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Special Issue "Computational Methods in Drug Design and Food Chemistry"

A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Medicinal Chemistry".

Deadline for manuscript submissions: 30 December 2020.

Special Issue Editor

Dr. Giosuè Costa
Website
Guest Editor
1. Department of Health Sciences, “Magna Græcia” University of Catanzaro, 88100, Catanzaro, Italy
2. Net4Science Academic Spin-Off, “Magna Græcia” University of Catanzaro, 88100, Catanzaro, Italy
Interests: medicinal chemistry; food chemistry; natural products; computational chemistry; in silico virtual screening; computer-aided drug design; structure-based drug repurposing and discovery; multi-target rational drug design

Special Issue Information

Dear Colleagues,

Today, the contribution of computational methodologies to drug discovery is no longer in doubt, and all major world pharmaceutical, academic, and biotechnology companies use computational design tools. Computer-aided drug design includes computational methods and resources that are used to facilitate the design and discovery of new bioactive chemical entities, including natural compounds with potentially nutraceutical activity.

The confirmation of the usefulness of these methodologies came in 2013, when the Nobel prize for chemistry was awarded to Martin Karplus, Michael Levitt, and Arieh Warshel “for the development of multiscale models for complex chemical systems”; thus, from this point of view, chemistry is an experimental science, but theoretical chemists are providing answers to questions about how to design drugs to fit with their target molecules.

In this Special Issue, we encourage authors to submit manuscripts in the form of a research paper, review, or communication that contributes positively in each aspect of medicinal chemistry and drug discovery, from the design of high-throughput screening libraries to providing estimations of the molecular properties required for drug molecules, improving our understanding of how they interact with biological targets of pharmaceutical interest.

This Special Issue will accept original research papers, high-quality reviews, and communications in the field of computational methods in drug design and food chemistry.

Dr. Giosuè Costa
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 papers will be 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. Molecules is an international peer-reviewed open access semimonthly 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 2000 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

  • Molecular docking and structure-based virtual screening
  • Fragment-based drug design
  • Advances in molecular dynamics simulations and free-energy calculations applicable in drug design
  • QM applications in drug discovery
  • Pharmacophore modeling
  • In silico absorption, distribution, metabolism, and excretion (ADME)
  • Computational methods for drug target profiling and polypharmacology
  • Integrating structure- and ligand-based approaches for computer-aided drug design
  • Multi-target rational drug design
  • Computer-aided drug repurposing
  • In silico toxicology
  • Database of natural compounds: implementation and searching

Published Papers (11 papers)

