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In Silico Drug Design and Discovery: Big Data for Small...
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In Silico Drug Design and Discovery: Big Data for Small Molecule Design

A special issue of Biomolecules (ISSN 2218-273X). This special issue belongs to the section "Bioinformatics and Systems Biology".

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 24562

Special Issue Editors

Prof. Dr. Antonio Lavecchia

E-Mail Website
Guest Editor
Department of Pharmacy, “Drug Discovery Lab”, University of Naples “Federico II”, Via D. Montesano 49, 80131 Naples, Italy
Interests: drug discovery; medicinal chemistry; molecular modeling; polypharmacology; artificial intelligence; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Carmen Cerchia

E-Mail Website
Guest Editor
Department of Pharmacy, University of Naples “Federico II”, via D. Montesano, 49, 80131 Napoli, Italy
Interests: computer-aided drug design; drug discovery; medicinal chemistry; structure-based drug design; molecular modeling; polypharmacology; data mining
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Life sciences heavily rely on data collected in different ways, for example, through experimental work, medical observations, or computer simulations, to name a few. Advances in novel technologies, such as high-throughput screening and readout, next-generation sequencing, and “-omics” approaches, represent the main drivers of the exponentially increasing amount of data being generated at a fast pace, part of which is available in public databases (e.g., ChEMBL, PubChem, PDB).

Taking advantage of this wealth of information is critical to improve decision making in drug discovery projects. For instance, structure–activity relationships (SARs) can be extracted on a large scale and used to complement chemical optimization efforts. 

Therefore, there is a growing interest in computational approaches to exploit this amount of data and their complexity, including data mining and visualization techniques, predictive models, and machine learning algorithms. 

In this context, this Special Issue has been conceptualized to showcase recent progresses and current trends in the use of in silico approaches leveraging big data and extracting useful knowledge to support all aspects of drug design and discovery. Topics of interest include but are not limited to data mining, molecular modeling, compound bioactivity prediction, and machine learning. Experimental and theoretical research studies are welcome; multidisciplinary approaches are particularly encouraged.

We look forward to your contributions.

Prof. Antonio Lavecchia
Dr. Carmen Cerchia
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. Biomolecules 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 2700 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

  • drug discovery
  • molecular modeling
  • medicinal chemistry
  • chemoinformatics
  • data mining
  • machine learning

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Related Special Issue

Published Papers (7 papers)

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Editorial

Jump to: Research, Review

3 pages, 194 KiB  
Editorial
In Silico Drug Design and Discovery: Big Data for Small Molecule Design
by Carmen Cerchia and Antonio Lavecchia
Biomolecules 2023, 13(1), 44; https://doi.org/10.3390/biom13010044 - 26 Dec 2022
Cited by 1 | Viewed by 1617
Abstract
Across life sciences, the steadily and rapidly increasing amount of data provide new opportunities for advancing knowledge and represent a key driver of emerging technological advancements [...] Full article
(This article belongs to the Special Issue In Silico Drug Design and Discovery: Big Data for Small Molecule Design)

