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Application and Latest Progress of Bioinformatics in Drug Discovery

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

Deadline for manuscript submissions: closed (20 October 2024) | Viewed by 3887

Special Issue Editor


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Guest Editor
Department of Biotechnology, Chemistry, and Pharmacy, University of Siena, Siena, Italy
Interests: molecular modeling; molecular dynamics and docking simulation; structural biology

Special Issue Information

Dear Colleagues,

The International Journal of Molecular Sciences is pleased to announce a Special Issue aiming to highlight and improve knowledge on the latest developments of bioinformatics applications in the drug discovery field.

The recent advancements in bioinformatics methods have revolutionized several scientific fields, providing researchers with novel approaches to manage and analyze an impressive amount of biological data, allowing for increasingly accurate predictions for drug discovery and the development of newer leads for therapeutic approaches.

As a Guest Editor of this Special Issue, I cordially invite scholars and experts to submit original research, reviews and perspectives on the advancements in computational methods aimed to explore new insights and innovative approaches to improve our understanding of bioinformatics-based drug discovery.

Areas of interest include, but are not limited to, the following:

  • Developments and applications of bioinformatics methods in the prediction and validation of novel selective drugs and drug targets;
  • Developments and applications of structure/ligand-based virtual screening methods in silico;
  • Developments and applications of data mining, machine learning, and artificial intelligence approaches for precision medicine and drug discovery;
  • Repurposing and repositioning of drugs with bioinformatics approaches;
  • Bioinformatics approaches for rare disease.

Dr. Alfonso Trezza
Guest Editor

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.

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Keywords

  • molecular dynamics and docking simulation
  • molecular modeling
  • target and drug discovery
  • artificial intelligence
  • big data

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Published Papers (3 papers)

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Research

9 pages, 3322 KiB  
Communication
Molecular Origins of the Mendelian Rare Diseases Reviewed by Orpha.net: A Structural Bioinformatics Investigation
by Anna Visibelli, Rebecca Finetti, Neri Niccolai, Ottavia Spiga and Annalisa Santucci
Int. J. Mol. Sci. 2024, 25(13), 6953; https://doi.org/10.3390/ijms25136953 - 25 Jun 2024
Viewed by 1038
Abstract
The study of rare diseases is important not only for the individuals affected but also for the advancement of medical knowledge and a deeper understanding of human biology and genetics. The wide repertoire of structural information now available from reliable and accurate prediction [...] Read more.
The study of rare diseases is important not only for the individuals affected but also for the advancement of medical knowledge and a deeper understanding of human biology and genetics. The wide repertoire of structural information now available from reliable and accurate prediction methods provides the opportunity to investigate the molecular origins of most of the rare diseases reviewed in the Orpha.net database. Thus, it has been possible to analyze the topology of the pathogenic missense variants found in the 2515 proteins involved in Mendelian rare diseases (MRDs), which form the database for our structural bioinformatics study. The amino acid substitutions responsible for MRDs showed different mutation site distributions at different three-dimensional protein depths. We then highlighted the depth-dependent effects of pathogenic variants for the 20,061 pathogenic variants that are present in our database. The results of this structural bioinformatics investigation are relevant, as they provide additional clues to mitigate the damage caused by MRD. Full article
(This article belongs to the Special Issue Application and Latest Progress of Bioinformatics in Drug Discovery)
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40 pages, 2408 KiB  
Article
G Protein-Coupled Receptor–Ligand Pose and Functional Class Prediction
by Gregory L. Szwabowski, Makenzie Griffing, Elijah J. Mugabe, Daniel O’Malley, Lindsey N. Baker, Daniel L. Baker and Abby L. Parrill
Int. J. Mol. Sci. 2024, 25(13), 6876; https://doi.org/10.3390/ijms25136876 - 22 Jun 2024
Viewed by 1235
Abstract
G protein-coupled receptor (GPCR) transmembrane protein family members play essential roles in physiology. Numerous pharmaceuticals target GPCRs, and many drug discovery programs utilize virtual screening (VS) against GPCR targets. Improvements in the accuracy of predicting new molecules that bind to and either activate [...] Read more.
G protein-coupled receptor (GPCR) transmembrane protein family members play essential roles in physiology. Numerous pharmaceuticals target GPCRs, and many drug discovery programs utilize virtual screening (VS) against GPCR targets. Improvements in the accuracy of predicting new molecules that bind to and either activate or inhibit GPCR function would accelerate such drug discovery programs. This work addresses two significant research questions. First, do ligand interaction fingerprints provide a substantial advantage over automated methods of binding site selection for classical docking? Second, can the functional status of prospective screening candidates be predicted from ligand interaction fingerprints using a random forest classifier? Ligand interaction fingerprints were found to offer modest advantages in sampling accurate poses, but no substantial advantage in the final set of top-ranked poses after scoring, and, thus, were not used in the generation of the ligand–receptor complexes used to train and test the random forest classifier. A binary classifier which treated agonists, antagonists, and inverse agonists as active and all other ligands as inactive proved highly effective in ligand function prediction in an external test set of GPR31 and TAAR2 candidate ligands with a hit rate of 82.6% actual actives within the set of predicted actives. Full article
(This article belongs to the Special Issue Application and Latest Progress of Bioinformatics in Drug Discovery)
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13 pages, 3066 KiB  
Article
A Drug Discovery Approach to a Reveal Novel Antioxidant Natural Source: The Case of Chestnut Burr Biomass
by Alfonso Trezza, Michela Geminiani, Giuseppe Cutrera, Elena Dreassi, Luisa Frusciante, Stefania Lamponi, Ottavia Spiga and Annalisa Santucci
Int. J. Mol. Sci. 2024, 25(5), 2517; https://doi.org/10.3390/ijms25052517 - 21 Feb 2024
Cited by 5 | Viewed by 1020
Abstract
Currently, many environmental and energy-related problems are threatening the future of our planet. In October 2022, the Worldmeter recorded the world population as 7.9 billion people, estimating that there will be an increase of 2 billion by 2057. The rapid growth of the [...] Read more.
Currently, many environmental and energy-related problems are threatening the future of our planet. In October 2022, the Worldmeter recorded the world population as 7.9 billion people, estimating that there will be an increase of 2 billion by 2057. The rapid growth of the population and the continuous increase in needs are causing worrying conditions, such as pollution, climate change, global warming, waste disposal, and natural resource reduction. Looking for novel and innovative methods to overcome these global troubles is a must for our common welfare. The circular bioeconomy represents a promising strategy to alleviate the current conditions using biomass-like natural wastes to replace commercial products that have a negative effect on our ecological footprint. Applying the circular bioeconomy concept, we propose an integrated in silico and in vitro approach to identify antioxidant bioactive compounds extracted from chestnut burrs (an agroforest waste) and their potential biological targets. Our study provides a novel and robust strategy developed within the circular bioeconomy concept aimed at target and drug discovery for a wide range of diseases. Our study could open new frontiers in the circular bioeconomy related to target and drug discovery, offering new ideas for sustainable scientific research aimed at identifying novel therapeutical strategies. Full article
(This article belongs to the Special Issue Application and Latest Progress of Bioinformatics in Drug Discovery)
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