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Research

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Open AccessArticle
Five Novel Non-Sialic Acid-Like Scaffolds Inhibit In Vitro H1N1 and H5N2 Neuraminidase Activity of Influenza a Virus
Molecules 2020, 25(18), 4248; https://doi.org/10.3390/molecules25184248 - 16 Sep 2020
Abstract
Neuraminidase (NA) of influenza viruses enables the virus to access the cell membrane. It degrades the sialic acid contained in extracellular mucin. Later, it is responsible for releasing newly formed virions from the membrane of infected cells. Both processes become key functions within [...] Read more.
Neuraminidase (NA) of influenza viruses enables the virus to access the cell membrane. It degrades the sialic acid contained in extracellular mucin. Later, it is responsible for releasing newly formed virions from the membrane of infected cells. Both processes become key functions within the viral cycle. Therefore, it is a therapeutic target for research of the new antiviral agents. Structure–activity relationships studies have revealed which are the important functional groups for the receptor–ligand interaction. Influenza virus type A NA activity was inhibited by five scaffolds without structural resemblance to sialic acid. Intending small organic compound repositioning along with drug repurposing, this study combined in silico simulations of ligand docking into the known binding site of NA, along with in vitro bioassays. The five proposed scaffolds are N-acetylphenylalanylmethionine, propanoic 3-[(2,5-dimethylphenyl) carbamoyl]-2-(piperazin-1-yl) acid, 3-(propylaminosulfonyl)-4-chlorobenzoic acid, ascorbic acid (vitamin C), and 4-(dipropylsulfamoyl) benzoic acid (probenecid). Their half maximal inhibitory concentration (IC50) was determined through fluorometry. An acidic reagent 2′-O-(4-methylumbelliferyl)-α-dN-acetylneuraminic acid (MUNANA) was used as substrate for viruses of human influenza H1N1 or avian influenza H5N2. Inhibition was observed in millimolar ranges in a concentration-dependent manner. The IC50 values of the five proposed scaffolds ranged from 6.4 to 73 mM. The values reflect a significant affinity difference with respect to the reference drug zanamivir (p < 0.001). Two compounds (N-acetyl dipeptide and 4-substituted benzoic acid) clearly showed competitive mechanisms, whereas ascorbic acid reflected non-competitive kinetics. The five small organic molecules constitute five different scaffolds with moderate NA affinities. They are proposed as lead compounds for developing new NA inhibitors which are not analogous to sialic acid. Full article
(This article belongs to the Special Issue Computational Methods in Drug Design and Food Chemistry)
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Open AccessArticle
Probing the Highly Disparate Dual Inhibitory Mechanisms of Novel Quinazoline Derivatives against Mycobacterium tuberculosis Protein Kinases A and B
Molecules 2020, 25(18), 4247; https://doi.org/10.3390/molecules25184247 - 16 Sep 2020
Abstract
Mycobacterium tuberculosis (Mtb) serine/threonine (Ser/Thr) Protein kinases A (PknA) and B (PknB) have been identified as highly attractive targets for overcoming drug resistant tuberculosis. A recent lead series optimization study yielded compound 33 which exhibited potencies ~1000 times higher than compound [...] Read more.
Mycobacterium tuberculosis (Mtb) serine/threonine (Ser/Thr) Protein kinases A (PknA) and B (PknB) have been identified as highly attractive targets for overcoming drug resistant tuberculosis. A recent lead series optimization study yielded compound 33 which exhibited potencies ~1000 times higher than compound 57. This huge discrepancy left us curious to investigate the mechanistic ‘dual’ (in)activities of the compound using computational methods, as carried out in this study. Findings revealed that 33 stabilized the PknA and B conformations and reduced their structural activities relative to 57. Optimal stability of 33 in the hydrophobic pockets further induced systemic alterations at the P-loops, catalytic loops, helix Cs and DFG motifs of PknA and B. Comparatively, 57 was more surface-bound with highly unstable motions. Furthermore, 33 demonstrated similar binding patterns in PknA and B, involving conserved residues of their binding pockets. Both π and hydrogen interactions played crucial roles in the binding of 33, which altogether culminated in high ΔGs for both proteins. On the contrary, the binding of 57 was characterized by unfavorable interactions with possible repulsive effects on its optimal dual binding to both proteins, as evidenced by the relatively lowered ΔGs. These findings would significantly contribute to the rational structure-based design of novel and highly selective dual inhibitors of Mtb PknA and B. Full article
(This article belongs to the Special Issue Computational Methods in Drug Design and Food Chemistry)
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Open AccessFeature PaperArticle
Computational Method for Quantitative Comparison of Activity Landscapes on the Basis of Image Data
Molecules 2020, 25(17), 3952; https://doi.org/10.3390/molecules25173952 - 29 Aug 2020
Abstract
Activity landscape (AL) models are used for visualizing and interpreting structure–activity relationships (SARs) in compound datasets. Therefore, ALs are designed to present chemical similarity and compound potency information in context. Different two- or three-dimensional (2D or 3D) AL representations have been introduced. For [...] Read more.
Activity landscape (AL) models are used for visualizing and interpreting structure–activity relationships (SARs) in compound datasets. Therefore, ALs are designed to present chemical similarity and compound potency information in context. Different two- or three-dimensional (2D or 3D) AL representations have been introduced. For SAR analysis, 3D AL models are particularly intuitive. In these models, an interpolated potency surface is added as a third dimension to a 2D projection of chemical space. Accordingly, AL topology can be associated with characteristic SAR features. Going beyond visualization and a qualitative assessment of SARs, it would be very helpful to compare 3D ALs of different datasets in more quantitative terms. However, quantitative AL analysis is still in its infancy. Recently, it has been shown that 3D AL models with pre-defined topologies can be correctly classified using machine learning. Classification was facilitated on the basis of AL image feature representations learned with convolutional neural networks. Therefore, we have further investigated image analysis for quantitative comparison of 3D ALs and devised an approach to determine (dis)similarity relationships for ALs representing different compound datasets. Herein, we report this approach and demonstrate proof-of-principle. The methodology makes it possible to computationally compare 3D ALs and quantify topological differences reflecting varying SAR information content. For SAR exploration in drug design, this adds a quantitative measure of AL (dis)similarity to graphical analysis. Full article