Research

Jump to: Editorial, Review

17 pages, 25298 KiB  
Article
Novel Scaffolds for Modulation of NOD2 Identified by Pharmacophore-Based Virtual Screening
by Samo Guzelj, Tihomir Tomašič and Žiga Jakopin
Biomolecules 2022, 12(8), 1054; https://doi.org/10.3390/biom12081054 - 29 Jul 2022
Cited by 4 | Viewed by 2370
Abstract
Nucleotide-binding oligomerization domain-containing protein 2 (NOD2) is an innate immune pattern recognition receptor responsible for the recognition of bacterial peptidoglycan fragments. Given its central role in the formation of innate and adaptive immune responses, NOD2 represents a valuable target for modulation with agonists [...] Read more.
Nucleotide-binding oligomerization domain-containing protein 2 (NOD2) is an innate immune pattern recognition receptor responsible for the recognition of bacterial peptidoglycan fragments. Given its central role in the formation of innate and adaptive immune responses, NOD2 represents a valuable target for modulation with agonists and antagonists. A major challenge in the discovery of novel small-molecule NOD2 modulators is the lack of a co-crystallized complex with a ligand, which has limited previous progress to ligand-based design approaches and high-throughput screening campaigns. To that end, a hybrid docking and pharmacophore modeling approach was used to identify key interactions between NOD2 ligands and residues in the putative ligand-binding site. Following docking of previously reported NOD2 ligands to a homology model of human NOD2, a structure-based pharmacophore model was created and used to virtually screen a library of commercially available compounds. Two compounds, 1 and 3, identified as hits by the pharmacophore model, exhibited NOD2 antagonist activity and are the first small-molecule NOD2 modulators identified by virtual screening to date. The newly identified NOD2 antagonist scaffolds represent valuable starting points for further optimization. Full article
(This article belongs to the Special Issue In Silico Drug Design and Discovery: Big Data for Small Molecule Design)
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14 pages, 3124 KiB  
Article
SiCoDEA: A Simple, Fast and Complete App for Analyzing the Effect of Individual Drugs and Their Combinations
by Giulio Spinozzi, Valentina Tini, Alessio Ferrari, Ilaria Gionfriddo, Roberta Ranieri, Francesca Milano, Sara Pierangeli, Serena Donnini, Federica Mezzasoma, Serenella Silvestri, Brunangelo Falini and Maria Paola Martelli
Biomolecules 2022, 12(7), 904; https://doi.org/10.3390/biom12070904 - 28 Jun 2022
Cited by 11 | Viewed by 3718
Abstract
The administration of combinations of drugs is a method widely used in the treatment of different pathologies as it can lead to an increase in the therapeutic effect and a reduction in the dose compared to the administration of single drugs. For these [...] Read more.
The administration of combinations of drugs is a method widely used in the treatment of different pathologies as it can lead to an increase in the therapeutic effect and a reduction in the dose compared to the administration of single drugs. For these reasons, it is of interest to study combinations of drugs and to determine whether a specific combination has a synergistic, antagonistic or additive effect. Various mathematical models have been developed, which use different methods to evaluate the synergy of a combination of drugs. We have developed an open access and easy to use app that allows different models to be explored and the most fitting to be chosen for the specific experimental data: SiCoDEA (Single and Combined Drug Effect Analysis). Despite the existence of other tools for drug combination analysis, SiCoDEA remains the most complete and flexible since it offers options such as outlier removal or the ability to choose between different models for analysis. SiCoDEA is an easy to use tool for analyzing drug combination data and to have a view of the various steps and offer different results based on the model chosen. Full article
(This article belongs to the Special Issue In Silico Drug Design and Discovery: Big Data for Small Molecule Design)
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23 pages, 8072 KiB  
Article
In Silico Discovery and Optimisation of a Novel Structural Class of Hsp90 C-Terminal Domain Inhibitors
by Živa Zajec, Jaka Dernovšek, Martina Gobec and Tihomir Tomašič
Biomolecules 2022, 12(7), 884; https://doi.org/10.3390/biom12070884 - 24 Jun 2022
Cited by 9 | Viewed by 2547
Abstract
Hsp90 is a promising target for the development of novel agents for cancer treatment. The N-terminal Hsp90 inhibitors have several therapeutic limitations, the most important of which is the induction of heat shock response, which can be circumvented by targeting the allosteric binding [...] Read more.
Hsp90 is a promising target for the development of novel agents for cancer treatment. The N-terminal Hsp90 inhibitors have several therapeutic limitations, the most important of which is the induction of heat shock response, which can be circumvented by targeting the allosteric binding site on the C-terminal domain (CTD) of Hsp90. In the absence of an Hsp90—CTD inhibitor co-crystal structure, the use of structure-based design approaches for the Hsp90 CTD is difficult and the structural diversity of Hsp90 CTD inhibitors is limited. In this study, we describe the discovery of a novel structural class of Hsp90 CTD inhibitors. A structure-based virtual screening was performed by docking a library of diverse compounds to the Hsp90β CTD binding site. Three selected virtual hits were tested in the MCF-7 breast cancer cell line, with compound TVS-23 showing antiproliferative activity with an IC50 value of 26.4 ± 1.1 µM. We report here the optimisation, synthesis and biological evaluation of TVS-23 analogues. Several analogues showed significantly enhanced antiproliferative activities in MCF-7 breast cancer and SK-N-MC Ewing sarcoma cell lines, with 7l being the most potent (IC50 = 1.4 ± 0.4 µM MCF-7; IC50 = 2.8 ± 0.4 µM SK-N-MC). The results of this study highlight the use of virtual screening to expand the structural diversity of Hsp90 CTD inhibitors and provide new starting points for further development. Full article
(This article belongs to the Special Issue In Silico Drug Design and Discovery: Big Data for Small Molecule Design)
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23 pages, 3608 KiB  
Article
Feature Reduction for Molecular Similarity Searching Based on Autoencoder Deep Learning
by Maged Nasser, Naomie Salim, Faisal Saeed, Shadi Basurra, Idris Rabiu, Hentabli Hamza and Muaadh A. Alsoufi
Biomolecules 2022, 12(4), 508; https://doi.org/10.3390/biom12040508 - 27 Mar 2022
Cited by 8 | Viewed by 2743
Abstract
The concept of molecular similarity has been commonly used in rational drug design, where structurally similar molecules are examined in molecular databases to retrieve functionally similar molecules. The most used conventional similarity methods used two-dimensional (2D) fingerprints to evaluate the similarity of molecules [...] Read more.
The concept of molecular similarity has been commonly used in rational drug design, where structurally similar molecules are examined in molecular databases to retrieve functionally similar molecules. The most used conventional similarity methods used two-dimensional (2D) fingerprints to evaluate the similarity of molecules towards a target query. However, these descriptors include redundant and irrelevant features that might impact the performance of similarity searching methods. Thus, this study proposed a new approach for identifying the important features of molecules in chemical datasets based on the representation of the molecular features using Autoencoder (AE), with the aim of removing irrelevant and redundant features. The proposed approach experimented using the MDL Data Drug Report standard dataset (MDDR). Based on experimental findings, the proposed approach performed better than several existing benchmark similarity methods such as Tanimoto Similarity Method (TAN), Adapted Similarity Measure of Text Processing (ASMTP), and Quantum-Based Similarity Method (SQB). The results demonstrated that the performance achieved by the proposed approach has proven to be superior, particularly with the use of structurally heterogeneous datasets, where it yielded improved results compared to other previously used methods with the similar goal of improving molecular similarity searching. Full article
(This article belongs to the Special Issue In Silico Drug Design and Discovery: Big Data for Small Molecule Design)
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22 pages, 8704 KiB  
Article
Towards the De Novo Design of HIV-1 Protease Inhibitors Based on Natural Products
by Ana L. Chávez-Hernández, K. Eurídice Juárez-Mercado, Fernanda I. Saldívar-González and José L. Medina-Franco
Biomolecules 2021, 11(12), 1805; https://doi.org/10.3390/biom11121805 - 1 Dec 2021
Cited by 9 | Viewed by 4546
Abstract
Acquired immunodeficiency syndrome (AIDS) caused by the human immunodeficiency virus (HIV) continues to be a public health problem. In 2020, 680,000 people died from HIV-related causes, and 1.5 million people were infected. Antiretrovirals are a way to control HIV infection but not to [...] Read more.
Acquired immunodeficiency syndrome (AIDS) caused by the human immunodeficiency virus (HIV) continues to be a public health problem. In 2020, 680,000 people died from HIV-related causes, and 1.5 million people were infected. Antiretrovirals are a way to control HIV infection but not to cure AIDS. As such, effective treatment must be developed to control AIDS. Developing a drug is not an easy task, and there is an enormous amount of work and economic resources invested. For this reason, it is highly convenient to employ computer-aided drug design methods, which can help generate and identify novel molecules. Using the de novo design, novel molecules can be developed using fragments as building blocks. In this work, we develop a virtual focused compound library of HIV-1 viral protease inhibitors from natural product fragments. Natural products are characterized by a large diversity of functional groups, many sp3 atoms, and chiral centers. Pseudo-natural products are a combination of natural products fragments that keep the desired structural characteristics from different natural products. An interactive version of chemical space visualization of virtual compounds focused on HIV-1 viral protease inhibitors from natural product fragments is freely available in the supplementary material. Full article
(This article belongs to the Special Issue In Silico Drug Design and Discovery: Big Data for Small Molecule Design)
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Review

