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Developments in Drug Discovery: Computational and Experimental Aspects 2.0

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Pharmacology".

Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 2766

Special Issue Editors


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Guest Editor
Nencki Institute of Experimental Biology of the Polish Academy of Sciences, Warsaw, Poland
Interests: structural biology; computational biology; protein crystallography; molecular modeling; molecular docking; drug design; molecular dynamics
Special Issues, Collections and Topics in MDPI journals
Centre of New Technologies, University of Warsaw, Warsaw, Poland
Interests: protein crystallography; drug design; enzymology; structural biology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

A vast amount of life science research investigates either completely new drugs or improvements to already-existing ones. Drug discovery is a multidisciplinary effort aiming to provide novel natural, semisynthetic, or fully (bio)synthetic therapeutic molecules that could target diseases of humans and animals. These therapeutic agents are most commonly small molecules, but they can also be macromolecules. The field of drug discovery is highly dynamic, with the already well-established yet ever-increasing role of in silico approaches and an urgent need to address drug resistance, especially in the form of antimicrobial resistance (AMR). Drug discovery research combines methods, techniques, and expertise from biochemistry, pharmacology, genetic engineering, and medicinal and computational chemistry or cheminformatics.

Here, we invite original research and review articles on drug discovery. The “Molecular Pharmacology” Section aims to publish the latest developments in cellular and molecular pharmacology, with a major emphasis on the mechanisms of action of novel drugs, innovative pharmacological technologies, cell signaling, transduction pathway analysis, genomics, proteomics, and metabonomics applications used to study drug action.

This Special Issue is committed to providing an overview of recent topics and developments from the broad field of drug discovery research. This can include findings of new molecular targets, novel modulators of activity of established targets, and descriptions of promising methods that could enrich the portfolio of available drug discovery techniques.

Dr. Adam Jarmuła
Dr. Piotr Maj
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. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. 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

  • drug discovery
  • molecular targets
  • receptors
  • drug development
  • computer-aided drug discovery
  • structure-based
  • ligand-based
  • antimicrobial resistance

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Published Papers (1 paper)

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Research

20 pages, 4308 KiB  
Article
Kinome-Wide Virtual Screening by Multi-Task Deep Learning
by Jiaming Hu, Bryce K. Allen, Vasileios Stathias, Nagi G. Ayad and Stephan C. Schürer
Int. J. Mol. Sci. 2024, 25(5), 2538; https://doi.org/10.3390/ijms25052538 - 22 Feb 2024
Cited by 2 | Viewed by 2136
Abstract
Deep learning is a machine learning technique to model high-level abstractions in data by utilizing a graph composed of multiple processing layers that experience various linear and non-linear transformations. This technique has been shown to perform well for applications in drug discovery, utilizing [...] Read more.
Deep learning is a machine learning technique to model high-level abstractions in data by utilizing a graph composed of multiple processing layers that experience various linear and non-linear transformations. This technique has been shown to perform well for applications in drug discovery, utilizing structural features of small molecules to predict activity. Here, we report a large-scale study to predict the activity of small molecules across the human kinome—a major family of drug targets, particularly in anti-cancer agents. While small-molecule kinase inhibitors exhibit impressive clinical efficacy in several different diseases, resistance often arises through adaptive kinome reprogramming or subpopulation diversity. Polypharmacology and combination therapies offer potential therapeutic strategies for patients with resistant diseases. Their development would benefit from a more comprehensive and dense knowledge of small-molecule inhibition across the human kinome. Leveraging over 650,000 bioactivity annotations for more than 300,000 small molecules, we evaluated multiple machine learning methods to predict the small-molecule inhibition of 342 kinases across the human kinome. Our results demonstrated that multi-task deep neural networks outperformed classical single-task methods, offering the potential for conducting large-scale virtual screening, predicting activity profiles, and bridging the gaps in the available data. Full article
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