Special Issue "Chemoinformatics and Drug Design"

A special issue of Pharmaceuticals (ISSN 1424-8247).

Deadline for manuscript submissions: 30 November 2018

Special Issue Editor

Guest Editor
Prof. Dr. Osvaldo A. Santos-Filho

Universidade Federal do Rio de Janeiro, IPPN, CCS, Bloco H-Ilha do Fundão, Rio de Janeiro, RJ-21941-902, Brazil
Website | E-Mail
Interests: molecular modeling; computer-aided drug design; bioinformatics; chemoinformatics

Special Issue Information

Dear Colleagues,

Chemoinformatics is the branch of chemical sciences regarding the design, creation, organization, retrieval, analysis, management, and dissemination of chemical information. In this context, one could say that chemoinformatics is the application of information science methods to solve chemical issues. Due to the development of hardware and software technologies, chemoinformatics is today a mature scientific field. Moreover, it is well known that, during the drug design process, a huge amount of chemical data is generated and, consequently, chemoinformatics have been successfully applied by the pharmaceutical community. To celebrate the success story and the advances on this important field, the journal Pharmaceuticals invites fellow scientists to submit original papers or reviews, which will be published as a Special Issue on “Chemoinformatics and Drug Design”.

Looking forward to your contribution.

Prof. Dr. Osvaldo Andrade Santos-Filho
Guest Editor

Manuscript Submission Information

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

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Pharmaceuticals is an international peer-reviewed open access quarterly 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 850 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

  • Chemoinformatics
  • Drug design
  • Machine learning
  • Data mining
  • Pharmacophore-based virtual screening
  • In silico databases
  • Molecular descriptors
  • Combinatorial chemistry
  • QSAR
  • ADMET

Published Papers (6 papers)