(This article belongs to the Special Issue Computational Methods in Drug Design and Food Chemistry)
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Open AccessArticle
Literature-Wide Association Studies (LWAS) for a Rare Disease: Drug Repurposing for Inflammatory Breast Cancer
Molecules 2020, 25(17), 3933; https://doi.org/10.3390/molecules25173933 - 28 Aug 2020
Abstract
Drug repurposing is an effective means for rapid drug discovery. The aim of this study was to develop and validate a computational methodology based on Literature-Wide Association Studies (LWAS) of PubMed to repurpose existing drugs for a rare inflammatory breast cancer (IBC). We [...] Read more.
Drug repurposing is an effective means for rapid drug discovery. The aim of this study was to develop and validate a computational methodology based on Literature-Wide Association Studies (LWAS) of PubMed to repurpose existing drugs for a rare inflammatory breast cancer (IBC). We have developed a methodology that conducted LWAS based on the text mining technology Word2Vec. 3.80 million “cancer”-related PubMed abstracts were processed as the corpus for Word2Vec to derive vector representation of biological concepts. These vectors for drugs and diseases served as the foundation for creating similarity maps of drugs and diseases, respectively, which were then employed to find potential therapy for IBC. Three hundred and thirty-six (336) known drugs and three hundred and seventy (370) diseases were expressed as vectors in this study. Nine hundred and seventy (970) previously known drug-disease association pairs among these drugs and diseases were used as the reference set. Based on the hypothesis that similar drugs can be used against similar diseases, we have identified 18 diseases similar to IBC, with 24 corresponding known drugs proposed to be the repurposing therapy for IBC. The literature search confirmed most known drugs tested for IBC, with four of them being novel candidates. We conclude that LWAS based on the Word2Vec technology is a novel approach to drug repurposing especially useful for rare diseases. Full article
(This article belongs to the Special Issue Computational Methods in Drug Design and Food Chemistry)
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Open AccessArticle
Alkylated Sesamol Derivatives as Potent Antioxidants
Molecules 2020, 25(14), 3300; https://doi.org/10.3390/molecules25143300 - 21 Jul 2020
Cited by 1
Abstract
Sesamol is a phenolic derivative. Its antioxidant activity is low than that of Trolox and depends on benzodioxole moiety. Thus, a molecular modification strategy through alkylation, inspired by natural and synthetic antioxidants, was studied by molecular modeling at the DFT/B3LYP level of theory [...] Read more.
Sesamol is a phenolic derivative. Its antioxidant activity is low than that of Trolox and depends on benzodioxole moiety. Thus, a molecular modification strategy through alkylation, inspired by natural and synthetic antioxidants, was studied by molecular modeling at the DFT/B3LYP level of theory by comparing the 6-31+G(d,p) and 6-311++G(2d,2p) basis sets. All proposed derivatives were compared to classical related antioxidants such as Trolox, t-butylated hydroxytoluene (BHT) and t-butylated hydroxyanisole (BHA). According to our results, molecular orbitals, single electron or hydrogen-atom transfers, spin density distributions, and alkyl substitutions at the ortho positions related to phenol moiety were found to be more effective than any other positions. The trimethylated derivative was more potent than Trolox. t-Butylated derivatives were stronger than all other alkylated derivatives and may be new alternative forms of modified antioxidants from natural products with applications in the chemical, pharmaceutical, and food industries. Full article
(This article belongs to the Special Issue Computational Methods in Drug Design and Food Chemistry)
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Open AccessArticle
Computational Methods for the Identification of Molecular Targets of Toxic Food Additives. Butylated Hydroxytoluene as a Case Study
Molecules 2020, 25(9), 2229; https://doi.org/10.3390/molecules25092229 - 09 May 2020
Abstract
Butylated hydroxytoluene (BHT) is one of the most commonly used synthetic antioxidants in food, cosmetic, pharmaceutical and petrochemical products. BHT is considered safe for human health; however, its widespread use together with the potential toxicological effects have increased consumers concern about the use [...] Read more.
Butylated hydroxytoluene (BHT) is one of the most commonly used synthetic antioxidants in food, cosmetic, pharmaceutical and petrochemical products. BHT is considered safe for human health; however, its widespread use together with the potential toxicological effects have increased consumers concern about the use of this synthetic food additive. In addition, the estimated daily intake of BHT has been demonstrated to exceed the recommended acceptable threshold. In the present work, using BHT as a case study, the usefulness of computational techniques, such as reverse screening and molecular docking, in identifying protein–ligand interactions of food additives at the bases of their toxicological effects has been probed. The computational methods here employed have been useful for the identification of several potential unknown targets of BHT, suggesting a possible explanation for its toxic effects. In silico analyses can be employed to identify new macromolecular targets of synthetic food additives and to explore their functional mechanisms or side effects. Noteworthy, this could be important for the cases in which there is an evident lack of experimental studies, as is the case for BHT. Full article
(This article belongs to the Special Issue Computational Methods in Drug Design and Food Chemistry)
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Open AccessArticle
Molecular Modeling of Epithiospecifier and Nitrile-Specifier Proteins of Broccoli and Their Interaction with Aglycones
Molecules 2020, 25(4), 772; https://doi.org/10.3390/molecules25040772 - 11 Feb 2020
Cited by 2
Abstract
Glucosinolates are secondary plant metabolites of Brassicaceae. They exert their effect after enzymatic hydrolysis to yield aglycones, which become nitriles and epithionitriles through the action of epithiospecifier (ESP) and nitrile-specifier proteins (NSP). The mechanism of action of broccoli ESP and NSP is [...] Read more.
