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Computational Approaches for Drug Discovery
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Computational Approaches for Drug Discovery

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

Deadline for manuscript submissions: closed (31 March 2019) | Viewed by 103425

Special Issue Editor

Dr. Simone Brogi

E-Mail Website1 Website2
Guest Editor
1. Department of Pharmacy, Department of Excellence 2018-2022, University of Naples “Federico II”, Via D. Montesano 49, 80131 Napoli, Italy
2. Department of Biotechnology, Chemistry and Pharmacy, Department of Excellence 2018-2022, University of Siena, Via A. Moro 2, 53100 Siena, Italy
Interests: molecular modelling; computational medicinal chemistry; computer-aided drug design; drug discovery; bioinformatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, in silico methodologies have become a crucial part of the drug discovery process; mostly because they can boost the whole drug development trajectory, identifying and discovering new potential drugs with a significant reduction of the costs and time. Furthermore, computer-aided drug design (CADD) approaches are important for reducing the experimental use of animals for in vivo testing, for helping the design of safer drugs and for repositioning known drugs, assisting medicinal chemists in each step (design, discovery, development, and hit-optimization) during the drug discovery process. The conventional methods for drug discovery imply the costly random screening of synthesized compounds or natural products. On the other hand, the computational procedures can be very multifarious, requiring interdisciplinary studies and application of computer science to rationally design effective and commercially feasible drugs. Remarkable progresses have been made both in computer science field, that have speeded up the drug discovery research, and in the development of new experimental procedures for the characterization of biological targets. Among the methods in drug discovery, pharmacophore modelling, three-dimensional quantitative structure activity relationships (3D-QSAR), Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) remain the preferred ligand-based (LB) methods for fast virtual screening (VS) procedures and for rationalizing the activities of a set of ligands. In a recent breakthrough, a novel approach in QSAR field is represented by the combination of the Molecular Dynamics (MD), and the relative computed descriptors, with the generation of QSAR models. This approach provides computational tools, the so-called MD-QSAR models, with an enhanced predictive power. When the information of the 3D structure of the targets in complex with ligands are known, structure-based (SB) drug design approaches such as SB pharmacophore models including excluded volumes or high throughput dockings are the elected methods for identifying novel chemical entities for a selected target. If we want to investigate ligand–receptor complexes and in general the dynamics and thermodynamics of biological systems, MD simulations represent one of the major computational resources and still remain the most representative technique for this kind of investigation. In addition, for better characterizing biological systems, understanding the mechanism of action of enzymes also in complex with ligands, quantum mechanics/molecular mechanics (QM/MM) calculations can be helpful in drug discovery and design. Currently, QM/MM can be combined with MD (QM/MM-MD) to completely characterize enzymatic mechanisms.

These tools can help the scientists to shorten the cycle of drug discovery, and thus make the process more cost-affordable. The huge technological progresses in hardware and software resources, algorithms design, as well as the biological advances for identifying new drug targets, make computer-assisted approaches the most valuable methods in pre-clinical research. For this Special Issue of Molecules, we invite researchers in the computational drug discovery field, to submit original research articles, short communications and review articles related to the in silico approaches used in Medicinal Chemistry.

Dr. Simone Brogi
Guest Editor

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Keywords

  • Molecular Modelling
  • Computer-Aided-Drug Design
  • Computational Chemistry
  • Drug Discovery
  • Computational Methods in Medicinal Chemistry

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Published Papers (16 papers)

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Editorial

Jump to: Research, Review

6 pages, 214 KiB  
Editorial
Computational Approaches for Drug Discovery
by Simone Brogi
Molecules 2019, 24(17), 3061; https://doi.org/10.3390/molecules24173061 - 22 Aug 2019
Cited by 42 | Viewed by 4963
Abstract
Computational approaches represent valuable and essential tools in each step of the drug discovery and development trajectory [...] Full article
(This article belongs to the Special Issue Computational Approaches for Drug Discovery)

