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

Deadline for manuscript submissions: closed (30 December 2019) | Viewed by 60151

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Prof. Dr. Julio Caballero

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Centro de Bioinformática y Simulación Molecular (CBSM), Facultad de Ingeniería, Universidad de Talca, Casilla 747, Talca 3460000, Chile
Interests: molecular modeling; molecular simulations; computational biochemistry; computer-aided drug design; protein kinase inhibitors
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Dear Colleagues,

In recent decades, drug design processes have been often assisted by computational methods. Such methods have been crucial to sustain the current development of medicinal chemistry research. It is rare to see a medicinal chemistry project without the support of computational methods belonging to the fields of pharmaceutical modeling, molecular modeling and simulation, cheminformatics, bioinformatics, computational chemistry, and biochemistry. These methods encompass tools that contribute to the finding of novel drugs or the processing of available information for creating useful knowledge about the interactions between bioactive ligands and their biological targets.

In this Special Issue, we are seeking original articles, short communications, or review articles focusing on the use of computational methods for drug design processes. Papers employing the computational methods available for in silico drug design, such as docking, molecular dynamics, QSAR, pharmacophore modeling, virtual screening, free energy calculations, density functional theory applications, and QM/MM, are welcome. Papers combining both experimental and computational studies are also desired.

Prof. Julio Caballero
Guest Editor

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Keywords

Molecular modeling
Molecular simulation
Computer-aided drug design
Docking
Molecular dynamics
QSAR
Virtual screening

Related Special Issue

Computational Methods for Drug Discovery and Design II in Molecules (7 articles)

Published Papers (13 papers)

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Research

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16 pages, 5096 KiB

Open AccessArticle

The Hydrophobic Ligands Entry and Exit from the GPCR Binding Site-SMD and SuMD Simulations

by Jakub Jakowiecki, Urszula Orzeł, Sathapana Chawananon, Przemysław Miszta and Sławomir Filipek

Molecules 2020, 25(8), 1930; https://doi.org/10.3390/molecules25081930 - 21 Apr 2020

Cited by 6 | Viewed by 3003

Abstract

Most G protein-coupled receptors that bind the hydrophobic ligands (lipid receptors and steroid receptors) belong to the most populated class A (rhodopsin-like) of these receptors. Typical examples of lipid receptors are: rhodopsin, cannabinoid (CB), sphingosine-1-phosphate (S1P) and lysophosphatidic (LPA) receptors. The hydrophobic ligands access the receptor binding site from the lipid bilayer not only because of their low solubility in water but also because of a large N-terminal domain plug preventing access to the orthosteric binding site from the extracellular milieu. In order to identify the most probable ligand exit pathway from lipid receptors CB1, S1P1 and LPA1 orthosteric binding sites we performed at least three repeats of steered molecular dynamics simulations in which ligands were pulled in various directions. For specific ligands being agonists, the supervised molecular dynamics approach was used to simulate the ligand entry events to the inactive receptor structures. For all investigated receptors the ligand entry/exit pathway goes through the gate between transmembrane helices TM1 and TM7, however, in some cases it combined with a direction toward water milieu. Full article

(This article belongs to the Special Issue Computational Methods for Drug Discovery and Design)

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23 pages, 5419 KiB

Open AccessArticle

GPR6 Structural Insights: Homology Model Construction and Docking Studies

by Israa H. Isawi, Paula Morales, Noori Sotudeh, Dow P. Hurst, Diane L. Lynch and Patricia H. Reggio

Molecules 2020, 25(3), 725; https://doi.org/10.3390/molecules25030725 - 07 Feb 2020

