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Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics

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

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 68235

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Dr. Marco Tutone

E-Mail Website
Guest Editor
Department of Biological, Chemical and Pharmaceutical Science and Technology (STEBICEF), University of Palermo, Palermo, Italy
Interests: oxidative stress; nutraceuticals; anticancer drugs; medicinal chemistry; drug design and discovery; molecular modeling; QSAR; pharmacophore modeling; molecular dynamics; docking; HTVS; cystic fibrosis translational readthrough inducing drugs (TRIDs)
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Anna Maria Almerico

E-Mail Website
Guest Editor
Dipartimento di Scienze e Tecnologie Biologiche Chimiche e Farmaceutiche (STEBICEF), Università di Palermo, Palermo, Italy
Interests: medicinal chemistry; molecular modeling; QSAR; pharmacophore modeling; molecular dynamics; docking
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Understanding ligand-receptor interaction is a key feature in all the research fields related to drug design and discovery. This issue will focus on computational approaches that can improve the development of in silico methodologies, a primary need in pharmaceutical sciences. The ongoing and widespread discovery of new biological targets suitable for therapeutic intervention should be paralleled by a high and fast development of newly discovered ligands (potential drugs) or the repurposing of an old drug for the treatment of new diseases.

In this light, all the computational approaches, such as docking, induced-fit docking, molecular dynamics simulations, free energy calculations, and reverse modeling, represent efficient tools to obtain insights on structure-function relationships for small molecules and/or natural compounds, including also the ligand-based approaches, such as molecular similarity fingerprints, shape methods, pharmacophore modeling, and QSAR, extensively used in the hit/lead identification and optimization.

Not forgetting that drug design and the development process strives to predict the metabolic fate of a drug candidate to establish a relationship between the pharmacodynamics and pharmacokinetics and to highlight the potential toxicity of the drug candidate. In recent years, the improvement of the in silico approaches allows researchers also to obtain more reliable data. As such, this Special Issue welcomes submissions from researchers in the field of computational drug discovery and design, including original research and review articles related to pharmaceutical sciences, pharmacology, chemical biology, and bioinformatics.

Prof. Dr. Marco Tutone
Prof. Dr. Anna Maria Almerico
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. 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 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • QSAR and 3D-QSAR
  • Unbiased and biased molecular dynamics
  • Pharmacophore modeling
  • Reverse modeling
  • Ab initio calculations
  • Protein-Protein interactions
  • Free energy profiling
  • Modeling of nucleic acids (mRNA, rRNA, tRNA)
  • Molecular docking
  • Virtual screening
  • QM/MM approaches
  • Multi-target approaches
  • ADMET prediction
  • Similarity analysis
  • Computational approaches applied to natural compounds
  • Machine learning techniques
  • Neural networks

Published Papers (21 papers)

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Editorial

Jump to: Research

4 pages, 190 KiB  
Editorial
Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics
by Marco Tutone and Anna Maria Almerico
Molecules 2021, 26(24), 7500; https://doi.org/10.3390/molecules26247500 - 11 Dec 2021
Cited by 11 | Viewed by 2851
Abstract
To date, computational approaches have been recognized as a key component in drug design and discovery workflows [...] Full article
(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics)