Research

Jump to: Editorial, Review

14 pages, 6771 KiB  
Article
Computational Investigation of Bisphosphate Inhibitors of 3-Deoxy-d-manno-octulosonate 8-phosphate Synthase
by Jéssica de Oliveira Araújo, Alberto Monteiro dos Santos, Jerônimo Lameira, Cláudio Nahum Alves and Anderson Henrique Lima
Molecules 2019, 24(13), 2370; https://doi.org/10.3390/molecules24132370 - 27 Jun 2019
Cited by 10 | Viewed by 2844
Abstract
The synthase, 3-deoxy-d-manno-octulosonate 8-phosphate (KDO8P), is a key enzyme for the lipopolysaccharide (LPS) biosynthesis of gram-negative bacteria and a potential target for developing new antimicrobial agents. In this study, computational molecular modeling methods were used to determine the complete [...] Read more.
The synthase, 3-deoxy-d-manno-octulosonate 8-phosphate (KDO8P), is a key enzyme for the lipopolysaccharide (LPS) biosynthesis of gram-negative bacteria and a potential target for developing new antimicrobial agents. In this study, computational molecular modeling methods were used to determine the complete structure of the KDO8P synthase from Neisseria meningitidis and to investigate the molecular mechanism of its inhibition by three bisphosphate inhibitors: BPH1, BPH2, and BPH3. Our results showed that BPH1 presented a protein–ligand complex with the highest affinity, which is in agreement with experimental data. Furthermore, molecular dynamics (MD) simulations showed that BPH1 is more active due to the many effective interactions, most of which are derived from its phosphoenolpyruvate moiety. Conversely, BPH2 exhibited few hydrogen interactions during the MD simulations with key residues located at the active sites of the KDO8P synthase. In addition, we hydroxylated BPH2 to create the hypothetical molecule named BPH3, to investigate the influence of the hydroxyl groups on the affinity of the bisphosphate inhibitors toward the KDO8P synthase. Overall, we discuss the main interactions between the KDO8P synthase and the bisphosphate inhibitors that are potential starting points for the design of new molecules with significant antibiotic activities. Full article
(This article belongs to the Special Issue Computational Approaches for Drug Discovery)
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16 pages, 5299 KiB  
Article
Machine Learning Models Combined with Virtual Screening and Molecular Docking to Predict Human Topoisomerase I Inhibitors
by Bingke Li, Xiaokang Kang, Dan Zhao, Yurong Zou, Xudong Huang, Jiexue Wang and Chenghua Zhang
Molecules 2019, 24(11), 2107; https://doi.org/10.3390/molecules24112107 - 4 Jun 2019
Cited by 12 | Viewed by 4455
Abstract
In this work, random forest (RF), support vector machine, k-nearest neighbor and C4.5 decision tree, were used to establish classification models for predicting whether an unknown molecule is an inhibitor of human topoisomerase I (Top1) protein. All these models have achieved satisfactory results, [...] Read more.
In this work, random forest (RF), support vector machine, k-nearest neighbor and C4.5 decision tree, were used to establish classification models for predicting whether an unknown molecule is an inhibitor of human topoisomerase I (Top1) protein. All these models have achieved satisfactory results, with total prediction accuracies from 89.70% to 97.12%. Through comparative analysis, it can be found that the RF model has the best forecasting effect. The parameters were further optimized to generate the best-performing RF model. At the same time, features selection was implemented to choose properties most relevant to the inhibition of Top1 from 189 molecular descriptors through a special RF procedure. Subsequently, a ligand-based virtual screening was performed from the Maybridge database by the optimal RF model and 596 hits were picked out. Then, 67 molecules with relative probability scores over 0.7 were selected based on the screening results. Next, the 67 molecules above were docked to Top1 using AutoDock Vina. Finally, six top-ranked molecules with binding energies less than −10.0 kcal/mol were screened out and a common backbone, which is entirely different from that of existing Top1 inhibitors reported in the literature, was found. Full article
(This article belongs to the Special Issue Computational Approaches for Drug Discovery)
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19 pages, 1951 KiB  
Article
In Silico Evaluation of Ibuprofen and Two Benzoylpropionic Acid Derivatives with Potential Anti-Inflammatory Activity