Cited by 5 | Viewed by 4805

Abstract

GPR6 is an orphan G protein-coupled receptor that has been associated with the cannabinoid family because of its recognition of a sub-set of cannabinoid ligands. The high abundance of GPR6 in the central nervous system, along with high constitutive activity and a link to several neurodegenerative diseases make GPR6 a promising biological target. In fact, diverse research groups have demonstrated that GPR6 represents a possible target for the treatment of neurodegenerative disorders such as Parkinson’s disease, Alzheimer’s disease, and Huntington’s disease. Several patents have claimed the use of a wide range of pyrazine derivatives as GPR6 inverse agonists for the treatment of Parkinson’s disease symptoms and other dyskinesia syndromes. However, the full pharmacological importance of GPR6 has not yet been fully explored due to the lack of high potency, readily available ligands targeting GPR6. The long-term goal of the present study is to develop such ligands. In this paper, we describe our initial steps towards this goal. A human GPR6 homology model was constructed using a suite of computational techniques. This model permitted the identification of unique GPR6 structural features and the exploration of the GPR6 binding crevice. A subset of patented pyrazine analogs were docked in the resultant GPR6 inactive state model to validate the model, rationalize the structure-activity relationships from the reported patents and identify the key residues in the binding crevice for ligand recognition. We will take this structural knowledge into the next phase of GPR6 project, in which scaffold hopping will be used to design new GPR6 ligands. Full article

(This article belongs to the Special Issue Computational Methods for Drug Discovery and Design)

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21 pages, 2429 KiB

Open AccessArticle

A Comprehensive QSAR Study on Antileishmanial and Antitrypanosomal Cinnamate Ester Analogues

by Freddy A. Bernal and Thomas J. Schmidt

Molecules 2019, 24(23), 4358; https://doi.org/10.3390/molecules24234358 - 28 Nov 2019

Cited by 10 | Viewed by 3813

Abstract

Parasitic infections like leishmaniasis and trypanosomiasis remain as a worldwide concern to public health. Improvement of the currently available drug discovery pipelines for those diseases is therefore mandatory. We have recently reported on the antileishmanial and antitrypanosomal activity of a set of cinnamate esters where we identified several compounds with interesting activity against L. donovani and T. brucei rhodesiense. For a better understanding of such compounds’ anti-infective activity, analyses of the underlying structure-activity relationships, especially from a quantitative point of view, would be a prerequisite for rational further development of such compounds. Thus, quantitative structure-activity relationships (QSAR) modeling for the mentioned set of compounds and their antileishmanial and antitrypanosomal activity was performed using a genetic algorithm as main variable selection tool and multiple linear regression as statistical analysis. Changes in the composition of the training/test sets were evaluated (two randomly selected and one by Kennard-Stone algorithm). The effect of the size of the models (number of descriptors) was also investigated. The quality of all resulting models was assessed by a variety of validation parameters. The models were ranked by newly introduced scoring functions accounting for the fulfillment of each of the validation criteria evaluated. The test sets were effectively within the applicability domain of the best models, which demonstrated high robustness. Detailed analysis of the molecular descriptors involved in those models revealed strong dependence of activity on the number and type of polar atoms, which affect the hydrophobic/hydrophilic properties causing a prominent influence on the investigated biological activities. Full article

(This article belongs to the Special Issue Computational Methods for Drug Discovery and Design)

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22 pages, 2638 KiB

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Lipophilicity Determination of Antifungal Isoxazolo[3,4-b]pyridin-3(1H)-ones and Their N1-Substituted Derivatives with Chromatographic and Computational Methods

by Krzesimir Ciura, Joanna Fedorowicz, Filip Andrić, Petar Žuvela, Katarzyna Ewa Greber, Paweł Baranowski, Piotr Kawczak, Joanna Nowakowska, Tomasz Bączek and Jarosław Sączewski

Molecules 2019, 24(23), 4311; https://doi.org/10.3390/molecules24234311 - 26 Nov 2019

