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Recent Advances in Computational Drug Discovery: From In Silico Screening to Multiscale De Novo Drug Design

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

Deadline for manuscript submissions: closed (31 January 2020) | Viewed by 46449

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Special Issue Editor

Prof. Dr. Alejandro Speck-Planche

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Guest Editor

Departamento de Ingeniería Química, Universidad San Francisco de Quito, Diego de Robles y vía Interoceánica, Quito 170901, Ecuador
Interests: multi-target drug discovery, chemoinformatics, QSAR-based approaches, virtual screening, multi-scale de novo drug design, machine learning.
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Special Issue Information

Dear Colleagues,

Diseases continue to plague modern societies, and over time, through the process known as drug discovery, a plethora of therapeutic options has been introduced to cure illnesses. Unfortunately, most of them remain as unresolved health issues. The experience accumulated demonstrates that the scientific community still faces several challenges in drug development. On one hand, it is well-established that the chemical space to be covered in the search for new drugs is vast, being formed by approximately 10⁶⁰ small molecules. On the other hand, diseases are difficult to treat because of their multifactorial nature, which in many cases is related to phenomena such as drug resistance. Further, most of the current drugs are associated with a broad spectrum of side effects. Consequently, designing a new drug is increasingly expensive, complex, and time-consuming, taking 12–17 years with a cost of around US$3 billion.

Today, to accelerate and improve drug discovery, there is a pressing need to exploit and integrate the huge amounts of data coming from the domains of the chemical, biological, and biomedical sciences. In this context, in silico approaches have become an integral part of all the drug discovery projects, helping to rationalize the design of potent and versatile therapeutic agents.

In this Special Issue of Molecules, we are inviting the scientific community to submit original research contributions, short communications, or review articles that highlight the most recent advances in the applications of in silico approaches to all the areas involved in drug discovery.

Prof. Alejandro Speck-Planche
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. 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

Virtual screening
De novo design
Chemoinformatics
Bioinformatics
QSAR
Big data and data mining
Machine learning
Network modeling
Multiscale models

Published Papers (7 papers)

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Research

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16 pages, 5456 KiB

Open AccessArticle

Identifying Novel ATX Inhibitors via Combinatory Virtual Screening Using Crystallography-Derived Pharmacophore Modelling, Docking Study, and QSAR Analysis

by Ji-Xia Ren, Rui-Tao Zhang and Hui Zhang

Molecules 2020, 25(5), 1107; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules25051107 - 02 Mar 2020

Cited by 5 | Viewed by 3876

Abstract

Autotaxin (ATX) is considered as an interesting drug target for the therapy of several diseases. The goal of the research was to detect new ATX inhibitors which have novel scaffolds by using virtual screening. First, based on two diverse receptor-ligand complexes, 14 pharmacophore models were developed, and the 14 models were verified through a big test database. Those pharmacophore models were utilized to accomplish virtual screening. Next, for the purpose of predicting the probable binding poses of compounds and then carrying out further virtual screening, docking-based virtual screening was performed. Moreover, an excellent 3D QSAR model was established, and 3D QSAR-based virtual screening was applied for predicting the activity values of compounds which got through the above two-round screenings. A correlation coefficient r², which equals 0.988, was supplied by the 3D QSAR model for the training set, and the correlation coefficient r² equaling 0.808 for the test set means that the developed 3D QSAR model is an excellent model. After the filtering was done by the combinatory virtual screening, which is based on the pharmacophore modelling, docking study, and 3D QSAR modelling, we chose nine potent inhibitors with novel scaffolds finally. Furthermore, two potent compounds have been particularly discussed. Full article

(This article belongs to the Special Issue Recent Advances in Computational Drug Discovery: From In Silico Screening to Multiscale De Novo Drug Design)

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20 pages, 5113 KiB

Open AccessArticle

Virtual Screening Approach to Identify High-Affinity Inhibitors of Serum and Glucocorticoid-Regulated Kinase 1 among Bioactive Natural Products: Combined Molecular Docking and Simulation Studies

by Taj Mohammad, Shiza Siddiqui, Anas Shamsi, Mohamed F. Alajmi, Afzal Hussain, Asimul Islam, Faizan Ahmad and Md. Imtaiyaz Hassan

Molecules 2020, 25(4), 823; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules25040823 - 13 Feb 2020

