure Get e-Alerts 

AlphaFold2 in Molecular Discovery.

While the intranasal administration of drugs to the brain has been gaining both research attention and regulatory success over the past several years, key fundamental and translational challenges remain to fully leveraging the promise of this drug delivery pathway for improving the treatment of various neurological and psychiatric illnesses. In response, this review highlights the current state of understanding of the nose-to-brain drug delivery pathway and how both biological and clinical barriers to drug transport using the pathway can been addressed, as illustrated by demonstrations of how currently approved intranasal sprays leverage these pathways to enable the design of successful therapies. Moving forward, aiming to better exploit the understanding of this fundamental pathway, we also outline the development of nanoparticle systems that show improvement in delivering approved drugs to the brain and how engineered nanoparticle formulations could aid in breakthroughs in terms of delivering emerging drugs and therapeutics while avoiding systemic adverse effects.

/pdf/recent-approaches-of-intranasal-to-brain-drug-delivery-plom7ha1.pdf

Recent Approaches of Intranasal to Brain Drug Delivery System

Targeting RNA including viral RNAs with small molecules is an emerging field. The hepatitis C virus internal ribosome entry site (HCV IRES) is a potential target for translation inhibitor development...

Structure-based virtual screening of unbiased and RNA-focused libraries to identify new ligands for the HCV IRES model system

AlphaFold2 and RoseTTAfold impress with their high accuracy in protein structure prediction. However, for structure-based virtual screenings, not only the overall structure but especially the binding sites need to be accurately predicted. In this work, the docking performance for 66 targets with known ligands but without experimental structures available in the protein data bank was elucidated. The results suggest that using an experimental surrogate-ligand complex is often superior over homology models, and only at low sequence identity to the closest homologue AlphaFold2 structures show an equal performance. The generally high fluctuation of receiver operating characteristic area under the curve values obtained for different homology models suggests that multiple combinations of docking programs and homology models should be tested prior to prospective virtual screenings, and in some cases post-processing of crude models might be necessary.

Hic Sunt Dracones: Molecular Docking in Uncharted Territories with Structures from AlphaFold2 and RoseTTAfold

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Hic Sunt Dracones: Molecular Docking in Uncharted Territories with Structures from AlphaFold2 and RoseTTAfold