Cited by 14 | Viewed by 5092

Abstract

The lipophilicity of a molecule is a well-recognized as a crucial physicochemical factor that conditions the biological activity of a drug candidate. This study was aimed to evaluate the lipophilicity of isoxazolo[3,4-b]pyridine-3(1H)-ones and their N1-substituted derivatives, which demonstrated pronounced antifungal activities. Several methods, including reversed-phase thin layer chromatography (RP-TLC), reversed phase high-performance liquid chromatography (RP-HPLC), and micellar electrokinetic chromatography (MEKC), were employed. Furthermore, the calculated logP values were estimated using various freely and commercially available software packages and online platforms, as well as density functional theory computations (DFT). Similarities and dissimilarities between the determined lipophilicity indices were assessed using several chemometric approaches. Principal component analysis (PCA) indicated that other features beside lipophilicity affect antifungal activities of the investigated derivatives. Quantitative-structure-retention-relationship (QSRR) analysis by means of genetic algorithm—partial least squares (GA-PLS)—was implemented to rationalize the link between the physicochemical descriptors and lipophilicity. Among the studied compounds, structure 16 should be considered as the best starting structure for further studies, since it demonstrated the lowest lipophilic character within the series while retaining biological activity. Sum of ranking differences (SRD) analysis indicated that the chromatographic approach, regardless of the technique employed, should be considered as the best approach for lipophilicity assessment of isoxazolones. Full article

(This article belongs to the Special Issue Computational Methods for Drug Discovery and Design)

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24 pages, 4567 KiB

Open AccessArticle

Carbon Nanotubes Having Haeckelite Defects as Potential Drug Carriers. Molecular Dynamics Simulation

by Camila Torres, Ignacio Villarroel, Roberto Rozas and Leonor Contreras

Molecules 2019, 24(23), 4281; https://doi.org/10.3390/molecules24234281 - 24 Nov 2019

Cited by 10 | Viewed by 3936

Abstract

Carbon nanotubes (CNTs) are valuable drug carriers since when properly functionalized they transport drugs and anchor directly to cancerous tumors whose more acidic pH causes the drug release. Herein, we study the so-called zigzag and armchair CNTs with haeckelite defects to rank their ability to adsorb doxorubicin (DOX) by determining the DOX-CNT binding free energies using the MM/PBSA and MM/GBSA methods implemented in AMBER. Our results reveal stronger DOX-CNT interactions for encapsulation of the drug inside the nanotube compared to its adsorption onto the defective nanotube external surface. Armchair CNTs with one and two defects exhibit better results compared with those with four and fifteen defects. Each haeckelite defect consists of a pair of square and octagonal rings. DOX-CNT binding free energies are predicted to be dependent on: (i) nanotube chirality and diameter, (ii) the number of defects, (iii) nitrogen doping and (iv) the position of the encapsulated DOX inside the nanotube. Armchair (10,10) nanotubes with two haeckelite defects, doped with nitrogen, exhibit the best drug-nanotube binding free energies compared with zigzag and fully hydrogenated nanotubes and, also previously reported ones with bumpy defects. These results contribute to further understanding drug-nanotube interactions and their potential application to the design of new drug delivery systems. Full article

(This article belongs to the Special Issue Computational Methods for Drug Discovery and Design)

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14 pages, 8357 KiB

Open AccessArticle

Computer-Aided Discovery of Small Molecule Inhibitors of Thymocyte Selection-Associated High Mobility Group Box Protein (TOX) as Potential Therapeutics for Cutaneous T-Cell Lymphomas

by Vibudh Agrawal, Mingwan Su, Yuanshen Huang, Michael Hsing, Artem Cherkasov and Youwen Zhou

Molecules 2019, 24(19), 3459; https://doi.org/10.3390/molecules24193459 - 24 Sep 2019

Cited by 6 | Viewed by 3958

Abstract

Cutaneous T-cell lymphomas (CTCL) are the most common primary lymphomas of the skin. We have previously identified thymocyte selection-associated high mobility group (HMG) box protein (TOX) as a promising drug target in CTCL; however, there are currently no small molecules able to directly inhibit TOX. We aimed to address this unmet opportunity by developing anti-TOX therapeutics with the use of computer-aided drug discovery methods. The available NMR-resolved structure of the TOX protein was used to model its DNA-binding HMG-box domain. To investigate the druggability of the corresponding protein–DNA interface on TOX, we performed a pilot virtual screening of 200,000 small molecules using in silico docking and identified ‘hot spots’ for drug-binding on the HMG-box domain. We then performed a large-scale virtual screening of 7.6 million drug-like compounds that were available from the ZINC15 database. As a result, a total of 140 top candidate compounds were selected for subsequent in vitro validation. Of those, 18 small molecules have been characterized as selective TOX inhibitors. Full article