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Research

Open AccessArticle An Antioxidant Potential, Quantum-Chemical and Molecular Docking Study of the Major Chemical Constituents Present in the Leaves of Curatella americana Linn
Pharmaceuticals 2018, 11(3), 72; https://doi.org/10.3390/ph11030072
Received: 6 May 2018 / Revised: 9 July 2018 / Accepted: 12 July 2018 / Published: 20 July 2018
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Abstract
Reactive oxygen species (ROS) are continuously generated in the normal biological systems, primarily by enzymes as xanthine oxidase (XO). The inappropriate scavenging or inhibition of ROS has been considered to be linked with aging, inflammatory disorders, and chronic diseases. Therefore, many plants and
[...] Read more.
Reactive oxygen species (ROS) are continuously generated in the normal biological systems, primarily by enzymes as xanthine oxidase (XO). The inappropriate scavenging or inhibition of ROS has been considered to be linked with aging, inflammatory disorders, and chronic diseases. Therefore, many plants and their products have been investigated as natural antioxidants for their potential use in preventive medicine. The leaves and bark extracts of Curatella americana Linn. were described in scientific research as anti-inflammatory, vasodilator, anti-ulcerogenic, and hypolipidemic effects. So, the aim of this study was to evaluate the antioxidant potentials of leaf hydroalcoholic extract from C. americana (HECA) through the scavenging DPPH assay and their main chemical constituents, evaluated by the following quantum chemical approaches (DFT B3LYP/6-31G**): Maps of Molecular Electrostatic Potential (MEP), Frontier Orbital’s (HOMO and LUMO) followed by multivariate analysis and molecular docking simulations with the xanthine oxidase enzyme. The hydroalcoholic extract showed significant antioxidant activity by free radical scavenging probably due to the great presence of flavonoids, which were grouped in the PCA and HCA analysis with the standard gallic acid. In the molecular docking study, the compounds studied presented the binding free energy (ΔG) values close each other, due to the similar interactions with amino acids residues at the activity site. The descriptors Gap and softness were important to characterize the molecules with antioxidant potential by capturing oxygen radicals. Full article
(This article belongs to the Special Issue Chemoinformatics and Drug Design)
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Open AccessArticle Heterodimer Binding Scaffolds Recognition via the Analysis of Kinetically Hot Residues
Pharmaceuticals 2018, 11(1), 29; https://doi.org/10.3390/ph11010029
Received: 27 December 2017 / Revised: 6 March 2018 / Accepted: 8 March 2018 / Published: 16 March 2018
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Abstract
Physical interactions between proteins are often difficult to decipher. The aim of this paper is to present an algorithm that is designed to recognize binding patches and supporting structural scaffolds of interacting heterodimer proteins using the Gaussian Network Model (GNM). The recognition is
[...] Read more.
Physical interactions between proteins are often difficult to decipher. The aim of this paper is to present an algorithm that is designed to recognize binding patches and supporting structural scaffolds of interacting heterodimer proteins using the Gaussian Network Model (GNM). The recognition is based on the (self) adjustable identification of kinetically hot residues and their connection to possible binding scaffolds. The kinetically hot residues are residues with the lowest entropy, i.e., the highest contribution to the weighted sum of the fastest modes per chain extracted via GNM. The algorithm adjusts the number of fast modes in the GNM’s weighted sum calculation using the ratio of predicted and expected numbers of target residues (contact and the neighboring first-layer residues). This approach produces very good results when applied to dimers with high protein sequence length ratios. The protocol’s ability to recognize near native decoys was compared to the ability of the residue-level statistical potential of Lu and Skolnick using the Sternberg and Vakser decoy dimers sets. The statistical potential produced better overall results, but in a number of cases its predicting ability was comparable, or even inferior, to the prediction ability of the adjustable GNM approach. The results presented in this paper suggest that in heterodimers at least one protein has interacting scaffold determined by the immovable, kinetically hot residues. In many cases, interacting proteins (especially if being of noticeably different sizes) either behave as a rigid lock and key or, presumably, exhibit the opposite dynamic behavior. While the binding surface of one protein is rigid and stable, its partner’s interacting scaffold is more flexible and adaptable. Full article
(This article belongs to the Special Issue Chemoinformatics and Drug Design)
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Open AccessArticle In Silico Study, Synthesis, and Cytotoxic Activities of Porphyrin Derivatives
Pharmaceuticals 2018, 11(1), 8; https://doi.org/10.3390/ph11010008
Received: 28 December 2017 / Revised: 14 January 2018 / Accepted: 19 January 2018 / Published: 20 January 2018
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Abstract
Five known porphyrins, 5,10,15,20-tetrakis(p-tolyl)porphyrin (TTP), 5,10,15,20-tetrakis(p-bromophenyl)porphyrin (TBrPP), 5,10,15,20-tetrakis(p-aminophenyl)porphyrin (TAPP), 5,10,15-tris(tolyl)-20-mono(p-nitrophenyl)porphyrin (TrTMNP), 5,10,15-tris(tolyl)-20-mono(p-aminophenyl)porphyrin (TrTMAP), and three novel porphyrin derivatives, 5,15-di-[bis(3,4-ethylcarboxymethylenoxy)phenyl]-10,20-di(p-tolyl)porphyrin
[...] Read more.
Five known porphyrins, 5,10,15,20-tetrakis(p-tolyl)porphyrin (TTP), 5,10,15,20-tetrakis(p-bromophenyl)porphyrin (TBrPP), 5,10,15,20-tetrakis(p-aminophenyl)porphyrin (TAPP), 5,10,15-tris(tolyl)-20-mono(p-nitrophenyl)porphyrin (TrTMNP), 5,10,15-tris(tolyl)-20-mono(p-aminophenyl)porphyrin (TrTMAP), and three novel porphyrin derivatives, 5,15-di-[bis(3,4-ethylcarboxymethylenoxy)phenyl]-10,20-di(p-tolyl)porphyrin (DBECPDTP), 5,10-di-[bis(3,4-ethylcarboxymethylenoxy)phenyl]-15,20-di-(methylpyrazole-4-yl)porphyrin (cDBECPDPzP), 5,15-di-[bis(3,4-ethylcarboxymethylenoxy)phenyl]-10,20-di-(methylpyrazole-4-yl)porphyrin (DBECPDPzP), were used to study their interaction with protein targets (in silico study), and were synthesized. Their cytotoxic activities against cancer cell lines were tested using 3-(4,5-dimetiltiazol-2-il)-2,5-difeniltetrazolium bromide (MTT) assay. The interaction of porphyrin derivatives with carbonic anhydrase IX (CAIX) and REV-ERBβ proteins were studied by molecular docking and molecular dynamic simulation. In silico study results reveal that DBECPDPzP and TrTMNP showed the highest binding interaction with REV- ERBβ and CAIX, respectively, and both complexes of DBECPDPzP-REV-ERBβ and TrTMNP-CAIX showed good and comparable stability during molecular dynamic simulation. The studied porphyrins have selective growth inhibition activities against tested cancer cells and are categorized as marginally active compounds based on their IC50. Full article
