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Pharmaceuticals
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Pharmacophore Modeling and Applications in Drug Discovery: Challenges and Recent...
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Pharmacophore Modeling and Applications in Drug Discovery: Challenges and Recent Advances

A special issue of Pharmaceuticals (ISSN 1424-8247). This special issue belongs to the section "Medicinal Chemistry".

Deadline for manuscript submissions: closed (28 August 2022) | Viewed by 9743

Special Issue Editors

Dr. Pavel Polishchuk

E-Mail Website
Guest Editor
Institute of Molecular and Translational Medicine, Palacky University, 771 47 Olomouc, Czech Republic
Interests: cheminformatics; machine learning; molecular and drug design; medicinal chemistry.
Dr. Thomas Seidel

E-Mail Website
Guest Editor
Department of Pharmaceutical Sciences, University of Vienna, 1010 Vienna, Austria
Interests: cheminformatics; algorithm development; medicinal chemistry; computer-aided drug design

Special Issue Information

Dear Colleagues,

Pharmacophores are widely used in drug design due to their simplicity and the high level of abstraction enabling a fast screening of chemical libraries and the identification of potential hits bearing new scaffolds. The concept of pharmacophores opened a well established research field with many achievements in the past and is still undergoing a rapid further development. Recent advancements mainly concern the extended application of pharmacophores to retrieve and refine information obtained from molecular dynamics simulations of protein-ligand complexes. This can improve performance of virtual screening, help to explain observed structure-property relationships, or derive pharmacophores for apo-protein sites. More recently, application of deep learning demonstrated the ability to generate structures for a particular pharmacophore model that substantially expands the applicability of pharmacophores to the design of novel promising ligands with tailored properties.

This Special Issue will cover a wide range of topics related to pharmacophore modeling to summarize recent advances and future challenges in the field. This includes but is not limited to new applications of pharmacophores, newly developed methods and their retrospective and prospective validation, application of pharmacophores in machine learning and de novo design. In addition, there will be space for “success stories” in drug design where pharmacophore-based approaches played a key role to achieve substantial advancements in the exploration of novel targets, newly discovered binding sites or targets where other methods failed.

Dr. Pavel Polishchuk
Dr. Thomas Seidel
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Pharmaceuticals is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • ligand-based pharmacophores
  • structure-based pharmacophores
  • virtual screening
  • drug design
  • machine learning
  • molecular dynamics
  • de novo design

Published Papers (4 papers)