by José A. H. M. Bittencourt, Moysés F. A. Neto, Pedro S. Lacerda, Renata C. V. S. Bittencourt, Rai C. Silva, Cleison C. Lobato, Luciane B. Silva, Franco H. A. Leite, Juliana P. Zuliani, Joaquín M. C. Rosa, Rosivaldo S. Borges and Cleydson B. R. Santos
Molecules 2019, 24(8), 1476; https://doi.org/10.3390/molecules24081476 - 15 Apr 2019
Cited by 28 | Viewed by 5327
Abstract
Inflammation is a complex reaction involving cellular and molecular components and an unspecific response to a specific aggression. The use of scientific and technological innovations as a research tool combining multidisciplinary knowledge in informatics, biotechnology, chemistry and biology are essential for optimizing time [...] Read more.
Inflammation is a complex reaction involving cellular and molecular components and an unspecific response to a specific aggression. The use of scientific and technological innovations as a research tool combining multidisciplinary knowledge in informatics, biotechnology, chemistry and biology are essential for optimizing time and reducing costs in the drug design. Thus, the integration of these in silico techniques makes it possible to search for new anti-inflammatory drugs with better pharmacokinetic and toxicological profiles compared to commercially used drugs. This in silico study evaluated the anti-inflammatory potential of two benzoylpropionic acid derivatives (MBPA and DHBPA) using molecular docking and their thermodynamic profiles by molecular dynamics, in addition to predicting oral bioavailability, bioactivity and toxicity. In accordance to our predictions the derivatives proposed here had the potential capacity for COX-2 inhibition in the human and mice enzyme, due to containing similar interactions with the control compound (ibuprofen). Ibuprofen showed toxic predictions of hepatotoxicity (in human, mouse and rat; toxicophoric group 2-arylacetic or 3-arylpropionic acid) and irritation of the gastrointestinal tract (in human, mouse and rat; toxicophoric group alpha-substituted propionic acid or ester) confirming the literature data, as well as the efficiency of the DEREK 10.0.2 program. Moreover, the proposed compounds are predicted to have a good oral bioavailability profile and low toxicity (LD50 < 700 mg/kg) and safety when compared to the commercial compound. Therefore, future studies are necessary to confirm the anti-inflammatory potential of these compounds. Full article
(This article belongs to the Special Issue Computational Approaches for Drug Discovery)
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7 pages, 749 KiB  
Article
Chemical Reactivity Theory and Empirical Bioactivity Scores as Computational Peptidology Alternative Tools for the Study of Two Anticancer Peptides of Marine Origin
by Juan Frau, Norma Flores-Holguín and Daniel Glossman-Mitnik
Molecules 2019, 24(6), 1115; https://doi.org/10.3390/molecules24061115 - 21 Mar 2019
Cited by 28 | Viewed by 3801
Abstract
This work presents an account of the reactivity behavior of the anticancer marine drugs, Soblidotin and Tasidotin, based on the calculation of the global and local descriptors resulting from Chemical Reactivity Theory (CRT), also known as Conceptual DFT, for their consideration as a [...] Read more.
This work presents an account of the reactivity behavior of the anticancer marine drugs, Soblidotin and Tasidotin, based on the calculation of the global and local descriptors resulting from Chemical Reactivity Theory (CRT), also known as Conceptual DFT, for their consideration as a useful complement to approximations based on Molecular Docking. The information on the global and local reactivity descriptors of the Soblidotin and Tasidotin molecules, obtained through our proposed methodology, may be used for the design of new pharmaceutical analogs by relying on the chemical interactions between these peptides and their protein-type biological receptors. It can be concluded that the CRT approximation to the global and local chemical reactivity, based on the descriptors, can provide interesting information for the consideration of both molecules as potential therapeutic drugs. This is complemented by a study on Advanced Glycation Endproduct (AGE) inhibition, by comparison with the usual molecular systems considered for the task, as a re-purposing study. Finally, the bioactivity scores for Soblidotin and Tasidotin are predicted through an empirical procedure, based on comparison with molecular structures with well-known pharmacological properties. Full article
(This article belongs to the Special Issue Computational Approaches for Drug Discovery)