Research

Jump to: Editorial

19 pages, 3756 KiB  
Article
Immunoproteasome and Non-Covalent Inhibition: Exploration by Advanced Molecular Dynamics and Docking Methods
by Giulia Culletta, Maria Zappalà, Roberta Ettari, Anna Maria Almerico and Marco Tutone
Molecules 2021, 26(13), 4046; https://doi.org/10.3390/molecules26134046 - 02 Jul 2021
Cited by 3 | Viewed by 2612
Abstract
The selective inhibition of immunoproteasome is a valuable strategy to treat autoimmune, inflammatory diseases, and hematologic malignancies. Recently, a new series of amide derivatives as non-covalent inhibitors of the β1i subunit with Ki values in the low/submicromolar ranges have been identified. Here, [...] Read more.
The selective inhibition of immunoproteasome is a valuable strategy to treat autoimmune, inflammatory diseases, and hematologic malignancies. Recently, a new series of amide derivatives as non-covalent inhibitors of the β1i subunit with Ki values in the low/submicromolar ranges have been identified. Here, we investigated the binding mechanism of the most potent and selective inhibitor, N-benzyl-2-(2-oxopyridin-1(2H)-yl)propanamide (1), to elucidate the steps from the ligand entrance into the binding pocket to the ligand-induced conformational changes. We carried out a total of 400 ns of MD-binding analyses, followed by 200 ns of plain MD. The trajectories clustering allowed identifying three representative poses evidencing new key interactions with Phe31 and Lys33 together in a flipped orientation of a representative pose. Further, Binding Pose MetaDynamics (BPMD) studies were performed to evaluate the binding stability, comparing 1 with four other inhibitors of the β1i subunit: N-benzyl-2-(2-oxopyridin-1(2H)-yl)acetamide (2), N-cyclohexyl-3-(2-oxopyridin-1(2H)-yl)propenamide (3), N-butyl-3-(2-oxopyridin-1(2H)-yl)propanamide (4), and (S)-2-(2-oxopyridin-1(2H)-yl)-N,4-diphenylbutanamide (5). The obtained results in terms of free binding energy were consistent with the experimental values of inhibition, confirming 1 as a lead compound of this series. The adopted methods provided a full dynamic description of the binding events, and the information obtained could be exploited for the rational design of new and more active inhibitors. Full article
(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics)
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28 pages, 2686 KiB  
Article
Multiple-Molecule Drug Design Based on Systems Biology Approaches and Deep Neural Network to Mitigate Human Skin Aging
by Shan-Ju Yeh, Jin-Fu Lin and Bor-Sen Chen
Molecules 2021, 26(11), 3178; https://doi.org/10.3390/molecules26113178 - 26 May 2021
Cited by 8 | Viewed by 2722
Abstract
Human skin aging is affected by various biological signaling pathways, microenvironment factors and epigenetic regulations. With the increasing demand for cosmetics and pharmaceuticals to prevent or reverse skin aging year by year, designing multiple-molecule drugs for mitigating skin aging is indispensable. In this [...] Read more.
Human skin aging is affected by various biological signaling pathways, microenvironment factors and epigenetic regulations. With the increasing demand for cosmetics and pharmaceuticals to prevent or reverse skin aging year by year, designing multiple-molecule drugs for mitigating skin aging is indispensable. In this study, we developed strategies for systems medicine design based on systems biology methods and deep neural networks. We constructed the candidate genomewide genetic and epigenetic network (GWGEN) via big database mining. After doing systems modeling and applying system identification, system order detection and principle network projection methods with real time-profile microarray data, we could obtain core signaling pathways and identify essential biomarkers based on the skin aging molecular progression mechanisms. Afterwards, we trained a deep neural network of drug–target interaction in advance and applied it to predict the potential candidate drugs based on our identified biomarkers. To narrow down the candidate drugs, we designed two filters considering drug regulation ability and drug sensitivity. With the proposed systems medicine design procedure, we not only shed the light on the skin aging molecular progression mechanisms but also suggested two multiple-molecule drugs for mitigating human skin aging from young adulthood to middle age and middle age to old age, respectively. Full article
(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics)
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16 pages, 2388 KiB  
Article
Multiscale Virtual Screening Optimization for Shotgun Drug Repurposing Using the CANDO Platform
by Matthew L. Hudson and Ram Samudrala
Molecules 2021, 26(9), 2581; https://doi.org/10.3390/molecules26092581 - 28 Apr 2021
Cited by 8 | Viewed by 2385
Abstract
Drug repurposing, the practice of utilizing existing drugs for novel clinical indications, has tremendous potential for improving human health outcomes and increasing therapeutic development efficiency. The goal of multi-disease multitarget drug repurposing, also known as shotgun drug repurposing, is to develop platforms that [...] Read more.
Drug repurposing, the practice of utilizing existing drugs for novel clinical indications, has tremendous potential for improving human health outcomes and increasing therapeutic development efficiency. The goal of multi-disease multitarget drug repurposing, also known as shotgun drug repurposing, is to develop platforms that assess the therapeutic potential of each existing drug for every clinical indication. Our Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget repurposing implements several pipelines for the large-scale modeling and simulation of interactions between comprehensive libraries of drugs/compounds and protein structures. In these pipelines, each drug is described by an interaction signature that is compared to all other signatures that are subsequently sorted and ranked based on similarity. Pipelines within the platform are benchmarked based on their ability to recover known drugs for all indications in our library, and predictions are generated based on the hypothesis that (novel) drugs with similar signatures may be repurposed for the same indication(s). The drug-protein interactions used to create the drug-proteome signatures may be determined by any screening or docking method, but the primary approach used thus far has been BANDOCK, our in-house bioanalytical or similarity docking protocol. In this study, we calculated drug-proteome interaction signatures using the publicly available molecular docking method Autodock Vina and created hybrid decision tree pipelines that combined our original bio- and chem-informatic approach with the goal of assessing and benchmarking their drug repurposing capabilities and performance. The hybrid decision tree pipeline outperformed the two docking-based pipelines from which it was synthesized, yielding an average indication accuracy of 13.3% at the top10 cutoff (the most stringent), relative to 10.9% and 7.1% for its constituent pipelines, and a random control accuracy of 2.2%. We demonstrate that docking-based virtual screening pipelines have unique performance characteristics and that the CANDO shotgun repurposing paradigm is not dependent on a specific docking method. Our results also provide further evidence that multiple CANDO pipelines can be synthesized to enhance drug repurposing predictive capability relative to their constituent pipelines. Overall, this study indicates that pipelines consisting of varied docking-based signature generation methods can capture unique and useful signals for accurate comparison of drug-proteome interaction signatures, leading to improvements in the benchmarking and predictive performance of the CANDO shotgun drug repurposing platform. Full article
