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Trends and Applications in Computationally Driven Drug Repurposing

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 (15 June 2023) | Viewed by 15059

Special Issue Editors

Department of Life Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125 Modena, Italy
Interests: computational approaches; drug discovery; drug design; molecular modeling; computational chemistry; virtual screening
Special Issues, Collections and Topics in MDPI journals
Department of Life Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125 Modena, Italy
Interests: computational approaches; drug discovery; drug design; molecular modeling; computational chemistry; virtual screening
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Drug repurposing, which represents one among the most attractive approaches in modern drug discovery, allows the identification of novel targets and therapeutic applications to already known drugs. Such approach provides several advantages as reduced time and costs compared to traditional drug discovery, allowing to circumvent potential in vitro and in vivo efficacy and ADMET (absorption, distribution, metabolism, excretion and toxicity) issues. In recent years, the application of computational approaches to drug repurposing has received greatest interest from the research community, this being fueled also by the ever-growing source of information reported in public databases and the availability of big data techniques.

This Special Issue aims at focusing on the most recent trends to computationally driven drug repurposing, welcoming selected reviews and original research contributions on:

  • the application of artificial intelligence, molecular modeling and chemoinformatics techniques for the repositioning of known molecules toward novel targets and therapeutic indications;
  • the integration of artificial intelligence methods with molecular modeling and chemoinformatics for drug repositioning and target identification;
  • the integration of real-world information into databases, platforms and innovative in silico strategies for drug repositioning;
  • applicability, predictivity and benchmarking of drug repurposing models and pipelines;
  • target-centric and phenotypic-centric drug repurposing approaches;
  • metabolic networks and their relevance in drug repurposing.

Dr. Luca Pinzi
Prof. Dr. Giulio Rastelli
Guest Editors

Manuscript Submission Information

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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 repurposing
  • target identification
  • artificial intelligence
  • machine learning
  • deep learning
  • molecular modelling
  • chemoinformatics
  • big data
  • medicinal chemistry

Published Papers (6 papers)

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Editorial

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4 pages, 208 KiB  
Editorial
Trends and Applications in Computationally Driven Drug Repurposing
by Luca Pinzi and Giulio Rastelli
Int. J. Mol. Sci. 2023, 24(22), 16511; https://doi.org/10.3390/ijms242216511 - 20 Nov 2023
Viewed by 562
Abstract
Drug repurposing is a widely used approach originally developed to aid in the identification of new uses of already existing drugs outside the scope of the original medical indication [...] Full article
(This article belongs to the Special Issue Trends and Applications in Computationally Driven Drug Repurposing)