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17 pages, 6626 KiB  
Article
Novel Approach for the Search for Chemical Scaffolds with Activity at Both Acetylcholinesterase and the α7 Nicotinic Acetylcholine Receptor: A Perspective on Scaffolds with Dual Activity for the Treatment of Neurodegenerative Disorders
by Natalia M. Kowal, Dinesh C. Indurthi, Philip K. Ahring, Mary Chebib, Elin S. Olafsdottir and Thomas Balle
Molecules 2019, 24(3), 446; https://doi.org/10.3390/molecules24030446 - 27 Jan 2019
Cited by 14 | Viewed by 4891
Abstract
Neurodegenerative disorders, including Alzheimer’s disease, belong to the group of the most difficult and challenging conditions with very limited treatment options. Attempts to find new drugs in most cases fail at the clinical stage. New tactics to develop better drug candidates to manage [...] Read more.
Neurodegenerative disorders, including Alzheimer’s disease, belong to the group of the most difficult and challenging conditions with very limited treatment options. Attempts to find new drugs in most cases fail at the clinical stage. New tactics to develop better drug candidates to manage these diseases are urgently needed. It is evident that better understanding of the neurodegeneration process is required and targeting multiple receptors may be essential. Herein, we present a novel approach, searching for dual active compounds interacting with acetylcholinesterase (AChE) and the α7 nicotinic acetylcholine receptor (nAChR) using computational chemistry methods including homology modelling and high throughput virtual screening. Activities of identified hits were evaluated at the two targets using the colorimetric method of Ellman and two-electrode voltage-clamp electrophysiology, respectively. Out of 87,250 compounds from a ZINC database of natural products and their derivatives, we identified two compounds, 8 and 9, with dual activity and balanced IC50 values of 10 and 5 µM at AChE, and 34 and 14 µM at α7 nAChR, respectively. This is the first report presenting successful use of virtual screening in finding compounds with dual mode of action inhibiting both the AChE enzyme and the α7 nAChR and shows that computational methods can be a valuable tool in the early lead discovery process. Full article
(This article belongs to the Special Issue Computational Approaches for Drug Discovery)
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12 pages, 2074 KiB  
Communication
PeptoGrid—Rescoring Function for AutoDock Vina to Identify New Bioactive Molecules from Short Peptide Libraries
by Arthur O. Zalevsky, Alexander S. Zlobin, Vasilina R. Gedzun, Roman V. Reshetnikov, Maxim L. Lovat, Anton V. Malyshev, Igor I. Doronin, Gennady A. Babkin and Andrey V. Golovin
Molecules 2019, 24(2), 277; https://doi.org/10.3390/molecules24020277 - 13 Jan 2019
Cited by 15 | Viewed by 7502
Abstract
Peptides are promising drug candidates due to high specificity and standout safety. Identification of bioactive peptides de novo using molecular docking is a widely used approach. However, current scoring functions are poorly optimized for peptide ligands. In this work, we present a novel [...] Read more.
Peptides are promising drug candidates due to high specificity and standout safety. Identification of bioactive peptides de novo using molecular docking is a widely used approach. However, current scoring functions are poorly optimized for peptide ligands. In this work, we present a novel algorithm PeptoGrid that rescores poses predicted by AutoDock Vina according to frequency information of ligand atoms with particular properties appearing at different positions in the target protein’s ligand binding site. We explored the relevance of PeptoGrid ranking with a virtual screening of peptide libraries using angiotensin-converting enzyme and GABAB receptor as targets. A reasonable agreement between the computational and experimental data suggests that PeptoGrid is suitable for discovering functional leads. Full article
(This article belongs to the Special Issue Computational Approaches for Drug Discovery)
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16 pages, 2236 KiB  
Article
Toward of Safer Phenylbutazone Derivatives by Exploration of Toxicity Mechanism
by Rosivaldo S. Borges, Ivanete C. Palheta, Sirlene S. B. Ota, Roberto B. Morais, Valéria A. Barros, Ryan S. Ramos, Rai C. Silva, Josivan da S. Costa, Carlos H. T. P. Silva, Joaquín M. Campos and Cleydson B. R. Santos
Molecules 2019, 24(1), 143; https://doi.org/10.3390/molecules24010143 - 1 Jan 2019