(This article belongs to the Special Issue Computational Methods for Drug Discovery and Design)

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13 pages, 5668 KiB

Open AccessArticle

Calculation of the Global and Local Conceptual DFT Indices for the Prediction of the Chemical Reactivity Properties of Papuamides A–F Marine Drugs

by Norma Flores-Holguín, Juan Frau and Daniel Glossman-Mitnik

Molecules 2019, 24(18), 3312; https://doi.org/10.3390/molecules24183312 - 11 Sep 2019

Cited by 14 | Viewed by 3318

Abstract

A well-behaved model chemistry previously validated for the study of the chemical reactivity of peptides was considered for the calculation of the molecular properties and structures of the Papuamide family of marine peptides. A methodology based on Conceptual Density Functional Theory (CDFT) was chosen for the determination of the reactivity descriptors. The molecular active sites were associated with the active regions of the molecules related to the nucleophilic and electrophilic Parr functions. Finally, the drug-likenesses and the bioactivity scores for the Papuamide peptides were predicted through a homology methodology relating them with the calculated reactivity descriptors, while other properties such as the pKas were determined following a methodology developed by our group. Full article

(This article belongs to the Special Issue Computational Methods for Drug Discovery and Design)

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20 pages, 6523 KiB

Open AccessArticle

Mechanistic and Structural Insights on the IL-15 System through Molecular Dynamics Simulations

by Rui P. Sousa, Adèle D. Laurent, Agnès Quéméner, Erwan Mortier and Jean-Yves Le Questel

Molecules 2019, 24(18), 3261; https://doi.org/10.3390/molecules24183261 - 06 Sep 2019

Cited by 2 | Viewed by 4448

Abstract

Interleukin 15 (IL-15), a four-helix bundle cytokine, is involved in a plethora of different cellular functions and, particularly, plays a key role in the development and activation of immune responses. IL-15 forms receptor complexes by binding with IL-2Rβ- and common γ (γc)-signaling subunits, which are shared with other members of the cytokines family (IL-2 for IL-2Rβ- and all other γc- cytokines for γc). The specificity of IL-15 is brought by the non-signaling α-subunit, IL-15Rα. Here we present the results of molecular dynamics simulations carried out on four relevant forms of IL-15: its monomer, IL-15 interacting individually with IL-15Rα (IL-15/IL-15Rα), with IL-2Rβ/γc subunits (IL-15/IL-2Rβ/γc) or with its three receptors simultaneously (IL-15/IL-15Rα/IL-2Rβ/γc). Through the analyses of the various trajectories, new insights on the structural features of the interfaces are highlighted, according to the considered form. The comparison of the results with the experimental data, available from X-ray crystallography, allows, in particular, the rationalization of the importance of IL-15 key residues (e.g., Asp8, Lys10, Glu64). Furthermore, the pivotal role of water molecules in the stabilization of the various protein-protein interfaces and their H-bonds networks are underlined for each of the considered complexes. Full article

(This article belongs to the Special Issue Computational Methods for Drug Discovery and Design)

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18 pages, 2660 KiB

Open AccessArticle

Identification of New Inhibitors with Potential Antitumor Activity from Polypeptide Structures via Hierarchical Virtual Screening

by Elenilze F. B. Ferreira, Luciane B. Silva, Glauber V. Costa, Josivan S. Costa, Mayara A. T. Fujishima, Rozires P. Leão, André L. S. Ferreira, Leonardo B. Federico, Carlos H. T. P. Silva, Joaquín M. C. Rosa, Williams J. C. Macêdo and Cleydson B. R. Santos

Molecules 2019, 24(16), 2943; https://doi.org/10.3390/molecules24162943 - 14 Aug 2019