Research

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23 pages, 6907 KiB  
Article
Disruption of Irisin Dimerization by FDA-Approved Drugs: A Computational Repurposing Approach for the Potential Treatment of Lipodystrophy Syndromes
by Lorenzo Flori, Simone Brogi, Hajar Sirous and Vincenzo Calderone
Int. J. Mol. Sci. 2023, 24(8), 7578; https://doi.org/10.3390/ijms24087578 - 20 Apr 2023
Cited by 2 | Viewed by 1348
Abstract
In this paper, we present the development of a computer-based repurposing approach to identify FDA-approved drugs that are potentially able to interfere with irisin dimerization. It has been established that altered levels of irisin dimers are a pure hallmark of lipodystrophy (LD) syndromes. [...] Read more.
In this paper, we present the development of a computer-based repurposing approach to identify FDA-approved drugs that are potentially able to interfere with irisin dimerization. It has been established that altered levels of irisin dimers are a pure hallmark of lipodystrophy (LD) syndromes. Accordingly, the identification of compounds capable of slowing down or precluding the irisin dimers’ formation could represent a valuable therapeutic strategy in LD. Combining several computational techniques, we identified five FDA-approved drugs with satisfactory computational scores (iohexol, XP score = −7.70 kcal/mol, SP score = −5.5 kcal/mol, ΔGbind = −61.47 kcal/mol, ΔGbind (average) = −60.71 kcal/mol; paromomycin, XP score = −7.23 kcal/mol, SP score = −6.18 kcal/mol, ΔGbind = −50.14 kcal/mol, ΔGbind (average) = −49.13 kcal/mol; zoledronate, XP score = −6.33 kcal/mol, SP score = −5.53 kcal/mol, ΔGbind = −32.38 kcal/mol, ΔGbind (average) = −29.42 kcal/mol; setmelanotide, XP score = −6.10 kcal/mol, SP score = −7.24 kcal/mol, ΔGbind = −56.87 kcal/mol, ΔGbind (average) = −62.41 kcal/mol; and theophylline, XP score = −5.17 kcal/mol, SP score = −5.55 kcal/mol, ΔGbind = −33.25 kcal/mol, ΔGbind (average) = −35.29 kcal/mol) that are potentially able to disrupt the dimerization of irisin. For this reason, they deserve further investigation to characterize them as irisin disruptors. Remarkably, the identification of drugs targeting this process can offer novel therapeutic opportunities for the treatment of LD. Furthermore, the identified drugs could provide a starting point for a repositioning approach, synthesizing novel analogs with improved efficacy and selectivity against the irisin dimerization process. Full article
(This article belongs to the Special Issue Trends and Applications in Computationally Driven Drug Repurposing)
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23 pages, 4392 KiB  
Article
Identification of Promising Drug Candidates against Prostate Cancer through Computationally-Driven Drug Repurposing
by Leonardo Bernal, Luca Pinzi and Giulio Rastelli
Int. J. Mol. Sci. 2023, 24(4), 3135; https://doi.org/10.3390/ijms24043135 - 05 Feb 2023
Cited by 1 | Viewed by 1847
Abstract
Prostate cancer (PC) is one of the most common types of cancer in males. Although early stages of PC are generally associated with favorable outcomes, advanced phases of the disease present a significantly poorer prognosis. Moreover, currently available therapeutic options for the treatment [...] Read more.
Prostate cancer (PC) is one of the most common types of cancer in males. Although early stages of PC are generally associated with favorable outcomes, advanced phases of the disease present a significantly poorer prognosis. Moreover, currently available therapeutic options for the treatment of PC are still limited, being mainly focused on androgen deprivation therapies and being characterized by low efficacy in patients. As a consequence, there is a pressing need to identify alternative and more effective therapeutics. In this study, we performed large-scale 2D and 3D similarity analyses between compounds reported in the DrugBank database and ChEMBL molecules with reported anti-proliferative activity on various PC cell lines. The analyses included also the identification of biological targets of ligands with potent activity on PC cells, as well as investigations on the activity annotations and clinical data associated with the more relevant compounds emerging from the ligand-based similarity results. The results led to the prioritization of a set of drugs and/or clinically tested candidates potentially useful in drug repurposing against PC. Full article
(This article belongs to the Special Issue Trends and Applications in Computationally Driven Drug Repurposing)
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26 pages, 9411 KiB  
Article
Exploiting ELIOT for Scaffold-Repurposing Opportunities: TRIM33 a Possible Novel E3 Ligase to Expand the Toolbox for PROTAC Design
by Tommaso Palomba, Giusy Tassone, Carmine Vacca, Matteo Bartalucci, Aurora Valeri, Cecilia Pozzi, Simon Cross, Lydia Siragusa and Jenny Desantis
Int. J. Mol. Sci. 2022, 23(22), 14218; https://doi.org/10.3390/ijms232214218 - 17 Nov 2022
Cited by 3 | Viewed by 2392
Abstract
The field of targeted protein degradation, through the control of the ubiquitin–proteasome system (UPS), is progressing considerably; to exploit this new therapeutic modality, the proteolysis targeting chimera (PROTAC) technology was born. The opportunity to use PROTACs engaging of new E3 ligases that can [...] Read more.
The field of targeted protein degradation, through the control of the ubiquitin–proteasome system (UPS), is progressing considerably; to exploit this new therapeutic modality, the proteolysis targeting chimera (PROTAC) technology was born. The opportunity to use PROTACs engaging of new E3 ligases that can hijack and control the UPS system could greatly extend the applicability of degrading molecules. To this end, here we show a potential application of the ELIOT (E3 LIgase pocketOme navigaTor) platform, previously published by this group, for a scaffold-repurposing strategy to identify new ligands for a novel E3 ligase, such as TRIM33. Starting from ELIOT, a case study of the cross-relationship using GRID Molecular Interaction Field (MIF) similarities between TRIM24 and TRIM33 binding sites was selected. Based on the assumption that similar pockets could bind similar ligands and considering that TRIM24 has 12 known co-crystalised ligands, we applied a scaffold-repurposing strategy for the identification of TRIM33 ligands exploiting the scaffold of TRIM24 ligands. We performed a deeper computational analysis to identify pocket similarities and differences, followed by docking and water analysis; selected ligands were synthesised and subsequently tested against TRIM33 via HTRF binding assay, and we obtained the first-ever X-ray crystallographic complexes of TRIM33α with three of the selected compounds. Full article
(This article belongs to the Special Issue Trends and Applications in Computationally Driven Drug Repurposing)
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16 pages, 4406 KiB  
Article
Identification of Diosmin and Flavin Adenine Dinucleotide as Repurposing Treatments for Monkeypox Virus: A Computational Study
by Thua-Phong Lam, Viet-Hung Tran, Tan Thanh Mai, Nghia Vo-Trong Lai, Bao-Tran Ngoc Dang, Minh-Tri Le, Thanh-Dao Tran, Dieu-Thuong Thi Trinh and Khac-Minh Thai
Int. J. Mol. Sci. 2022, 23(19), 11570; https://doi.org/10.3390/ijms231911570 - 30 Sep 2022
Cited by 11 | Viewed by 2965
Abstract
The World Health Organization declared monkeypox a global public health emergency on 23 July 2022. This disease was caused by the monkeypox virus (MPXV), which was first identified in 1958 in Denmark. The MPXV is a member of the Poxviridae family, the Chordopoxvirinae [...] Read more.
The World Health Organization declared monkeypox a global public health emergency on 23 July 2022. This disease was caused by the monkeypox virus (MPXV), which was first identified in 1958 in Denmark. The MPXV is a member of the Poxviridae family, the Chordopoxvirinae subfamily, and the genus Orthopoxvirus, which share high similarities with the vaccinia virus (the virus used to produce the smallpox vaccine). For the initial stage of infection, the MPXV needs to attach to the human cell surface glycosaminoglycan (GAG) adhesion molecules using its E8 protein. However, up until now, neither a structure for the MPXV E8 protein nor a specific cure for the MPXV exists. This study aimed to search for small molecules that inhibit the MPXV E8 protein, using computational approaches. In this study, a high-quality three-dimensional structure of the MPXV E8 protein was retrieved by homology modeling using the AlphaFold deep learning server. Subsequent molecular docking and molecular dynamics simulations (MDs) for a cumulative duration of 2.1 microseconds revealed that ZINC003977803 (Diosmin) and ZINC008215434 (Flavin adenine dinucleotide-FAD) could be potential inhibitors against the E8 protein with the MM/GBSA binding free energies of −38.19 ± 9.69 and −35.59 ± 7.65 kcal·mol−1, respectively. Full article
(This article belongs to the Special Issue Trends and Applications in Computationally Driven Drug Repurposing)
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Review