Cited by 19 | Viewed by 6327
Abstract
A drug design for safer phenylbutazone was been explored by reactivity and docking studies involving single electron transfer mechanism, as well as toxicological predictions. Several approaches about its structural properties were performed through quantum chemistry calculations at the B3LYP level of theory, together [...] Read more.
A drug design for safer phenylbutazone was been explored by reactivity and docking studies involving single electron transfer mechanism, as well as toxicological predictions. Several approaches about its structural properties were performed through quantum chemistry calculations at the B3LYP level of theory, together with the 6-31+G(d,p) basis sets. Molecular orbital and ionization potential were associated to electron donation capacity. The spin densities contribution showed a preferential hydroxylation at the para-positions of phenyl ring when compared to other positions. In addition, on electron abstractions the aromatic hydroxylation has more impact than alkyl hydroxylation. Docking studies indicate that six structures 1, 7, 8 and 13–15 have potential for inhibiting human as well as murine COX-2, due to regions showing similar intermolecular interactions to the observed for the control compounds (indomethacin and refecoxib). Toxicity can be related to aromatic hydroxylation. In accordance to our calculations, the derivatives here proposed are potentially more active as well safer than phenylbutazone and only structures 8 and 13–15 were the most promising. Such results can explain the biological properties of phenylbutazone and support the design of potentially safer candidates. Full article
(This article belongs to the Special Issue Computational Approaches for Drug Discovery)
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14 pages, 15240 KiB  
Article
Activity Landscape and Molecular Modeling to Explore the SAR of Dual Epigenetic Inhibitors: A Focus on G9a and DNMT1
by Edgar López-López, Fernando D. Prieto-Martínez and José L. Medina-Franco
Molecules 2018, 23(12), 3282; https://doi.org/10.3390/molecules23123282 - 11 Dec 2018
Cited by 23 | Viewed by 5224
Abstract
In this work we discuss the insights from activity landscape, docking and molecular dynamics towards the understanding of the structure-activity relationships of dual inhibitors of major epigenetic targets: lysine methyltransferase (G9a) and DNA methyltranferase 1 (DNMT1). The study was based on a novel [...] Read more.
In this work we discuss the insights from activity landscape, docking and molecular dynamics towards the understanding of the structure-activity relationships of dual inhibitors of major epigenetic targets: lysine methyltransferase (G9a) and DNA methyltranferase 1 (DNMT1). The study was based on a novel data set of 50 published compounds with reported experimental activity for both targets. The activity landscape analysis revealed the presence of activity cliffs, e.g., pairs of compounds with high structure similarity but large activity differences. Activity cliffs were further rationalized at the molecular level by means of molecular docking and dynamics simulations that led to the identification of interactions with key residues involved in the dual activity or selectivity with the epigenetic targets. Full article
(This article belongs to the Special Issue Computational Approaches for Drug Discovery)
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12 pages, 2186 KiB  
Article
Classical QSAR and Docking Simulation of 4-Pyridone Derivatives for Their Antimalarial Activity
by Máryury Flores-Sumoza, Jackson J. Alcázar, Edgar Márquez, José R. Mora, Jesús Lezama and Esneyder Puello
Molecules 2018, 23(12), 3166; https://doi.org/10.3390/molecules23123166 - 1 Dec 2018
Cited by 13 | Viewed by 3856
Abstract
In this work, the minimum energy structures of 22 4-pyridone derivatives have been optimized at Density Functional Theory level, and several quantum molecular, including electronic and thermodynamic descriptors, were computed for these substrates in order to obtain a statistical and meaningful QSAR equation. [...] Read more.