Cited by 89 | Viewed by 5288

Abstract

Serum and glucocorticoid-regulated kinase 1 (SGK1) is a serine/threonine kinase that works under acute transcriptional control by several stimuli, including serum and glucocorticoids. It plays a significant role in the cancer progression and metastasis, as it regulates inflammation, apoptosis, hormone release, neuro-excitability, and cell proliferation. SGK1 has recently been considered as a potential drug target for cancer, diabetes, and neurodegenerative diseases. In the present study, we have performed structure-based virtual high-throughput screening of natural compounds from the ZINC database to find potential inhibitors of SGK1. Initially, hits were selected based on their physicochemical, absorption, distribution, metabolism, excretion, and toxicity (ADMET), and other drug-like properties. Afterwards, PAINS filter, binding affinities estimation, and interaction analysis were performed to find safe and effective hits. We found four compounds bearing appreciable binding affinity and specificity towards the binding pocket of SGK1. The docking results were complemented by all-atom molecular dynamics simulation for 100 ns, followed by MM/PBSA, and principal component analysis to investigate the conformational changes, stability, and interaction mechanism of SGK1 in-complex with the selected compound ZINC00319000. Molecular dynamics simulation results suggested that the binding of ZINC00319000 stabilizes the SGK1 structure, and it leads to fewer conformational changes. In conclusion, the identified compound ZINC00319000 might be further exploited as a scaffold to develop promising inhibitors of SGK1 for the therapeutic management of associated diseases, including cancer. Full article

(This article belongs to the Special Issue Recent Advances in Computational Drug Discovery: From In Silico Screening to Multiscale De Novo Drug Design)

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22 pages, 11087 KiB

Open AccessArticle

Profiling the Structural Determinants of Aryl Benzamide Derivatives as Negative Allosteric Modulators of mGluR5 by In Silico Study

by Yujing Zhao, Jiabin Chen, Qilei Liu and Yan Li

Molecules 2020, 25(2), 406; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules25020406 - 18 Jan 2020

Cited by 8 | Viewed by 2853

Abstract

Glutamate plays a crucial role in the treatment of depression by interacting with the metabotropic glutamate receptor subtype 5 (mGluR5), whose negative allosteric modulators (NAMs) are thus promising antidepressants. At present, to explore the structural features of 106 newly synthesized aryl benzamide series molecules as mGluR5 NAMs, a set of ligand-based three-dimensional quantitative structure-activity relationship (3D-QSAR) analyses were firstly carried out applying comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods. In addition, receptor-based analysis, namely molecular docking and molecular dynamics (MD) simulations, were performed to further elucidate the binding modes of mGluR5 NAMs. As a result, the optimal CoMSIA model obtained shows that cross-validated correlation coefficient Q² = 0.70, non-cross-validated correlation coefficient R²_ncv = 0.89, predicted correlation coefficient R²_pre = 0.87. Moreover, we found that aryl benzamide series molecules bind as mGluR5 NAMs at Site 1, which consists of amino acids Pro655, Tyr659, Ile625, Ile651, Ile944, Ser658, Ser654, Ser969, Ser965, Ala970, Ala973, Trp945, Phe948, Pro903, Asn907, Val966, Leu904, and Met962. This site is the same as that of other types of NAMs; mGluR5 NAMs are stabilized in the “linear” and “arc” configurations mainly through the H-bonds interactions, π–π stacking interaction with Trp945, and hydrophobic contacts. We hope that the models and information obtained will help understand the interaction mechanism of NAMs and design and optimize NAMs as new types of antidepressants. Full article

(This article belongs to the Special Issue Recent Advances in Computational Drug Discovery: From In Silico Screening to Multiscale De Novo Drug Design)

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20 pages, 971 KiB

Open AccessArticle

Construction of Quantitative Structure Activity Relationship (QSAR) Models to Predict Potency of Structurally Diversed Janus Kinase 2 Inhibitors

by Saw Simeon and Nathjanan Jongkon

Molecules 2019, 24(23), 4393; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules24234393 - 01 Dec 2019

Cited by 11 | Viewed by 5206

Abstract

Janus kinase 2 (JAK2) inhibitors represent a promising therapeutic class of anticancer agents against many myeloproliferative disorders. Bioactivity data on pIC

_{50}

of 2229 JAK2 inhibitors were employed in the construction of quantitative structure-activity relationship (QSAR) models. The models were built from 100 [...] Read more.