Glucosinolates are secondary plant metabolites of Brassicaceae. They exert their effect after enzymatic hydrolysis to yield aglycones, which become nitriles and epithionitriles through the action of epithiospecifier (ESP) and nitrile-specifier proteins (NSP). The mechanism of action of broccoli ESP and NSP is poorly understood mainly because ESP and NSP structures have not been completely characterized and because aglycones are unstable, thus hindering experimental measurements. The aim of this work was to investigate the interaction of broccoli ESP and NSP with the aglycones derived from broccoli glucosinolates using molecular simulations. The three-dimensional structure of broccoli ESP was built based on its amino-acid sequence, and the NSP structure was constructed based on a consensus amino-acid sequence. The models obtained using Iterative Threading ASSEmbly Refinement (I-TASSER) were refined with the OPLS-AA/L all atom force field of GROMACS 5.0.7 and were validated by Veryfy3D and ERRAT. The structures were selected based on molecular dynamics simulations. Interactions between the proteins and aglycones were simulated with Autodock Vina at different pH. It was concluded that pH determines the stability of the complexes and that the aglycone derived from glucoraphanin has the highest affinity to both ESP and NSP. This agrees with the fact that glucoraphanin is the most abundant glucosinolate in broccoli florets. Full article
(This article belongs to the Special Issue Computational Methods in Drug Design and Food Chemistry)
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Open AccessArticle
Structure-Based Virtual Screening, Molecular Dynamics and Binding Free Energy Calculations of Hit Candidates as ALK-5 Inhibitors
Molecules 2020, 25(2), 264; https://doi.org/10.3390/molecules25020264 - 09 Jan 2020
Cited by 1
Abstract
Activin-like kinase 5 (ALK-5) is involved in the physiopathology of several conditions, such as pancreatic carcinoma, cervical cancer and liver hepatoma. Cellular events that are landmarks of tumorigenesis, such as loss of cell polarity and acquisition of motile properties and mesenchymal phenotype, are [...] Read more.
Activin-like kinase 5 (ALK-5) is involved in the physiopathology of several conditions, such as pancreatic carcinoma, cervical cancer and liver hepatoma. Cellular events that are landmarks of tumorigenesis, such as loss of cell polarity and acquisition of motile properties and mesenchymal phenotype, are associated to deregulated ALK-5 signaling. ALK-5 inhibitors, such as SB505154, GW6604, SD208, and LY2157299, have recently been reported to inhibit ALK-5 autophosphorylation and induce the transcription of matrix genes. Due to their ability to impair cell migration, invasion and metastasis, ALK-5 inhibitors have been explored as worthwhile hits as anticancer agents. This work reports the development of a structure-based virtual screening (SBVS) protocol aimed to prospect promising hits for further studies as novel ALK-5 inhibitors. From a lead-like subset of purchasable compounds, five molecules were identified as putative ALK-5 inhibitors. In addition, molecular dynamics and binding free energy calculations combined with pharmacokinetics and toxicity profiling demonstrated the suitability of these compounds to be further investigated as novel ALK-5 inhibitors. Full article
(This article belongs to the Special Issue Computational Methods in Drug Design and Food Chemistry)
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Open AccessArticle
Modeling Physico-Chemical ADMET Endpoints with Multitask Graph Convolutional Networks
Molecules 2020, 25(1), 44; https://doi.org/10.3390/molecules25010044 - 21 Dec 2019
Cited by 8
Abstract
Simple physico-chemical properties, like logD, solubility, or melting point, can reveal a great deal about how a compound under development might later behave. These data are typically measured for most compounds in drug discovery projects in a medium throughput fashion. Collecting and assembling [...] Read more.
Simple physico-chemical properties, like logD, solubility, or melting point, can reveal a great deal about how a compound under development might later behave. These data are typically measured for most compounds in drug discovery projects in a medium throughput fashion. Collecting and assembling all the Bayer in-house data related to these properties allowed us to apply powerful machine learning techniques to predict the outcome of those assays for new compounds. In this paper, we report our finding that, especially for predicting physicochemical ADMET endpoints, a multitask graph convolutional approach appears a highly competitive choice. For seven endpoints of interest, we compared the performance of that approach to fully connected neural networks and different single task models. The new model shows increased predictive performance compared to previous modeling methods and will allow early prioritization of compounds even before they are synthesized. In addition, our model follows the generalized solubility equation without being explicitly trained under this constraint. Full article
(This article belongs to the Special Issue Computational Methods in Drug Design and Food Chemistry)
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Open AccessArticle
Modeling the Antileukemia Activity of Ellipticine-Related Compounds: QSAR and Molecular Docking Study
Molecules 2020, 25(1), 24; https://doi.org/10.3390/molecules25010024 - 19 Dec 2019
Cited by 1
Abstract
The antileukemia cancer activity of organic compounds analogous to ellipticine representes a critical endpoint in the understanding of this dramatic disease. A molecular modeling simulation on a dataset of 23 compounds, all of which comply with Lipinski’s rules and have a structure analogous [...] Read more.
The antileukemia cancer activity of organic compounds analogous to ellipticine representes a critical endpoint in the understanding of this dramatic disease. A molecular modeling simulation on a dataset of 23 compounds, all of which comply with Lipinski’s rules and have a structure analogous to ellipticine, was performed using the quantitative structure activity relationship (QSAR) technique, followed by a detailed docking study on three different proteins significantly involved in this disease (PDB IDs: SYK, PI3K and BTK). As a result, a model with only four descriptors (HOMO, softness, AC1RABAMBID, and TS1KFABMID) was found to be robust enough for prediction of the antileukemia activity of the compounds studied in this work, with an R2 of 0.899 and Q2 of 0.730. A favorable interaction between the compounds and their target proteins was found in all cases; in particular, compounds 9 and 22 showed high activity and binding free energy values of around −10 kcal/mol. Theses compounds were evaluated in detail based on their molecular structure, and some modifications are suggested herein to enhance their biological activity. In particular, compounds 22_1, 22_2, 9_1, and 9_2 are indicated as possible new, potent ellipticine derivatives to be synthesized and biologically tested. Full article
(This article belongs to the Special Issue Computational Methods in Drug Design and Food Chemistry)
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Review