In this work, the minimum energy structures of 22 4-pyridone derivatives have been optimized at Density Functional Theory level, and several quantum molecular, including electronic and thermodynamic descriptors, were computed for these substrates in order to obtain a statistical and meaningful QSAR equation. In this sense, by using multiple linear regressions, five mathematical models have been obtained. The best model with only four descriptors (r2 = 0.86, Q2 = 0.92, S.E.P = 0.38) was validated by the leave-one-out cross-validation method. The antimalarial activity can be explained by the combination of the four mentioned descriptors e.g., electronic potential, dipolar momentum, partition coefficient and molar refractivity. The statistical parameters of this model suggest that it is robust enough to predict the antimalarial activity of new possible compounds; consequently, three small chemical modifications into the structural core of these compounds were performed specifically on the most active compound of the series (compound 13). These three new suggested compounds were leveled as 13A, 13B and 13C, and the predicted biological antimalarial activity is 0.02 µM, 0.03 µM, and 0.07 µM, respectively. In order to complement these results focused on the possible action mechanism of the substrates, a docking simulation was included for these new structures as well as for the compound 13 and the docking scores (binding affinity) obtained for the interaction of these substrates with the cytochrome bc1, were −7.5, −7.2, −6.9 and −7.5 kcal/mol for 13A, 13B, 13C and compound 13, respectively, which suggests that these compounds are good candidates for its biological application in this illness. Full article
(This article belongs to the Special Issue Computational Approaches for Drug Discovery)
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14 pages, 5046 KiB  
Article
Ligand-Based Pharmacophore Modeling Using Novel 3D Pharmacophore Signatures
by Alina Kutlushina, Aigul Khakimova, Timur Madzhidov and Pavel Polishchuk
Molecules 2018, 23(12), 3094; https://doi.org/10.3390/molecules23123094 - 27 Nov 2018
Cited by 36 | Viewed by 14682 | Correction
Abstract
Pharmacophore modeling is a widely used strategy for finding new hit molecules. Since not all protein targets have available 3D structures, ligand-based approaches are still useful. Currently, there are just a few free ligand-based pharmacophore modeling tools, and these have a lot of [...] Read more.
Pharmacophore modeling is a widely used strategy for finding new hit molecules. Since not all protein targets have available 3D structures, ligand-based approaches are still useful. Currently, there are just a few free ligand-based pharmacophore modeling tools, and these have a lot of restrictions, e.g., using a template molecule for alignment. We developed a new approach to 3D pharmacophore representation and matching which does not require pharmacophore alignment. This representation can be used to quickly find identical pharmacophores in a given set. Based on this representation, a 3D pharmacophore ligand-based modeling approach to search for pharmacophores which preferably match active compounds and do not match inactive ones was developed. The approach searches for 3D pharmacophore models starting from 2D structures of available active and inactive compounds. The implemented approach was successfully applied for several retrospective studies. The results were compared to a 2D similarity search, demonstrating some of the advantages of the developed 3D pharmacophore models. Also, the generated 3D pharmacophore models were able to match the 3D poses of known ligands from their protein-ligand complexes, confirming the validity of the models. The developed approach is available as an open-source software tool: http://www.qsar4u.com/pages/pmapper.php and https://github.com/meddwl/psearch. Full article
(This article belongs to the Special Issue Computational Approaches for Drug Discovery)
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15 pages, 4585 KiB  
Article
On the Mechanism of Action of Anti-Inflammatory Activity of Hypericin: An In Silico Study Pointing to the Relevance of Janus Kinases Inhibition
by Luca Dellafiora, Gianni Galaverna, Gabriele Cruciani, Chiara Dall’Asta and Renato Bruni
Molecules 2018, 23(12), 3058; https://doi.org/10.3390/molecules23123058 - 22 Nov 2018
Cited by 20 | Viewed by 5077
Abstract
St. John’s Wort (Hypericum perforatum L.) flowers are commonly used in ethnomedical preparations with promising outcomes to treat inflammation both per os and by topical application. However, the underlying molecular mechanisms need to be described toward a rational, evidence-based, and reproducible use. [...] Read more.