(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics)
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13 pages, 3222 KiB  
Article
Identification of New Mycobacterium tuberculosis Proteasome Inhibitors Using a Knowledge-Based Computational Screening Approach
by Tahani M. Almeleebia, Mesfer Al Shahrani, Mohammad Y. Alshahrani, Irfan Ahmad, Abdullah M. Alkahtani, Md Jahoor Alam, Mohd Adnan Kausar, Amir Saeed, Mohd Saeed and Sana Iram
Molecules 2021, 26(8), 2326; https://doi.org/10.3390/molecules26082326 - 16 Apr 2021
Cited by 9 | Viewed by 1964
Abstract
Mycobacterium tuberculosis (Mtb) is a deadly tuberculosis (TB)-causing pathogen. The proteasome is vital to the survival of Mtb and is therefore validated as a potential target for anti-TB therapy. Mtb resistance to existing antibacterial agents has enhanced drastically, becoming a worldwide health issue. [...] Read more.
Mycobacterium tuberculosis (Mtb) is a deadly tuberculosis (TB)-causing pathogen. The proteasome is vital to the survival of Mtb and is therefore validated as a potential target for anti-TB therapy. Mtb resistance to existing antibacterial agents has enhanced drastically, becoming a worldwide health issue. Therefore, new potential therapeutic agents need to be developed that can overcome the complications of TB. With this purpose, in the present study, 224,205 natural compounds from the ZINC database have been screened against the catalytic site of Mtb proteasome by the computational approach. The best scoring hits, ZINC3875469, ZINC4076131, and ZINC1883067, demonstrated robust interaction with Mtb proteasome with binding energy values of −7.19, −7.95, and −7.21 kcal/mol for the monomer (K-chain) and −8.05, −9.10, and −7.07 kcal/mol for the dimer (both K and L chains) of the beta subunit, which is relatively higher than that of reference compound HT1171 (−5.83 kcal/mol (monomer) and −5.97 kcal/mol (dimer)). In-depth molecular docking of top-scoring compounds with Mtb proteasome reveals that amino acid residues Thr1, Arg19, Ser20, Thr21, Gln22, Gly23, Asn24, Lys33, Gly47, Asp124, Ala126, Trp129, and Ala180 are crucial in binding. Furthermore, a molecular dynamics study showed steady-state interaction of hit compounds with Mtb proteasome. Computational prediction of physicochemical property assessment showed that these hits are non-toxic and possess good drug-likeness properties. This study proposed that these compounds could be utilized as potential inhibitors of Mtb proteasome to combat TB infection. However, there is a need for further bench work experiments for their validation as inhibitors of Mtb proteasome. Full article
(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics)
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12 pages, 4525 KiB  
Article
Is PF-00835231 a Pan-SARS-CoV-2 Mpro Inhibitor? A Comparative Study
by Mohammad Hassan Baig, Tanuj Sharma, Irfan Ahmad, Mohammed Abohashrh, Mohammad Mahtab Alam and Jae-June Dong
Molecules 2021, 26(6), 1678; https://doi.org/10.3390/molecules26061678 - 17 Mar 2021
Cited by 18 | Viewed by 4002
Abstract
The COVID-19 outbreak continues to spread worldwide at a rapid rate. Currently, the absence of any effective antiviral treatment is the major concern for the global population. The reports of the occurrence of various point mutations within the important therapeutic target protein of [...] Read more.
The COVID-19 outbreak continues to spread worldwide at a rapid rate. Currently, the absence of any effective antiviral treatment is the major concern for the global population. The reports of the occurrence of various point mutations within the important therapeutic target protein of SARS-CoV-2 has elevated the problem. The SARS-CoV-2 main protease (Mpro) is a major therapeutic target for new antiviral designs. In this study, the efficacy of PF-00835231 was investigated (a Mpro inhibitor under clinical trials) against the Mpro and their reported mutants. Various in silico approaches were used to investigate and compare the efficacy of PF-00835231 and five drugs previously documented to inhibit the Mpro. Our study shows that PF-00835231 is not only effective against the wild type but demonstrates a high affinity against the studied mutants as well. Full article
(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics)
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18 pages, 5427 KiB  
Article
Doxorubicin Encapsulation in Carbon Nanotubes Having Haeckelite or Stone–Wales Defects as Drug Carriers: A Molecular Dynamics Approach
by Leonor Contreras, Ignacio Villarroel, Camila Torres and Roberto Rozas
Molecules 2021, 26(6), 1586; https://doi.org/10.3390/molecules26061586 - 13 Mar 2021
Cited by 7 | Viewed by 1912
Abstract
Doxorubicin (DOX), a recognized anticancer drug, forms stable associations with carbon nanotubes (CNTs). CNTs when properly functionalized have the ability to anchor directly in cancerous tumors where the release of the drug occurs thanks to the tumor slightly acidic pH. Herein, we study [...] Read more.
Doxorubicin (DOX), a recognized anticancer drug, forms stable associations with carbon nanotubes (CNTs). CNTs when properly functionalized have the ability to anchor directly in cancerous tumors where the release of the drug occurs thanks to the tumor slightly acidic pH. Herein, we study the armchair and zigzag CNTs with Stone–Wales (SW) defects to rank their ability to encapsulate DOX by determining the DOX-CNT binding free energies using the MM/PBSA and MM/GBSA methods implemented in AMBER16. We investigate also the chiral CNTs with haeckelite defects. Each haeckelite defect consists of a pair of square and octagonal rings. The armchair and zigzag CNT with SW defects and chiral nanotubes with haeckelite defects predict DOX-CNT interactions that depend on the length of the nanotube, the number of present defects and nitrogen doping. Chiral nanotubes having two haeckelite defects reveal a clear dependence on the nitrogen content with DOX-CNT interaction forces decreasing in the order 0N > 4N > 8N. These results contribute to a further understanding of drug-nanotube interactions and to the design of new drug delivery systems based on CNTs. Full article