(This article belongs to the Special Issue Chemoinformatics and Drug Design)
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Open AccessArticle Propagation of Fibrillar Structural Forms in Proteins Stopped by Naturally Occurring Short Polypeptide Chain Fragments
Pharmaceuticals 2017, 10(4), 89; https://doi.org/10.3390/ph10040089
Received: 22 September 2017 / Revised: 2 November 2017 / Accepted: 13 November 2017 / Published: 16 November 2017
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Abstract
Amyloids characterized by unbounded growth of fibrillar structures cause many pathological processes. Such unbounded propagation is due to the presence of a propagating hydrophobicity field around the fibril’s main axis, preventing its closure (unlike in globular proteins). Interestingly, similar fragments, commonly referred to
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Amyloids characterized by unbounded growth of fibrillar structures cause many pathological processes. Such unbounded propagation is due to the presence of a propagating hydrophobicity field around the fibril’s main axis, preventing its closure (unlike in globular proteins). Interestingly, similar fragments, commonly referred to as solenoids, are present in many naturally occurring proteins, where their propagation is arrested by suitably located “stopper” fragments. In this work, we analyze the distribution of hydrophobicity in solenoids and in their corresponding “stoppers” from the point of view of the fuzzy oil drop model (called FOD in this paper). This model characterizes the unique linear propagation of local hydrophobicity in the solenoid fragment and allows us to pinpoint “stopper” sequences, where local hydrophobicity quite closely resembles conditions encountered in globular proteins. Consequently, such fragments perform their function by mediating entropically advantageous contact with the water environment. We discuss examples of amyloid-like structures in solenoids, with particular attention to “stop” segments present in properly folded proteins found in living organisms. Full article
(This article belongs to the Special Issue Chemoinformatics and Drug Design)
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Open AccessArticle Molecular Docking and 3D-Pharmacophore Modeling to Study the Interactions of Chalcone Derivatives with Estrogen Receptor Alpha
Pharmaceuticals 2017, 10(4), 81; https://doi.org/10.3390/ph10040081
Received: 21 August 2017 / Revised: 9 October 2017 / Accepted: 15 October 2017 / Published: 16 October 2017
Cited by 2 | PDF Full-text (6845 KB) | HTML Full-text | XML Full-text
Abstract
Tamoxifen is the most frequently used anti-estrogen adjuvant treatment for estrogen receptor-positive breast cancer. However, it is associated with an increased risk of several serious side–effects, such as uterine cancer, stroke, and pulmonary embolism. The 2′,4′-dihydroxy-6-methoxy-3,5-dimethylchalcone (ChalcEA) from plant leaves of Eugenia aquea
[...] Read more.
Tamoxifen is the most frequently used anti-estrogen adjuvant treatment for estrogen receptor-positive breast cancer. However, it is associated with an increased risk of several serious side–effects, such as uterine cancer, stroke, and pulmonary embolism. The 2′,4′-dihydroxy-6-methoxy-3,5-dimethylchalcone (ChalcEA) from plant leaves of Eugenia aquea, has been found to inhibit the proliferation of MCF-7 human breast cancer cells in a dose-dependent manner, with an IC50 of 74.5 μg/mL (250 μM). The aim of this work was to study the molecular interactions of new ChalcEA derivatives formed with the Estrogen Receptor α (ERα) using computer aided drug design approaches. Molecular docking using Autodock 4.2 was employed to explore the modes of binding of ChalcEA derivatives with ERα. The 3D structure-based pharmacophore model was derived using LigandScout 4.1 Advanced to investigate the important chemical interactions of the ERα-tamoxifen complex structure. The binding energy and the tamoxifen-pharmacophore fit score of the best ChalcEA derivative (HNS10) were −12.33 kcal/mol and 67.07 kcal/mol, respectively. The HNS10 interacted with Leu346, Thr347, Leu349, Ala350, Glu353, Leu387, Met388, Leu391, Arg394, Met421, and Leu525. These results suggest that the new ChalcEA derivatives could serve as the lead compound for potent ERα inhibitor in the fight against breast cancer. Full article
(This article belongs to the Special Issue Chemoinformatics and Drug Design)
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Open AccessArticle Anti-Mycobacterial Evaluation of 7-Chloro-4-Aminoquinolines and Hologram Quantitative Structure–Activity Relationship (HQSAR) Modeling of Amino–Imino Tautomers
Pharmaceuticals 2017, 10(2), 52; https://doi.org/10.3390/ph10020052
Received: 8 May 2017 / Revised: 5 June 2017 / Accepted: 6 June 2017 / Published: 9 June 2017
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Abstract
In an ongoing research program for the development of new anti-tuberculosis drugs, we synthesized three series (A, B, and C) of 7-chloro-4-aminoquinolines, which were evaluated in vitro against Mycobacterium tuberculosis (MTB). Now, we report the anti-MTB and cytotoxicity evaluations
[...] Read more.
In an ongoing research program for the development of new anti-tuberculosis drugs, we synthesized three series (A, B, and C) of 7-chloro-4-aminoquinolines, which were evaluated in vitro against Mycobacterium tuberculosis (MTB). Now, we report the anti-MTB and cytotoxicity evaluations of a new series, D (D01D21). Considering the active compounds of series A (A01A13), B (B01B13), C (C01C07), and D (D01D09), we compose a data set of 42 compounds and carried out hologram quantitative structure–activity relationship (HQSAR) analysis. The amino–imino tautomerism of the 4-aminoquinoline moiety was considered using both amino (I) and imino (II) forms as independent datasets. The best HQSAR model from each dataset was internally validated and both models showed significant statistical indexes. Tautomer I model: leave-one-out (LOO) cross-validated correlation coefficient (q2) = 0.80, squared correlation coefficient (r2) = 0.97, standard error (SE) = 0.12, cross-validated standard error (SEcv) = 0.32. Tautomer II model: q2 = 0.77, r2 = 0.98, SE = 0.10, SEcv = 0.35. Both models were externally validated by predicting the activity values of the corresponding test set, and the tautomer II model, which showed the best external prediction performance, was used to predict the biological activity responses of the compounds that were not evaluated in the anti-MTB trials due to poor solubility, pointing out D21 for further solubility studies to attempt to determine its actual biological activity. Full article
(This article belongs to the Special Issue Chemoinformatics and Drug Design)
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