St. John’s Wort (Hypericum perforatum L.) flowers are commonly used in ethnomedical preparations with promising outcomes to treat inflammation both per os and by topical application. However, the underlying molecular mechanisms need to be described toward a rational, evidence-based, and reproducible use. For this purpose, the aptitude of the prominent Hypericum metabolite hypericin was assessed, along with that of its main congeners, to behave as an inhibitor of janus kinase 1, a relevant enzyme in inflammatory response. It was used a molecular modeling approach relying on docking simulations, pharmacophoric modeling, and molecular dynamics to estimate the capability of molecules to interact and persist within the enzyme pocket. Our results highlighted the capability of hypericin, and some of its analogues and metabolites, to behave as ATP-competitive inhibitor providing: (i) a likely mechanistic elucidation of anti-inflammatory activity of H. perforatum extracts containing hypericin and related compounds; and (ii) a rational-based prioritization of H. perforatum components to further characterize their actual effectiveness as anti-inflammatory agents. Full article
(This article belongs to the Special Issue Computational Approaches for Drug Discovery)
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20 pages, 12675 KiB  
Article
Virtual Screening, Biological Evaluation, and 3D-QSAR Studies of New HIV-1 Entry Inhibitors That Function via the CD4 Primary Receptor
by Chaozai Zhang, Huijun Zhang, Lina S. Huang, Siyu Zhu, Yan Xu, Xing-Quan Zhang, Robert T. Schooley, Xiaohong Yang, Ziwei Huang and Jing An
Molecules 2018, 23(11), 3036; https://doi.org/10.3390/molecules23113036 - 20 Nov 2018
Cited by 8 | Viewed by 5019
Abstract
Human immunodeficiency virus type 1 (HIV-1) is responsible for the majority of HIV infections worldwide, and we still lack a cure for this infection. Blocking the interaction of HIV-1 and its primary receptor CD4 is one strategy for identifying new anti-HIV-1 entry inhibitors. [...] Read more.
Human immunodeficiency virus type 1 (HIV-1) is responsible for the majority of HIV infections worldwide, and we still lack a cure for this infection. Blocking the interaction of HIV-1 and its primary receptor CD4 is one strategy for identifying new anti-HIV-1 entry inhibitors. Here we report the discovery of a novel ligand that can inhibit HIV-1 entry and infection via CD4. Biological and computational analyses of this inhibitor and its analogs, using bioactivity evaluation, Rule of Five (RO5), comparative molecular field analysis (CoMFA)/comparative molecular similarity index analysis (CoMSIA) models, and three-dimensional quantitative structure-activity relationship (3D-QSAR), singled out compound 3 as a promising lead molecule for the further development of therapeutics targeting HIV-1 entry. Our study demonstrates an effective approach for employing structure-based, rational drug design techniques to identify novel antiviral compounds with interesting biological activities. Full article
(This article belongs to the Special Issue Computational Approaches for Drug Discovery)
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17 pages, 21635 KiB  
Article
An In Silico Study of the Antioxidant Ability for Two Caffeine Analogs Using Molecular Docking and Quantum Chemical Methods
by Josivan da Silva Costa, Ryan da Silva Ramos, Karina da Silva Lopes Costa, Davi do Socorro Barros Brasil, Carlos Henrique Tomich de Paula da Silva, Elenilze Figueiredo Batista Ferreira, Rosivaldo dos Santos Borges, Joaquín María Campos, Williams Jorge da Cruz Macêdo and Cleydson Breno Rodrigues dos Santos
Molecules 2018, 23(11), 2801; https://doi.org/10.3390/molecules23112801 - 29 Oct 2018
Cited by 42 | Viewed by 7397
Abstract
The antioxidant activity of molecules constitutes an important factor for the regulation of redox homeostasis and reduction of the oxidative stress. Cells affected by oxidative stress can undergo genetic alteration, causing structural changes and promoting the onset of chronic diseases, such as cancer. [...] Read more.