(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics)
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23 pages, 11949 KiB  
Article
The Interplay of Cholesterol and Ligand Binding in hTSPO from Classical Molecular Dynamics Simulations
by Hien T. T. Lai, Alejandro Giorgetti, Giulia Rossetti, Toan T. Nguyen, Paolo Carloni and Agata Kranjc
Molecules 2021, 26(5), 1250; https://doi.org/10.3390/molecules26051250 - 26 Feb 2021
Cited by 6 | Viewed by 2500
Abstract
The translocator protein (TSPO) is a 18kDa transmembrane protein, ubiquitously present in human mitochondria. It is overexpressed in tumor cells and at the sites of neuroinflammation, thus representing an important biomarker, as well as a promising drug target. In mammalian TSPO, there are [...] Read more.
The translocator protein (TSPO) is a 18kDa transmembrane protein, ubiquitously present in human mitochondria. It is overexpressed in tumor cells and at the sites of neuroinflammation, thus representing an important biomarker, as well as a promising drug target. In mammalian TSPO, there are cholesterol–binding motifs, as well as a binding cavity able to accommodate different chemical compounds. Given the lack of structural information for the human protein, we built a model of human (h) TSPO in the apo state and in complex with PK11195, a molecule routinely used in positron emission tomography (PET) for imaging of neuroinflammatory sites. To better understand the interactions of PK11195 and cholesterol with this pharmacologically relevant protein, we ran molecular dynamics simulations of the apo and holo proteins embedded in a model membrane. We found that: (i) PK11195 stabilizes hTSPO structural fold; (ii) PK11195 might enter in the binding site through transmembrane helices I and II of hTSPO; (iii) PK11195 reduces the frequency of cholesterol binding to the lower, N–terminal part of hTSPO in the inner membrane leaflet, while this impact is less pronounced for the upper, C–terminal part in the outer membrane leaflet, where the ligand binding site is located; (iv) very interestingly, cholesterol most frequently binds simultaneously to the so-called CRAC and CARC regions in TM V in the free form (residues L150–X–Y152–X(3)–R156 and R135–X(2)–Y138–X(2)–L141, respectively). However, when the protein is in complex with PK11195, cholesterol binds equally frequently to the CRAC–resembling motif that we observed in TM I (residues L17–X(2)–F20–X(3)–R24) and to CRAC in TM V. We expect that the CRAC–like motif in TM I will be of interest in future experimental investigations. Thus, our MD simulations provide insight into the structural features of hTSPO and the previously unknown interplay between PK11195 and cholesterol interactions with this pharmacologically relevant protein. Full article
(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics)
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23 pages, 10284 KiB  
Article
Study of Endogen Substrates, Drug Substrates and Inhibitors Binding Conformations on MRP4 and Its Variants by Molecular Docking and Molecular Dynamics
by Edgardo Becerra, Giovanny Aguilera-Durán, Laura Berumen, Antonio Romo-Mancillas and Guadalupe García-Alcocer
Molecules 2021, 26(4), 1051; https://doi.org/10.3390/molecules26041051 - 17 Feb 2021
Cited by 10 | Viewed by 2765
Abstract
Multidrug resistance protein-4 (MRP4) belongs to the ABC transporter superfamily and promotes the transport of xenobiotics including drugs. A non-synonymous single nucleotide polymorphisms (nsSNPs) in the ABCC4 gene can promote changes in the structure and function of MRP4. In this work, the interaction [...] Read more.
Multidrug resistance protein-4 (MRP4) belongs to the ABC transporter superfamily and promotes the transport of xenobiotics including drugs. A non-synonymous single nucleotide polymorphisms (nsSNPs) in the ABCC4 gene can promote changes in the structure and function of MRP4. In this work, the interaction of certain endogen substrates, drug substrates, and inhibitors with wild type-MRP4 (WT-MRP4) and its variants G187W and Y556C were studied to determine differences in the intermolecular interactions and affinity related to SNPs using protein threading modeling, molecular docking, all-atom, coarse grained, and umbrella sampling molecular dynamics simulations (AA-MDS and CG-MDS, respectively). The results showed that the three MRP4 structures had significantly different conformations at given sites, leading to differences in the docking scores (DS) and binding sites of three different groups of molecules. Folic acid (FA) had the highest variation in DS on G187W concerning WT-MRP4. WT-MRP4, G187W, Y556C, and FA had different conformations through 25 ns AA-MD. Umbrella sampling simulations indicated that the Y556C-FA complex was the most stable one with or without ATP. In Y556C, the cyclic adenosine monophosphate (cAMP) and ceefourin-1 binding sites are located out of the entrance of the inner cavity, which suggests that both cAMP and ceefourin-1 may not be transported. The binding site for cAMP and ceefourin-1 is quite similar and the affinity (binding energy) of ceefourin-1 to WT-MRP4, G187W, and Y556C is greater than the affinity of cAMP, which may suggest that ceefourin-1 works as a competitive inhibitor. In conclusion, the nsSNPs G187W and Y556C lead to changes in protein conformation, which modifies the ligand binding site, DS, and binding energy. Full article
(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics)
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24 pages, 1679 KiB  
Article
Combining Different Docking Engines and Consensus Strategies to Design and Validate Optimized Virtual Screening Protocols for the SARS-CoV-2 3CL Protease
by Candida Manelfi, Jonas Gossen, Silvia Gervasoni, Carmine Talarico, Simone Albani, Benjamin Joseph Philipp, Francesco Musiani, Giulio Vistoli, Giulia Rossetti, Andrea Rosario Beccari and Alessandro Pedretti
Molecules 2021, 26(4), 797; https://doi.org/10.3390/molecules26040797 - 04 Feb 2021
Cited by 13 | Viewed by 2849
Abstract
The 3CL-Protease appears to be a very promising medicinal target to develop anti-SARS-CoV-2 agents. The availability of resolved structures allows structure-based computational approaches to be carried out even though the lack of known inhibitors prevents a proper validation of the performed simulations. The [...] Read more.