Cited by 16 | Viewed by 3883

Abstract

Leukemias are neoplasms that affect hematopoietic cells, which are developed by genetic alterations (mutations) that lead to the loss of proliferation control mechanisms (maturation and/or cell death). The α4β1 integrin receptor is a therapeutic target for inflammation, autoimmune diseases and lymphoid tumors. This study was carried out to search through the antagonists-based virtual screening for α4β1 receptor. Initially, seventeen (17) structures were selected (based on the inhibitory activity values, IC₅₀) and the structure with the best value was chosen as the pivot. The pharmacophoric pattern was determined from the online PharmaGist server and resulted in a model of score value equal to 97.940 with 15 pharmacophoric characteristics that were statistically evaluated via Pearson correlations, principal component analysis (PCA) and hierarchical clustering analysis (HCA). A refined model generated four pharmacophoric hypotheses totaling 1.478 structures set of Zinc_database. After, the pharmacokinetic, toxicological and biological activity predictions were realized comparing with pivot structure that resulted in five (ZINC72088291, ZINC68842860, ZINC14365931, ZINC09588345 and ZINC91247798) structures with optimal in silico predictions. Therefore, future studies are needed to confirm antitumor potential activity of molecules selected this work with in vitro and in vivo assays. Full article

(This article belongs to the Special Issue Computational Methods for Drug Discovery and Design)

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14 pages, 1944 KiB

Open AccessArticle

Synthesis, Biological and In Silico Evaluation of Pure Nucleobase-Containing Spiro (Indane-Isoxazolidine) Derivatives as Potential Inhibitors of MDM2–p53 Interaction

by Loredana Maiuolo, Vincenzo Algieri, Beatrice Russo, Matteo Antonio Tallarida, Monica Nardi, Maria Luisa Di Gioia, Zahra Merchant, Pedro Merino, Ignacio Delso and Antonio De Nino

Molecules 2019, 24(16), 2909; https://doi.org/10.3390/molecules24162909 - 10 Aug 2019

Cited by 20 | Viewed by 3765

Abstract

Nucleobase-containing isoxazolidines spiro-bonded to an indane core have been synthesized in very good yields by regio- and diastereoselective 1,3-dipolar cycloaddition starting from indanyl nitrones and N-vinylnucleobases by using environmentally benign microwave technology. The contemporary presence of various structural groups that are individually active scaffolds of different typology of drugs, has directed us to speculate that these compounds may act as inhibitors of MDM2–p53 interaction. Therefore, both computational calculations and antiproliferative screening against A549 human lung adenocarcinoma cells and human SH-SY5Y neuroblastoma cells were carried out to support this hypothesis. Full article

(This article belongs to the Special Issue Computational Methods for Drug Discovery and Design)

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13 pages, 1027 KiB

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Heterologous Machine Learning for the Identification of Antimicrobial Activity in Human-Targeted Drugs

by Rodrigo A. Nava Lara, Longendri Aguilera-Mendoza, Carlos A. Brizuela, Antonio Peña and Gabriel Del Rio

Molecules 2019, 24(7), 1258; https://doi.org/10.3390/molecules24071258 - 31 Mar 2019

Cited by 10 | Viewed by 3796

Abstract

The emergence of microbes resistant to common antibiotics represent a current treat to human health. It has been recently recognized that non-antibiotic labeled drugs may promote antibiotic-resistance mechanisms in the human microbiome by presenting a secondary antibiotic activity; hence, the development of computer-assisted procedures to identify antibiotic activity in human-targeted compounds may assist in preventing the emergence of resistant microbes. In this regard, it is worth noting that while most antibiotics used to treat human infectious diseases are non-peptidic compounds, most known antimicrobials nowadays are peptides, therefore all computer-based models aimed to predict antimicrobials either use small datasets of non-peptidic compounds rendering predictions with poor reliability or they predict antimicrobial peptides that are not currently used in humans. Here we report a machine-learning-based approach trained to identify gut antimicrobial compounds; a unique aspect of our model is the use of heterologous training sets, in which peptide and non-peptide antimicrobial compounds were used to increase the size of the training data set. Our results show that combining peptide and non-peptide antimicrobial compounds rendered the best classification of gut antimicrobial compounds. Furthermore, this classification model was tested on the latest human-approved drugs expecting to identify antibiotics with broad-spectrum activity and our results show that the model rendered predictions consistent with current knowledge about broad-spectrum antibiotics. Therefore, heterologous machine learning rendered an efficient computational approach to classify antimicrobial compounds. Full article