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Open AccessReview
Application of MM-PBSA Methods in Virtual Screening
Molecules 2020, 25(8), 1971; https://doi.org/10.3390/molecules25081971 - 23 Apr 2020
Cited by 1
Abstract
Computer-aided drug design techniques are today largely applied in medicinal chemistry. In particular, receptor-based virtual screening (VS) studies, in which molecular docking represents the gold standard in silico approach, constitute a powerful strategy for identifying novel hit compounds active against the desired target [...] Read more.
Computer-aided drug design techniques are today largely applied in medicinal chemistry. In particular, receptor-based virtual screening (VS) studies, in which molecular docking represents the gold standard in silico approach, constitute a powerful strategy for identifying novel hit compounds active against the desired target receptor. Nevertheless, the need for improving the ability of docking in discriminating true active ligands from inactive compounds, thus boosting VS hit rates, is still pressing. In this context, the use of binding free energy evaluation approaches can represent a profitable tool for rescoring ligand-protein complexes predicted by docking based on more reliable estimations of ligand-protein binding affinities than those obtained with simple scoring functions. In the present review, we focused our attention on the Molecular Mechanics-Poisson Boltzman Surface Area (MM-PBSA) method for the calculation of binding free energies and its application in VS studies. We provided examples of successful applications of this method in VS campaigns and evaluation studies in which the reliability of this approach has been assessed, thus providing useful guidelines for employing this approach in VS. Full article
(This article belongs to the Special Issue Computational Methods in Drug Design and Food Chemistry)
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