The 3CL-Protease appears to be a very promising medicinal target to develop anti-SARS-CoV-2 agents. The availability of resolved structures allows structure-based computational approaches to be carried out even though the lack of known inhibitors prevents a proper validation of the performed simulations. The innovative idea of the study is to exploit known inhibitors of SARS-CoV 3CL-Pro as a training set to perform and validate multiple virtual screening campaigns. Docking simulations using four different programs (Fred, Glide, LiGen, and PLANTS) were performed investigating the role of both multiple binding modes (by binding space) and multiple isomers/states (by developing the corresponding isomeric space). The computed docking scores were used to develop consensus models, which allow an in-depth comparison of the resulting performances. On average, the reached performances revealed the different sensitivity to isomeric differences and multiple binding modes between the four docking engines. In detail, Glide and LiGen are the tools that best benefit from isomeric and binding space, respectively, while Fred is the most insensitive program. The obtained results emphasize the fruitful role of combining various docking tools to optimize the predictive performances. Taken together, the performed simulations allowed the rational development of highly performing virtual screening workflows, which could be further optimized by considering different 3CL-Pro structures and, more importantly, by including true SARS-CoV-2 3CL-Pro inhibitors (as learning set) when available. Full article
(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics)
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17 pages, 4115 KiB  
Article
Computational Determination of Potential Multiprotein Targeting Natural Compounds for Rational Drug Design Against SARS-COV-2
by Ziyad Tariq Muhseen, Alaa R. Hameed, Halah M. H. Al-Hasani, Sajjad Ahmad and Guanglin Li
Molecules 2021, 26(3), 674; https://doi.org/10.3390/molecules26030674 - 28 Jan 2021
Cited by 26 | Viewed by 4037
Abstract
SARS-CoV-2 caused the current COVID-19 pandemic and there is an urgent need to explore effective therapeutics that can inhibit enzymes that are imperative in virus reproduction. To this end, we computationally investigated the MPD3 phytochemical database along with the pool of reported natural [...] Read more.
SARS-CoV-2 caused the current COVID-19 pandemic and there is an urgent need to explore effective therapeutics that can inhibit enzymes that are imperative in virus reproduction. To this end, we computationally investigated the MPD3 phytochemical database along with the pool of reported natural antiviral compounds with potential to be used as anti-SARS-CoV-2. The docking results demonstrated glycyrrhizin followed by azadirachtanin, mycophenolic acid, kushenol-w and 6-azauridine, as potential candidates. Glycyrrhizin depicted very stable binding mode to the active pocket of the Mpro (binding energy, −8.7 kcal/mol), PLpro (binding energy, −7.9 kcal/mol), and Nucleocapsid (binding energy, −7.9 kcal/mol) enzymes. This compound showed binding with several key residues that are critical to natural substrate binding and functionality to all the receptors. To test docking prediction, the compound with each receptor was subjected to molecular dynamics simulation to characterize the molecule stability and decipher its possible mechanism of binding. Each complex concludes that the receptor dynamics are stable (Mpro (mean RMSD, 0.93 Å), PLpro (mean RMSD, 0.96 Å), and Nucleocapsid (mean RMSD, 3.48 Å)). Moreover, binding free energy analyses such as MMGB/PBSA and WaterSwap were run over selected trajectory snapshots to affirm intermolecular affinity in the complexes. Glycyrrhizin was rescored to form strong affinity complexes with the virus enzymes: Mpro (MMGBSA, −24.42 kcal/mol and MMPBSA, −10.80 kcal/mol), PLpro (MMGBSA, −48.69 kcal/mol and MMPBSA, −38.17 kcal/mol) and Nucleocapsid (MMGBSA, −30.05 kcal/mol and MMPBSA, −25.95 kcal/mol), were dominated mainly by vigorous van der Waals energy. Further affirmation was achieved by WaterSwap absolute binding free energy that concluded all the complexes in good equilibrium and stability (Mpro (mean, −22.44 kcal/mol), PLpro (mean, −25.46 kcal/mol), and Nucleocapsid (mean, −23.30 kcal/mol)). These promising findings substantially advance our understanding of how natural compounds could be shaped to counter SARS-CoV-2 infection. Full article
(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics)
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26 pages, 17499 KiB  
Article
Structural Impacts of Drug-Resistance Mutations Appearing in HIV-2 Protease
by Pierre Laville, Michel Petitjean and Leslie Regad
Molecules 2021, 26(3), 611; https://doi.org/10.3390/molecules26030611 - 25 Jan 2021
Cited by 3 | Viewed by 1782
Abstract
The use of antiretroviral drugs is accompanied by the emergence of HIV-2 resistances. Thus, it is important to elucidate the mechanisms of resistance to antiretroviral drugs. Here, we propose a structural analysis of 31 drug-resistant mutants of HIV-2 protease (PR2) that is an [...] Read more.
The use of antiretroviral drugs is accompanied by the emergence of HIV-2 resistances. Thus, it is important to elucidate the mechanisms of resistance to antiretroviral drugs. Here, we propose a structural analysis of 31 drug-resistant mutants of HIV-2 protease (PR2) that is an important target against HIV-2 infection. First, we modeled the structures of each mutant. We then located structural shifts putatively induced by mutations. Finally, we compared wild-type and mutant inhibitor-binding pockets and interfaces to explore the impacts of these induced structural deformations on these two regions. Our results showed that one mutation could induce large structural rearrangements in side-chain and backbone atoms of mutated residue, in its vicinity or further. Structural deformations observed in side-chain atoms are frequent and of greater magnitude, that confirms that to fight drug resistance, interactions with backbone atoms should be favored. We showed that these observed structural deformations modify the conformation, volume, and hydrophobicity of the binding pocket and the composition and size of the PR2 interface. These results suggest that resistance mutations could alter ligand binding by modifying pocket properties and PR2 stability by impacting its interface. Our results reinforce the understanding of the effects of mutations that occurred in PR2 and the different mechanisms of PR2 resistance. Full article
(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics)
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18 pages, 6871 KiB  
Article
Biocomputational Prediction Approach Targeting FimH by Natural SGLT2 Inhibitors: A Possible Way to Overcome the Uropathogenic Effect of SGLT2 Inhibitor Drugs
by Mutaib M. Mashraqi, Navaneet Chaturvedi, Qamre Alam, Saleh Alshamrani, Mosa M. Bahnass, Khurshid Ahmad, Amany I. Alqosaibi, Mashael M. Alnamshan, Syed Sayeed Ahmad, Mirza Masroor Ali Beg, Abha Mishra, Sibhghatulla Shaikh and Syed Mohd Danish Rizvi
Molecules 2021, 26(3), 582; https://doi.org/10.3390/molecules26030582 - 22 Jan 2021
Cited by 9 | Viewed by 3048
Abstract
The Food and Drug Administration (FDA) approved a new class of anti-diabetic medication (a sodium–glucose co-transporter 2 (SGLT2) inhibitor) in 2013. However, SGLT2 inhibitor drugs are under evaluation due to their associative side effects, such as urinary tract and genital infection, urinary discomfort, [...] Read more.