(This article belongs to the Special Issue Computational Methods for Drug Discovery and Design)

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17 pages, 2687 KiB

Open AccessReview

TLR4-Targeting Therapeutics: Structural Basis and Computer-Aided Drug Discovery Approaches

by Qurat ul Ain, Maria Batool and Sangdun Choi

Molecules 2020, 25(3), 627; https://doi.org/10.3390/molecules25030627 - 31 Jan 2020

Cited by 64 | Viewed by 8927

Abstract

The integration of computational techniques into drug development has led to a substantial increase in the knowledge of structural, chemical, and biological data. These techniques are useful for handling the big data generated by empirical and clinical studies. Over the last few years, computer-aided drug discovery methods such as virtual screening, pharmacophore modeling, quantitative structure-activity relationship analysis, and molecular docking have been employed by pharmaceutical companies and academic researchers for the development of pharmacologically active drugs. Toll-like receptors (TLRs) play a vital role in various inflammatory, autoimmune, and neurodegenerative disorders such as sepsis, rheumatoid arthritis, inflammatory bowel disease, Alzheimer’s disease, multiple sclerosis, cancer, and systemic lupus erythematosus. TLRs, particularly TLR4, have been identified as potential drug targets for the treatment of these diseases, and several relevant compounds are under preclinical and clinical evaluation. This review covers the reported computational studies and techniques that have provided insights into TLR4-targeting therapeutics. Furthermore, this article provides an overview of the computational methods that can benefit a broad audience in this field and help with the development of novel drugs for TLR-related disorders. Full article

(This article belongs to the Special Issue Computational Methods for Drug Discovery and Design)

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19 pages, 1945 KiB

Open AccessReview

Considerations for Docking of Selective Angiotensin-Converting Enzyme Inhibitors

by Julio Caballero

Molecules 2020, 25(2), 295; https://doi.org/10.3390/molecules25020295 - 11 Jan 2020

Cited by 35 | Viewed by 5526

Abstract

The angiotensin-converting enzyme (ACE) is a two-domain dipeptidylcarboxypeptidase, which has a direct involvement in the control of blood pressure by performing the hydrolysis of angiotensin I to produce angiotensin II. At the same time, ACE hydrolyzes other substrates such as the vasodilator peptide bradykinin and the anti-inflammatory peptide N-acetyl-SDKP. In this sense, ACE inhibitors are bioactive substances with potential use as medicinal products for treatment or prevention of hypertension, heart failures, myocardial infarction, and other important diseases. This review examined the most recent literature reporting ACE inhibitors with the help of molecular modeling. The examples exposed here demonstrate that molecular modeling methods, including docking, molecular dynamics (MD) simulations, quantitative structure-activity relationship (QSAR), etc, are essential for a complete structural picture of the mode of action of ACE inhibitors, where molecular docking has a key role. Examples show that too many works identified ACE inhibitory activities of natural peptides and peptides obtained from hydrolysates. In addition, other works report non-peptide compounds extracted from natural sources and synthetic compounds. In all these cases, molecular docking was used to provide explanation of the chemical interactions between inhibitors and the ACE binding sites. For docking applications, most of the examples exposed here do not consider that: (i) ACE has two domains (nACE and cACE) with available X-ray structures, which are relevant for the design of selective inhibitors, and (ii) nACE and cACE binding sites have large dimensions, which leads to non-reliable solutions during docking calculations. In support of the solution of these problems, the structural information found in Protein Data Bank (PDB) was used to perform an interaction fingerprints (IFPs) analysis applied on both nACE and cACE domains. This analysis provides plots that identify the chemical interactions between ligands and both ACE binding sites, which can be used to guide docking experiments in the search of selective natural components or novel drugs. In addition, the use of hydrogen bond constraints in the S₂ and S₂′ subsites of nACE and cACE are suggested to guarantee that docking solutions are reliable. Full article

(This article belongs to the Special Issue Computational Methods for Drug Discovery and Design)

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