Janus kinase 2 (JAK2) inhibitors represent a promising therapeutic class of anticancer agents against many myeloproliferative disorders. Bioactivity data on pIC

_{50}

of 2229 JAK2 inhibitors were employed in the construction of quantitative structure-activity relationship (QSAR) models. The models were built from 100 data splits using decision tree (DT), support vector machine (SVM), deep neural network (DNN) and random forest (RF). The predictive power of RF models were assessed via 10-fold cross validation, which afforded excellent predictive performance with

R^{2}

and RMSE of 0.74 ± 0.05 and 0.63 ± 0.05, respectively. Moreover, test set has excellent performance of

R^{2}

(0.75 ± 0.03) and RMSE (0.62 ± 0.04). In addition, Y-scrambling was utilized to evaluate the possibility of chance correlation of the predictive model. A thorough analysis of the substructure fingerprint count was conducted to provide insights on the inhibitory properties of JAK2 inhibitors. Molecular cluster analysis revealed that pyrazine scaffolds have nanomolar potency against JAK2. Full article

(This article belongs to the Special Issue Recent Advances in Computational Drug Discovery: From In Silico Screening to Multiscale De Novo Drug Design)

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Review

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17 pages, 264 KiB

Open AccessEditor’s ChoiceReview

A Review on Applications of Computational Methods in Drug Screening and Design

by Xiaoqian Lin, Xiu Li and Xubo Lin

Molecules 2020, 25(6), 1375; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules25061375 - 18 Mar 2020

Cited by 304 | Viewed by 19108

Abstract

Drug development is one of the most significant processes in the pharmaceutical industry. Various computational methods have dramatically reduced the time and cost of drug discovery. In this review, we firstly discussed roles of multiscale biomolecular simulations in identifying drug binding sites on the target macromolecule and elucidating drug action mechanisms. Then, virtual screening methods (e.g., molecular docking, pharmacophore modeling, and QSAR) as well as structure- and ligand-based classical/de novo drug design were introduced and discussed. Last, we explored the development of machine learning methods and their applications in aforementioned computational methods to speed up the drug discovery process. Also, several application examples of combining various methods was discussed. A combination of different methods to jointly solve the tough problem at different scales and dimensions will be an inevitable trend in drug screening and design. Full article

(This article belongs to the Special Issue Recent Advances in Computational Drug Discovery: From In Silico Screening to Multiscale De Novo Drug Design)

16 pages, 483 KiB

Open AccessReview

QSPR/QSAR: State-of-Art, Weirdness, the Future

by Andrey A. Toropov and Alla P. Toropova

Molecules 2020, 25(6), 1292; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules25061292 - 12 Mar 2020

Cited by 43 | Viewed by 5709

Abstract

Ability of quantitative structure–property/activity relationships (QSPRs/QSARs) to serve for epistemological processes in natural sciences is discussed. Some weirdness of QSPR/QSAR state-of-art is listed. There are some contradictions in the research results in this area. Sometimes, these should be classified as paradoxes or weirdness. These points are often ignored. Here, these are listed and briefly commented. In addition, hypotheses on the future evolution of the QSPR/QSAR theory and practice are suggested. In particular, the possibility of extending of the QSPR/QSAR problematic by searching for the “statistical similarity” of different endpoints is suggested and illustrated by an example for relatively “distanced each from other” endpoints, namely (i) mutagenicity, (ii) anticancer activity, and (iii) blood–brain barrier. Full article

(This article belongs to the Special Issue Recent Advances in Computational Drug Discovery: From In Silico Screening to Multiscale De Novo Drug Design)

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10 pages, 2174 KiB

Open AccessReview

Recent Advances in the Discovery of CK2 Allosteric Inhibitors: From Traditional Screening to Structure-Based Design

by Xiaolan Chen, Chunqiong Li, Dada Wang, Yu Chen and Na Zhang

Molecules 2020, 25(4), 870; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules25040870 - 16 Feb 2020

Cited by 13 | Viewed by 3061

Abstract

Protein kinase (CK2) has emerged as an attractive cancer therapeutic target and recent efforts have been made to develop its inhibitors. However, the development of selective inhibitors remains challenging because of the highly conserved ATP-binding pocket (orthosteric site) of kinase family. As an alternative strategy, allosteric inhibitors, by targeting the much more diversified allosteric site relative to the conserved ATP-binding site, achieve better pharmacological advantages than orthosteric inhibitors. Traditional serendipitous screening and structure-based design are robust tools for the discovery of CK2 allosteric inhibitors. In this review, we summarize the recent advances in the identification of CK2 allosteric inhibitors. Firstly, we briefly present the CK2 allosteric sites. Then, the allosteric inhibitors targeting the well-elucidated allosteric sites (α/β interface, αD pocket and interface between the Glycine-rich loop and αC-helix) are highlighted in the discovery process and possible binding modes with the allosteric sites are described. This study is expected to provide valuable clues for the design of CK2 allosteric inhibitors. Full article

(This article belongs to the Special Issue Recent Advances in Computational Drug Discovery: From In Silico Screening to Multiscale De Novo Drug Design)

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