The Food and Drug Administration (FDA) approved a new class of anti-diabetic medication (a sodium–glucose co-transporter 2 (SGLT2) inhibitor) in 2013. However, SGLT2 inhibitor drugs are under evaluation due to their associative side effects, such as urinary tract and genital infection, urinary discomfort, diabetic ketosis, and kidney problems. Even clinicians have difficulty in recommending it to diabetic patients due to the increased probability of urinary tract infection. In our study, we selected natural SGLT2 inhibitors, namely acerogenin B, formononetin, (−)-kurarinone, (+)-pteryxin, and quinidine, to explore their potential against an emerging uropathogenic bacterial therapeutic target, i.e., FimH. FimH plays a critical role in the colonization of uropathogenic bacteria on the urinary tract surface. Thus, FimH antagonists show promising effects against uropathogenic bacterial strains via their targeting of FimH’s adherence mechanism with less chance of resistance. The molecular docking results showed that, among natural SGLT2 inhibitors, formononetin, (+)-pteryxin, and quinidine have a strong interaction with FimH proteins, with binding energy (∆G) and inhibition constant (ki) values of −5.65 kcal/mol and 71.95 µM, −5.50 kcal/mol and 92.97 µM, and −5.70 kcal/mol and 66.40 µM, respectively. These interactions were better than those of the positive control heptyl α-d-mannopyranoside and far better than those of the SGLT2 inhibitor drug canagliflozin. Furthermore, a 50 ns molecular dynamics simulation was conducted to optimize the interaction, and the resulting complexes were found to be stable. Physicochemical property assessments predicted little toxicity and good drug-likeness properties for these three compounds. Therefore, formononetin, (+)-pteryxin, and quinidine can be proposed as promising SGLT2 inhibitors drugs, with add-on FimH inhibition potential that might reduce the probability of uropathogenic side effects. Full article
(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics)
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32 pages, 5920 KiB  
Article
Identification of Potential COX-2 Inhibitors for the Treatment of Inflammatory Diseases Using Molecular Modeling Approaches
by Pedro H. F. Araújo, Ryan S. Ramos, Jorddy N. da Cruz, Sebastião G. Silva, Elenilze F. B. Ferreira, Lúcio R. de Lima, Williams J. C. Macêdo, José M. Espejo-Román, Joaquín M. Campos and Cleydson B. R. Santos
Molecules 2020, 25(18), 4183; https://doi.org/10.3390/molecules25184183 - 12 Sep 2020
Cited by 45 | Viewed by 6015
Abstract
Non-steroidal anti-inflammatory drugs are inhibitors of cyclooxygenase-2 (COX-2) that were developed in order to avoid the side effects of non-selective inhibitors of COX-1. Thus, the present study aims to identify new selective chemical entities for the COX-2 enzyme via molecular modeling approaches. The [...] Read more.
Non-steroidal anti-inflammatory drugs are inhibitors of cyclooxygenase-2 (COX-2) that were developed in order to avoid the side effects of non-selective inhibitors of COX-1. Thus, the present study aims to identify new selective chemical entities for the COX-2 enzyme via molecular modeling approaches. The best pharmacophore model was used to identify compounds within the ZINC database. The molecular properties were determined and selected with Pearson’s correlation for the construction of quantitative structure–activity relationship (QSAR) models to predict the biological activities of the compounds obtained with virtual screening. The pharmacokinetic/toxicological profiles of the compounds were determined, as well as the binding modes through molecular docking compared to commercial compounds (rofecoxib and celecoxib). The QSAR analysis showed a fit with R = 0.9617, R2 = 0.9250, standard error of estimate (SEE) = 0.2238, and F = 46.2739, with the tetra-parametric regression model. After the analysis, only three promising inhibitors were selected, Z-964, Z-627, and Z-814, with their predicted pIC50 (−log IC50) values, Z-814 = 7.9484, Z-627 = 9.3458, and Z-964 = 9.5272. All candidates inhibitors complied with Lipinski’s rule of five, which predicts a good oral availability and can be used in in vitro and in vivo tests in the zebrafish model in order to confirm the obtained in silico data. Full article
(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics)
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13 pages, 7329 KiB  
Article
Identification of Novel Src Inhibitors: Pharmacophore-Based Virtual Screening, Molecular Docking and Molecular Dynamics Simulations
by Yi Zhang, Ting-jian Zhang, Shun Tu, Zhen-hao Zhang and Fan-hao Meng
Molecules 2020, 25(18), 4094; https://doi.org/10.3390/molecules25184094 - 08 Sep 2020
Cited by 28 | Viewed by 3389
Abstract
Src plays a crucial role in many signaling pathways and contributes to a variety of cancers. Therefore, Src has long been considered an attractive drug target in oncology. However, the development of Src inhibitors with selectivity and novelty has been challenging. In the [...] Read more.
Src plays a crucial role in many signaling pathways and contributes to a variety of cancers. Therefore, Src has long been considered an attractive drug target in oncology. However, the development of Src inhibitors with selectivity and novelty has been challenging. In the present study, pharmacophore-based virtual screening and molecular docking were carried out to identify potential Src inhibitors. A total of 891 molecules were obtained after pharmacophore-based virtual screening, and 10 molecules with high docking scores and strong interactions were selected as potential active molecules for further study. Absorption, distribution, metabolism, elimination and toxicity (ADMET) property evaluation was used to ascertain the drug-like properties of the obtained molecules. The proposed inhibitor–protein complexes were further subjected to molecular dynamics (MD) simulations involving root-mean-square deviation and root-mean-square fluctuation to explore the binding mode stability inside active pockets. Finally, two molecules (ZINC3214460 and ZINC1380384) were obtained as potential lead compounds against Src kinase. All these analyses provide a reference for the further development of novel Src inhibitors. Full article
(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics)
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19 pages, 11997 KiB  
Article
Characterizing Epitope Binding Regions of Entire Antibody Panels by Combining Experimental and Computational Analysis of Antibody: Antigen Binding Competition
by Benjamin D. Brooks, Adam Closmore, Juechen Yang, Michael Holland, Tina Cairns, Gary H. Cohen and Chris Bailey-Kellogg
Molecules 2020, 25(16), 3659; https://doi.org/10.3390/molecules25163659 - 11 Aug 2020
Cited by 5 | Viewed by 4028
Abstract
Vaccines and immunotherapies depend on the ability of antibodies to sensitively and specifically recognize particular antigens and specific epitopes on those antigens. As such, detailed characterization of antibody–antigen binding provides important information to guide development. Due to the time and expense required, high-resolution [...] Read more.