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14 pages, 2377 KiB  
Review
Saquinavir: From HIV to COVID-19 and Cancer Treatment
by Mariana Pereira and Nuno Vale
Biomolecules 2022, 12(7), 944; https://doi.org/10.3390/biom12070944 - 5 Jul 2022
Cited by 23 | Viewed by 5213
Abstract
Saquinavir was the first protease inhibitor developed for HIV therapy, and it changed the standard of treatment for this disease to a combination of drugs that ultimately led to increased survival of this otherwise deadly condition. Inhibiting the HIV protease impedes the virus [...] Read more.
Saquinavir was the first protease inhibitor developed for HIV therapy, and it changed the standard of treatment for this disease to a combination of drugs that ultimately led to increased survival of this otherwise deadly condition. Inhibiting the HIV protease impedes the virus from maturing and replicating. With this in mind, since the start of the COVID-19 outbreak, the research for already approved drugs (mainly antivirals) to repurpose for treatment of this disease has increased. Among the drugs tested, saquinavir showed promise in silico and in vitro in the inhibition of the SARS-CoV-2 main protease (3CLpro). Another field for saquinavir repurposing has been in anticancer treatment, in which it has shown effects in vitro and in vivo in several types of cancer, from Kaposi carcinoma to neuroblastoma, demonstrating cytotoxicity, apoptosis, inhibition of cell invasion, and improvement of radiosensibility of cancer cells. Despite the lack of follow-up in clinical trials for cancer use, there has been a renewed interest in this drug recently due to COVID-19, which shows similar pharmacological pathways and has developed superior in silico models that can be translated to oncologic research. This could help further testing and future approval of saquinavir repurposing for cancer treatment. Full article
(This article belongs to the Special Issue In Silico Drug Design and Discovery: Big Data for Small Molecule Design)
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