The antioxidant activity of molecules constitutes an important factor for the regulation of redox homeostasis and reduction of the oxidative stress. Cells affected by oxidative stress can undergo genetic alteration, causing structural changes and promoting the onset of chronic diseases, such as cancer. We have performed an in silico study to evaluate the antioxidant potential of two molecules of the zinc database: ZINC08706191 (Z91) and ZINC08992920 (Z20). Molecular docking, quantum chemical calculations (HF/6-31G**) and Pearson’s correlation have been performed. Molecular docking results of Z91 and Z20 showed both the lower binding affinity (BA) and inhibition constant (Ki) values for the receptor-ligand interactions in the three tested enzymes (cytochrome P450—CP450, myeloperoxidase—MP and NADPH oxidase—NO) than the control molecules (5-fluorouracil—FLU, melatonin—MEL and dextromethorphan—DEX, for each receptor respectively). Molecular descriptors were correlated with Ki and strong correlations were observed for the CP450, MP and NO receptors. These and other results attest the significant antioxidant ability of Z91 and Z20, that may be indicated for further analyses in relation to the control of oxidative stress and as possible antioxidant agents to be used in the pharmaceutical industry. Full article
(This article belongs to the Special Issue Computational Approaches for Drug Discovery)
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22 pages, 5955 KiB  
Article
A Comprehensive In Silico Method to Study the QSTR of the Aconitine Alkaloids for Designing Novel Drugs
by Ming-Yang Wang, Jing-Wei Liang, Kamara Mohamed Olounfeh, Qi Sun, Nan Zhao and Fan-Hao Meng
Molecules 2018, 23(9), 2385; https://doi.org/10.3390/molecules23092385 - 18 Sep 2018
Cited by 15 | Viewed by 5457
Abstract
A combined in silico method was developed to predict potential protein targets that are involved in cardiotoxicity induced by aconitine alkaloids and to study the quantitative structure–toxicity relationship (QSTR) of these compounds. For the prediction research, a Protein-Protein Interaction (PPI) network was built [...] Read more.
A combined in silico method was developed to predict potential protein targets that are involved in cardiotoxicity induced by aconitine alkaloids and to study the quantitative structure–toxicity relationship (QSTR) of these compounds. For the prediction research, a Protein-Protein Interaction (PPI) network was built from the extraction of useful information about protein interactions connected with aconitine cardiotoxicity, based on nearly a decade of literature and the STRING database. The software Cytoscape and the PharmMapper server were utilized to screen for essential proteins in the constructed network. The Calcium-Calmodulin-Dependent Protein Kinase II alpha (CAMK2A) and gamma (CAMK2G) were identified as potential targets. To obtain a deeper insight on the relationship between the toxicity and the structure of aconitine alkaloids, the present study utilized QSAR models built in Sybyl software that possess internal robustness and external high predictions. The molecular dynamics simulation carried out here have demonstrated that aconitine alkaloids possess binding stability for the receptor CAMK2G. In conclusion, this comprehensive method will serve as a tool for following a structural modification of the aconitine alkaloids and lead to a better insight into the cardiotoxicity induced by the compounds that have similar structures to its derivatives. Full article
(This article belongs to the Special Issue Computational Approaches for Drug Discovery)
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30 pages, 4934 KiB  
Review
An Overview of Molecular Modeling for Drug Discovery with Specific Illustrative Examples of Applications
by Maral Aminpour, Carlo Montemagno and Jack A. Tuszynski
Molecules 2019, 24(9), 1693; https://doi.org/10.3390/molecules24091693 - 30 Apr 2019
Cited by 105 | Viewed by 14594
Abstract
In this paper we review the current status of high-performance computing applications in the general area of drug discovery. We provide an introduction to the methodologies applied at atomic and molecular scales, followed by three specific examples of implementation of these tools. The [...] Read more.
In this paper we review the current status of high-performance computing applications in the general area of drug discovery. We provide an introduction to the methodologies applied at atomic and molecular scales, followed by three specific examples of implementation of these tools. The first example describes in silico modeling of the adsorption of small molecules to organic and inorganic surfaces, which may be applied to drug delivery issues. The second example involves DNA translocation through nanopores with major significance to DNA sequencing efforts. The final example offers an overview of computer-aided drug design, with some illustrative examples of its usefulness. Full article
(This article belongs to the Special Issue Computational Approaches for Drug Discovery)
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