Vaccines and immunotherapies depend on the ability of antibodies to sensitively and specifically recognize particular antigens and specific epitopes on those antigens. As such, detailed characterization of antibody–antigen binding provides important information to guide development. Due to the time and expense required, high-resolution structural characterization techniques are typically used sparingly and late in a development process. Here, we show that antibody–antigen binding can be characterized early in a process for whole panels of antibodies by combining experimental and computational analyses of competition between monoclonal antibodies for binding to an antigen. Experimental “epitope binning” of monoclonal antibodies uses high-throughput surface plasmon resonance to reveal which antibodies compete, while a new complementary computational analysis that we call “dock binning” evaluates antibody–antigen docking models to identify why and where they might compete, in terms of possible binding sites on the antigen. Experimental and computational characterization of the identified antigenic hotspots then enables the refinement of the competitors and their associated epitope binding regions on the antigen. While not performed at atomic resolution, this approach allows for the group-level identification of functionally related monoclonal antibodies (i.e., communities) and identification of their general binding regions on the antigen. By leveraging extensive epitope characterization data that can be readily generated both experimentally and computationally, researchers can gain broad insights into the basis for antibody–antigen recognition in wide-ranging vaccine and immunotherapy discovery and development programs. Full article
(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics)
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22 pages, 3808 KiB  
Article
In Silico Study Identified Methotrexate Analog as Potential Inhibitor of Drug Resistant Human Dihydrofolate Reductase for Cancer Therapeutics
by Rabia Mukhtar Rana, Shailima Rampogu, Noman Bin Abid, Amir Zeb, Shraddha Parate, Gihwan Lee, Sanghwa Yoon, Yumi Kim, Donghwan Kim and Keun Woo Lee
Molecules 2020, 25(15), 3510; https://doi.org/10.3390/molecules25153510 - 31 Jul 2020
Cited by 13 | Viewed by 4165
Abstract
Drug resistance is a core issue in cancer chemotherapy. A known folate antagonist, methotrexate (MTX) inhibits human dihydrofolate reductase (hDHFR), the enzyme responsible for the catalysis of 7,8-dihydrofolate reduction to 5,6,7,8-tetrahydrofolate, in biosynthesis and cell proliferation. Structural change in the DHFR enzyme is [...] Read more.
Drug resistance is a core issue in cancer chemotherapy. A known folate antagonist, methotrexate (MTX) inhibits human dihydrofolate reductase (hDHFR), the enzyme responsible for the catalysis of 7,8-dihydrofolate reduction to 5,6,7,8-tetrahydrofolate, in biosynthesis and cell proliferation. Structural change in the DHFR enzyme is a significant cause of resistance and the subsequent loss of MTX. In the current study, wild type hDHFR and double mutant (engineered variant) F31R/Q35E (PDB ID: 3EIG) were subject to computational study. Structure-based pharmacophore modeling was carried out for wild type (WT) and mutant (MT) (variant F31R/Q35E) hDHFR structures by generating ten models for each. Two pharmacophore models, WT-pharma and MT-pharma, were selected for further computations, and showed excellent ROC curve quality. Additionally, the selected pharmacophore models were validated by the Guner-Henry decoy test method, which yielded high goodness of fit for WT-hDHFR and MT-hDHFR. Using a SMILES string of MTX in ZINC15 with the selections of ‘clean’, in vitro and in vivo options, 32 MTX-analogs were obtained. Eight analogs were filtered out due to their drug-like properties by applying absorption, distribution, metabolism, excretion, and toxicity (ADMET) assessment tests and Lipinski’s Rule of five. WT-pharma and MT-pharma were further employed as a 3D query in virtual screening with drug-like MTX analogs. Subsequently, seven screening hits along with a reference compound (MTX) were subjected to molecular docking in the active site of WT- and MT-hDHFR. Through a clustering analysis and examination of protein-ligand interactions, one compound was found with a ChemPLP fitness score greater than that of MTX (reference compound). Finally, a simulation of molecular dynamics (MD) identified an MTX analog which exhibited strong affinity for WT- and MT-hDHFR, with stable RMSD, hydrogen bonds (H-bonds) in the binding site and the lowest MM/PBSA binding free energy. In conclusion, we report on an MTX analog which is capable of inhibiting hDHFR in wild type form, as well as in cases where the enzyme acquires resistance to drugs during chemotherapy treatment. Full article
(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics)
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20 pages, 6090 KiB  
Article
Structure-Based Discovery of Dual-Target Hits for Acetylcholinesterase and the α7 Nicotinic Acetylcholine Receptors: In Silico Studies and In Vitro Confirmation
by Sebastian Oddsson, Natalia M. Kowal, Philip K. Ahring, Elin S. Olafsdottir and Thomas Balle
Molecules 2020, 25(12), 2872; https://doi.org/10.3390/molecules25122872 - 22 Jun 2020
Cited by 10 | Viewed by 3272
Abstract
Despite extensive efforts in the development of drugs for complex neurodegenerative diseases, treatment often remains challenging or ineffective, and hence new treatment strategies are necessary. One approach is the design of multi-target drugs, which can potentially address the complex nature of disorders such [...] Read more.
Despite extensive efforts in the development of drugs for complex neurodegenerative diseases, treatment often remains challenging or ineffective, and hence new treatment strategies are necessary. One approach is the design of multi-target drugs, which can potentially address the complex nature of disorders such as Alzheimer’s disease. We report a method for high throughput virtual screening aimed at identifying new dual target hit molecules. One of the identified hits, N,N-dimethyl-1-(4-(3-methyl-[1,2,4]triazolo[4,3-a]pyrimidin-6-yl)phenyl)ethan-1-amine (Ýmir-2), has dual-activity as an acetylcholinesterase (AChE) inhibitor and as an α7 nicotinic acetylcholine receptor (α7 nAChR) agonist. Using computational chemistry methods, parallel and independent screening of a virtual compound library consisting of 3,848,234 drug-like and commercially available molecules from the ZINC15 database, resulted in an intersecting set of 57 compounds, that potentially possess activity at both of the two protein targets. Based on ligand efficiency as well as scaffold and molecular diversity, 16 of these compounds were purchased for in vitro validation by Ellman’s method and two-electrode voltage-clamp electrophysiology. Ýmir-2 was shown to exhibit the desired activity profile (AChE IC50 = 2.58 ± 0.96 µM; α7 nAChR activation = 7.0 ± 0.9% at 200 µM) making it the first reported compound with this particular profile and providing further evidence of the feasibility of in silico methods for the identification of novel multi-target hit molecules. Full article