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Research

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18 pages, 2542 KiB  
Article
Consensus Ensemble Multitarget Neural Network Model of Anxiolytic Activity of Chemical Compounds and Its Use for Multitarget Pharmacophore Design
by Pavel M. Vassiliev, Dmitriy V. Maltsev, Alexander A. Spasov, Maxim A. Perfilev, Maria O. Skripka and Andrey N. Kochetkov
Pharmaceuticals 2023, 16(5), 731; https://doi.org/10.3390/ph16050731 - 11 May 2023
Viewed by 1161
Abstract
A classification consensus ensemble multitarget neural network model of the dependence of the anxiolytic activity of chemical compounds on the energy of their docking in 17 biotargets was developed. The training set included compounds thathadalready been tested for anxiolytic activity and were structurally [...] Read more.
A classification consensus ensemble multitarget neural network model of the dependence of the anxiolytic activity of chemical compounds on the energy of their docking in 17 biotargets was developed. The training set included compounds thathadalready been tested for anxiolytic activity and were structurally similar to the 15 studied nitrogen-containing heterocyclic chemotypes. Seventeen biotargets relevant to anxiolytic activity were selected, taking into account the possible effect on them of the derivatives of these chemotypes. The generated model consistedof three ensembles of artificial neural networks for predicting three levels of anxiolytic activity, with sevenneural networks in each ensemble. A sensitive analysis of neurons in an ensemble of neural networks for a high level of activity made it possible to identify four biotargets ADRA1B, ADRA2A, AGTR1, and NMDA-Glut, which were the most significant for the manifestation of the anxiolytic effect. For these four key biotargets for 2,3,4,5-tetrahydro-11H-[1,3]diazepino[1,2-a]benzimidazole and [1,2,4]triazolo[3,4-a][2,3]benzodiazepine derivatives, eight monotarget pharmacophores of high anxiolytic activity were built. Superposition of monotarget pharmacophores built two multitarget pharmacophores of high anxiolytic activity, reflecting the universal features of interaction 2,3,4,5-tetrahydro-11H-[1,3]diazepino[1,2-a]benzimidazole and [1,2,4]triazolo[3,4-a][2,3]benzodiazepine derivatives with the most significant biotargets ADRA1B, ADRA2A, AGTR1, and NMDA-Glut. Full article
(This article belongs to the Special Issue Pharmacophore Modeling and Applications in Drug Discovery: Challenges and Recent Advances)
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12 pages, 2372 KiB  
Article
Applications of the Novel Quantitative Pharmacophore Activity Relationship Method QPhAR in Virtual Screening and Lead-Optimisation
by Stefan Michael Kohlbacher, Matthias Schmid, Thomas Seidel and Thierry Langer
Pharmaceuticals 2022, 15(9), 1122; https://doi.org/10.3390/ph15091122 - 08 Sep 2022
Cited by 3 | Viewed by 1672
Abstract
Pharmacophores are an established concept for the modelling of ligand–receptor interactions based on the abstract representations of stereoelectronic molecular features. They became widely popular as filters for the fast virtual screening of large compound libraries. A lot of effort has been put into [...] Read more.
Pharmacophores are an established concept for the modelling of ligand–receptor interactions based on the abstract representations of stereoelectronic molecular features. They became widely popular as filters for the fast virtual screening of large compound libraries. A lot of effort has been put into the development of sophisticated algorithms and strategies to increase the computational efficiency of the screening process. However, hardly any focus has been put on the development of automated procedures that optimise pharmacophores towards higher discriminatory power, which still has to be done manually by a human expert. In the age of machine learning, the researcher has become the decision-maker at the top level, outsourcing analysis tasks and recurrent work to advanced algorithms and automation workflows. Here, we propose an algorithm for the automated selection of features driving pharmacophore model quality using SAR information extracted from validated QPhAR models. By integrating the developed method into an end-to-end workflow, we present a fully automated method that is able to derive best-quality pharmacophores from a given input dataset. Finally, we show how the QPhAR-generated models can be used to guide the researcher with insights regarding (un-)favourable interactions for compounds of interest. Full article
(This article belongs to the Special Issue Pharmacophore Modeling and Applications in Drug Discovery: Challenges and Recent Advances)
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11 pages, 1034 KiB  
Article
Pharmacophore-Based Discovery of Viral RNA Conformational Modulators
by María Martín-Villamil, Isaías Sanmartín, Ángela Moreno and José Gallego
Pharmaceuticals 2022, 15(6), 748; https://doi.org/10.3390/ph15060748 - 14 Jun 2022
Cited by 2 | Viewed by 1894
Abstract
New RNA-binding small-molecule scaffolds are needed to unleash the pharmacological potential of RNA targets. Here we have applied a pharmacophore-based virtual screening approach, seldom used in the RNA recognition field, to identify novel conformational inhibitors of the hepatitis C virus internal ribosome entry [...] Read more.
New RNA-binding small-molecule scaffolds are needed to unleash the pharmacological potential of RNA targets. Here we have applied a pharmacophore-based virtual screening approach, seldom used in the RNA recognition field, to identify novel conformational inhibitors of the hepatitis C virus internal ribosome entry site. The conformational effect of the screening hits was assessed with a fluorescence resonance energy transfer assay, and the affinity, specificity, and binding site of the ligands were determined using a combination of fluorescence intensity and NMR spectroscopy experiments. The results indicate that this strategy can be successfully applied to discover RNA conformational inhibitors bearing substantially less positive charge than the reference ligands. This methodology can potentially be accommodated to other RNA motifs of pharmacological interest, facilitating the discovery of novel RNA-targeted molecules. Full article
(This article belongs to the Special Issue Pharmacophore Modeling and Applications in Drug Discovery: Challenges and Recent Advances)
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Review

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31 pages, 5203 KiB  
Review
Mind the Gap—Deciphering GPCR Pharmacology Using 3D Pharmacophores and Artificial Intelligence
by Theresa Noonan, Katrin Denzinger, Valerij Talagayev, Yu Chen, Kristina Puls, Clemens Alexander Wolf, Sijie Liu, Trung Ngoc Nguyen and Gerhard Wolber
Pharmaceuticals 2022, 15(11), 1304; https://doi.org/10.3390/ph15111304 - 22 Oct 2022
Cited by 2 | Viewed by 3081
Abstract
G protein-coupled receptors (GPCRs) are amongst the most pharmaceutically relevant and well-studied protein targets, yet unanswered questions in the field leave significant gaps in our understanding of their nuanced structure and function. Three-dimensional pharmacophore models are powerful computational tools in in silico drug [...] Read more.
G protein-coupled receptors (GPCRs) are amongst the most pharmaceutically relevant and well-studied protein targets, yet unanswered questions in the field leave significant gaps in our understanding of their nuanced structure and function. Three-dimensional pharmacophore models are powerful computational tools in in silico drug discovery, presenting myriad opportunities for the integration of GPCR structural biology and cheminformatics. This review highlights success stories in the application of 3D pharmacophore modeling to de novo drug design, the discovery of biased and allosteric ligands, scaffold hopping, QSAR analysis, hit-to-lead optimization, GPCR de-orphanization, mechanistic understanding of GPCR pharmacology and the elucidation of ligand–receptor interactions. Furthermore, advances in the incorporation of dynamics and machine learning are highlighted. The review will analyze challenges in the field of GPCR drug discovery, detailing how 3D pharmacophore modeling can be used to address them. Finally, we will present opportunities afforded by 3D pharmacophore modeling in the advancement of our understanding and targeting of GPCRs. Full article
(This article belongs to the Special Issue Pharmacophore Modeling and Applications in Drug Discovery: Challenges and Recent Advances)
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