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21 pages, 2361 KiB  
Review
Recent Studies of Artificial Intelligence on In Silico Drug Distribution Prediction
by Thi Tuyet Van Tran, Hilal Tayara and Kil To Chong
Int. J. Mol. Sci. 2023, 24(3), 1815; https://doi.org/10.3390/ijms24031815 - 17 Jan 2023
Cited by 3 | Viewed by 4878
Abstract
Drug distribution is an important process in pharmacokinetics because it has the potential to influence both the amount of medicine reaching the active sites and the effectiveness as well as safety of the drug. The main causes of 90% of drug failures in [...] Read more.
Drug distribution is an important process in pharmacokinetics because it has the potential to influence both the amount of medicine reaching the active sites and the effectiveness as well as safety of the drug. The main causes of 90% of drug failures in clinical development are lack of efficacy and uncontrolled toxicity. In recent years, several advances and promising developments in drug distribution property prediction have been achieved, especially in silico, which helped to drastically reduce the time and expense of screening undesired drug candidates. In this study, we provide comprehensive knowledge of drug distribution background, influencing factors, and artificial intelligence-based distribution property prediction models from 2019 to the present. Additionally, we gathered and analyzed public databases and datasets commonly utilized by the scientific community for distribution prediction. The distribution property prediction performance of five large ADMET prediction tools is mentioned as a benchmark for future research. On this basis, we also offer future challenges in drug distribution prediction and research directions. We hope that this review will provide researchers with helpful insight into distribution prediction, thus facilitating the development of innovative approaches for drug discovery. Full article
(This article belongs to the Special Issue Trends and Applications in Computationally Driven Drug Repurposing)
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