(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics)
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16 pages, 1288 KiB  
Article
The Performance of Gene Expression Signature-Guided Drug–Disease Association in Different Categories of Drugs and Diseases
by Xiguang Qi, Mingzhe Shen, Peihao Fan, Xiaojiang Guo, Tianqi Wang, Ning Feng, Manling Zhang, Robert A. Sweet, Levent Kirisci and Lirong Wang
Molecules 2020, 25(12), 2776; https://doi.org/10.3390/molecules25122776 - 16 Jun 2020
Cited by 4 | Viewed by 2797
Abstract
A gene expression signature (GES) is a group of genes that shows a unique expression profile as a result of perturbations by drugs, genetic modification or diseases on the transcriptional machinery. The comparisons between GES profiles have been used to investigate the relationships [...] Read more.
A gene expression signature (GES) is a group of genes that shows a unique expression profile as a result of perturbations by drugs, genetic modification or diseases on the transcriptional machinery. The comparisons between GES profiles have been used to investigate the relationships between drugs, their targets and diseases with quite a few successful cases reported. Especially in the study of GES-guided drugs–disease associations, researchers believe that if a GES induced by a drug is opposite to a GES induced by a disease, the drug may have potential as a treatment of that disease. In this study, we data-mined the crowd extracted expression of differential signatures (CREEDS) database to evaluate the similarity between GES profiles from drugs and their indicated diseases. Our study aims to explore the application domains of GES-guided drug–disease associations through the analysis of the similarity of GES profiles on known pairs of drug–disease associations, thereby identifying subgroups of drugs/diseases that are suitable for GES-guided drug repositioning approaches. Our results supported our hypothesis that the GES-guided drug–disease association method is better suited for some subgroups or pathways such as drugs and diseases associated with the immune system, diseases of the nervous system, non-chemotherapy drugs or the mTOR signaling pathway. Full article
(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics)
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10 pages, 1639 KiB  
Article
The Study on the hERG Blocker Prediction Using Chemical Fingerprint Analysis
by Kwang-Eun Choi, Anand Balupuri and Nam Sook Kang
Molecules 2020, 25(11), 2615; https://doi.org/10.3390/molecules25112615 - 04 Jun 2020
Cited by 21 | Viewed by 3169
Abstract
Human ether-a-go-go-related gene (hERG) potassium channel blockage by small molecules may cause severe cardiac side effects. Thus, it is crucial to screen compounds for activity on the hERG channels early in the drug discovery process. In this study, we collected 5299 hERG inhibitors [...] Read more.
Human ether-a-go-go-related gene (hERG) potassium channel blockage by small molecules may cause severe cardiac side effects. Thus, it is crucial to screen compounds for activity on the hERG channels early in the drug discovery process. In this study, we collected 5299 hERG inhibitors with diverse chemical structures from a number of sources. Based on this dataset, we evaluated different machine learning (ML) and deep learning (DL) algorithms using various integer and binary type fingerprints. A training set of 3991 compounds was used to develop quantitative structure–activity relationship (QSAR) models. The performance of the developed models was evaluated using a test set of 998 compounds. Models were further validated using external set 1 (263 compounds) and external set 2 (47 compounds). Overall, models with integer type fingerprints showed better performance than models with no fingerprints, converted binary type fingerprints or original binary type fingerprints. Comparison of ML and DL algorithms revealed that integer type fingerprints are suitable for ML, whereas binary type fingerprints are suitable for DL. The outcomes of this study indicate that the rational selection of fingerprints is important for hERG blocker prediction. Full article
(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics)
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20 pages, 3481 KiB  
Article
Computer-Driven Development of an in Silico Tool for Finding Selective Histone Deacetylase 1 Inhibitors
by Hajar Sirous, Giuseppe Campiani, Simone Brogi, Vincenzo Calderone and Giulia Chemi
Molecules 2020, 25(8), 1952; https://doi.org/10.3390/molecules25081952 - 22 Apr 2020
Cited by 14 | Viewed by 3082
Abstract
Histone deacetylases (HDACs) are a class of epigenetic modulators overexpressed in numerous types of cancers. Consequently, HDAC inhibitors (HDACIs) have emerged as promising antineoplastic agents. Unfortunately, the most developed HDACIs suffer from poor selectivity towards a specific isoform, limiting their clinical applicability. Among [...] Read more.
Histone deacetylases (HDACs) are a class of epigenetic modulators overexpressed in numerous types of cancers. Consequently, HDAC inhibitors (HDACIs) have emerged as promising antineoplastic agents. Unfortunately, the most developed HDACIs suffer from poor selectivity towards a specific isoform, limiting their clinical applicability. Among the isoforms, HDAC1 represents a crucial target for designing selective HDACIs, being aberrantly expressed in several malignancies. Accordingly, the development of a predictive in silico tool employing a large set of HDACIs (aminophenylbenzamide derivatives) is herein presented for the first time. Software Phase was used to derive a 3D-QSAR model, employing as alignment rule a common-features pharmacophore built on 20 highly active/selective HDAC1 inhibitors. The 3D-QSAR model was generated using 370 benzamide-based HDACIs, which yielded an excellent correlation coefficient value (R2 = 0.958) and a satisfactory predictive power (Q2 = 0.822; Q2F3 = 0.894). The model was validated (r2ext_ts = 0.794) using an external test set (113 compounds not used for generating the model), and by employing a decoys set and the receiver-operating characteristic (ROC) curve analysis, evaluating the Güner–Henry score (GH) and the enrichment factor (EF). The results confirmed a satisfactory predictive power of the 3D-QSAR model. This latter represents a useful filtering tool for screening large chemical databases, finding novel derivatives with improved HDAC1 inhibitory activity. Full article
(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics)
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