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

Deadline for manuscript submissions: closed (30 November 2018) | Viewed by 53276

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Prof. Dr. Osvaldo Andrade Santos-Filho

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Center of Health Sciences, Laboratory of Molecular Modeling and Computational Structural Biology, Federal University of Rio de Janeiro, IPPN, Av. Carlos Chagas Filho 373, Bloco H, Rio de Janeiro 21941-599, Brazil
Interests: molecular modeling; computational and medicinal chemistry; molecular simulations; structural biology
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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 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. Pharmaceuticals is an international peer-reviewed open access monthly 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 2900 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

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Related Special Issues

Molecular Dynamics in Drug Design in Pharmaceuticals (3 articles)
Computational Drug Discovery and Development in the Era of Big Data in Pharmaceuticals (4 articles)

Published Papers (7 papers)

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Research

19 pages, 10502 KiB

Open AccessArticle

In Silico Study to Identify New Antituberculosis Molecules from Natural Sources by Hierarchical Virtual Screening and Molecular Dynamics Simulations

by Vinícius de S. Pinto, Janay S. C. Araújo, Rai C. Silva, Glauber V. da Costa, Jorddy N. Cruz, Moysés F. De A. Neto, Joaquín M. Campos, Cleydson B. R. Santos, Franco H. A. Leite and Manoelito C. S. Junior

Pharmaceuticals 2019, 12(1), 36; https://doi.org/10.3390/ph12010036 - 12 Mar 2019

Cited by 54 | Viewed by 7717

Abstract

Tuberculosis (TB) is an infection caused by Mycobacterium tuberculosis, responsible for 1.5 million documented deaths in 2016. The increase in reported cases of M. tuberculosis resistance to the main drugs show the need for the development of new and efficient drugs for better TB control. Based on these facts, this work aimed to use combined in silico techniques for the discovery of potential inhibitors to β-ketoacyl-ACP synthase (MtKasA). Initially compounds from natural sources present in the ZINC database were selected, then filters were sequentially applied by virtual screening, initially with pharmacophoric modeling, and later the selected compounds (based on QFIT scores) were submitted to the DOCK 6.5 program. After recategorization of the variables (QFIT score and GRID score), compounds ZINC35465970 and ZINC31170017 were selected. These compounds showed great hydrophobic contributions and for each established system 100 ns of molecular dynamics simulations were performed and the binding free energy was calculated. ZINC35465970 demonstrated a greater capacity for the KasA enzyme inhibition, with a ΔG_bind = −30.90 kcal/mol and ZINC31170017 presented a ΔG_bind = −27.49 kcal/mol. These data can be used in other studies that aim at the inhibition of the same biological targets through drugs with a dual action. Full article

(This article belongs to the Special Issue Chemoinformatics and Drug Design)

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20 pages, 2641 KiB

Open AccessArticle

An Antioxidant Potential, Quantum-Chemical and Molecular Docking Study of the Major Chemical Constituents Present in the Leaves of Curatella americana Linn

by Mayara Amoras Teles Fujishima, Nayara Dos Santos Raulino da Silva, Ryan Da Silva Ramos, Elenilze Figueiredo Batista Ferreira, Kelton Luís Belém dos Santos, Carlos Henrique Tomich de Paula da Silva, Jocivania Oliveira da Silva, Joaquín Maria Campos Rosa and Cleydson Breno Rodrigues dos Santos

Pharmaceuticals 2018, 11(3), 72; https://doi.org/10.3390/ph11030072 - 20 Jul 2018

Cited by 39 | Viewed by 6209

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 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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26 pages, 4180 KiB

Open AccessArticle

Heterodimer Binding Scaffolds Recognition via the Analysis of Kinetically Hot Residues

by Ognjen Perišić

Pharmaceuticals 2018, 11(1), 29; https://doi.org/10.3390/ph11010029 - 16 Mar 2018

Cited by 3 | Viewed by 5172

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 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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18 pages, 7327 KiB

Open AccessArticle

In Silico Study, Synthesis, and Cytotoxic Activities of Porphyrin Derivatives

by Fransiska Kurniawan, Youhei Miura, Rahmana Emran Kartasasmita, Naoki Yoshioka, Abdul Mutalib and Daryono Hadi Tjahjono

Pharmaceuticals 2018, 11(1), 8; https://doi.org/10.3390/ph11010008 - 20 Jan 2018

Cited by 15 | Viewed by 8638

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 (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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8432 KiB

Open AccessArticle

Propagation of Fibrillar Structural Forms in Proteins Stopped by Naturally Occurring Short Polypeptide Chain Fragments

by Irena Roterman, Mateusz Banach and Leszek Konieczny

Pharmaceuticals 2017, 10(4), 89; https://doi.org/10.3390/ph10040089 - 16 Nov 2017

Cited by 7 | Viewed by 4822

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 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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6845 KiB

Open AccessArticle

Molecular Docking and 3D-Pharmacophore Modeling to Study the Interactions of Chalcone Derivatives with Estrogen Receptor Alpha

by Muchtaridi Muchtaridi, Hasna Nur Syahidah, Anas Subarnas, Muhammad Yusuf, Sharon D. Bryant and Thierry Langer

Pharmaceuticals 2017, 10(4), 81; https://doi.org/10.3390/ph10040081 - 16 Oct 2017

Cited by 66 | Viewed by 13154

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, 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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16820 KiB

Open AccessArticle

Anti-Mycobacterial Evaluation of 7-Chloro-4-Aminoquinolines and Hologram Quantitative Structure–Activity Relationship (HQSAR) Modeling of Amino–Imino Tautomers

by Marcelle L. F. Bispo, Camilo H. S. Lima, Laura N. F. Cardoso, André L. P. Candéa, Flávio A. F. M. Bezerra, Maria C. S. Lourenço, Maria G. M. O. Henriques, Ricardo B. Alencastro, Carlos R. Kaiser, Marcus V. N. Souza and Magaly G. Albuquerque

Pharmaceuticals 2017, 10(2), 52; https://doi.org/10.3390/ph10020052 - 9 Jun 2017

Cited by 4 | Viewed by 5799

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 of a new series, D (D01–D21). Considering the active compounds of series A (A01–A13), B (B01–B13), C (C01–C07), and D (D01–D09), 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 (SE_cv) = 0.32. Tautomer II model: q² = 0.77, r² = 0.98, SE = 0.10, SE_cv = 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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