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Molecular Big Data, Computing, and Atomic-Level Simulation for Drug Discovery and Biology

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A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 22396

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Special Issue Editor

Prof. Mi-hyun Kim

E-Mail Website
Guest Editor

Gachon University, Incheon, Korea
Interests: Medicinal Chemistry and Informatics

Special Issue Information

Dear Colleagues,

Current development of machine learning algorithms and data technology make drug discovery and biology gain new challenges and opportunities to solve a scientific big problem. In particular, diverse unstructured data (e.g., real world data) as well as public databases are considerable to improve the applicability domain and predictive power in molecular simulations. In the same way we can find a shortcut to reach the summit of a mountain through searching uncertain different paths, a scientific big problem can be solved through investigating different methodologies of diverse research area (at a glance, unrelated). This issue covers recent diverse and novel approaches of atomic-level simulations using algorithms and data technologies. In this issue, unconventional or unexpected methods are recommended to suggest innovative prediction of atomic level molecular mechanics or biological activity. However, the prediction can also be conducted by current major methods (e.g., molecular dynamics simulations, docking simulations, QSAR, pharmacophore modeling) using well-encoded unique data.

Prof. Mi-hyun Kim
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. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. 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

In silico target deconvolution
Machine learning
Unstructured data
Molecular features
Molecular representation
Real world data
Heterogeneous data processing
Data encoding
Meta data
Meta prediction

Published Papers (4 papers)

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Research

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21 pages, 4659 KiB

Open AccessArticle

Comparing a Query Compound with Drug Target Classes Using 3D-Chemical Similarity

by Sang-Hyeok Lee, Sangjin Ahn and Mi-hyun Kim

Int. J. Mol. Sci. 2020, 21(12), 4208; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms21124208 - 12 Jun 2020

Cited by 5 | Viewed by 2516

Abstract

3D similarity is useful in predicting the profiles of unprecedented molecular frameworks that are 2D dissimilar to known compounds. When comparing pairs of compounds, 3D similarity of the pairs depends on conformational sampling, the alignment method, the chosen descriptors, and the similarity coefficients. In addition to these four factors, 3D chemocentric target prediction of an unknown compound requires compound–target associations, which replace compound-to-compound comparisons with compound-to-target comparisons. In this study, quantitative comparison of query compounds to target classes (one-to-group) was achieved via two types of 3D similarity distributions for the respective target class with parameter optimization for the fitting models: (1) maximum likelihood (ML) estimation of queries, and (2) the Gaussian mixture model (GMM) of target classes. While Jaccard–Tanimoto similarity of query-to-ligand pairs with 3D structures (sampled multi-conformers) can be transformed into query distribution using ML estimation, the ligand pair similarity within each target class can be transformed into a representative distribution of a target class through GMM, which is hyperparameterized via the expectation–maximization (EM) algorithm. To quantify the discriminativeness of a query ligand against target classes, the Kullback–Leibler (K–L) divergence of each query was calculated and compared between targets. 3D similarity-based K–L divergence together with the probability and the feasibility index, (F_m), showed discriminative power with regard to some query–class associations. The K–L divergence of 3D similarity distributions can be an additional method for (1) the rank of the 3D similarity score or (2) the p-value of one 3D similarity distribution to predict the target of unprecedented drug scaffolds. Full article

(This article belongs to the Special Issue Molecular Big Data, Computing, and Atomic-Level Simulation for Drug Discovery and Biology)

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35 pages, 11465 KiB

Open AccessArticle

Homology Modeling of the Human P-glycoprotein (ABCB1) and Insights into Ligand Binding through Molecular Docking Studies

by Liadys Mora Lagares, Nikola Minovski, Ana Yisel Caballero Alfonso, Emilio Benfenati, Sara Wellens, Maxime Culot, Fabien Gosselet and Marjana Novič

Int. J. Mol. Sci. 2020, 21(11), 4058; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms21114058 - 05 Jun 2020

Cited by 35 | Viewed by 6238

Abstract

The ABCB1 transporter also known as P-glycoprotein (P-gp) is a transmembrane protein belonging to the ATP binding cassette super-family of transporters; it is a xenobiotic efflux pump that limits intracellular drug accumulation by pumping the compounds out of cells. P-gp contributes to a decrease of toxicity and possesses broad substrate specificity. It is involved in the failure of numerous anticancer and antiviral chemotherapies due to the multidrug resistance (MDR) phenomenon, where it removes the chemotherapeutics out of the targeted cells. Understanding the details of the ligand–P-gp interaction is therefore crucial for the development of drugs that might overcome the MRD phenomenon and for obtaining a more effective prediction of the toxicity of certain compounds. In this work, an in silico modeling was performed using homology modeling and molecular docking methods with the aim of better understanding the ligand–P-gp interactions. Based on different mouse P-gp structural templates from the PDB repository, a 3D model of the human P-gp (hP-gp) was constructed by means of protein homology modeling. The homology model was then used to perform molecular docking calculations on a set of thirteen compounds, including some well-known compounds that interact with P-gp as substrates, inhibitors, or both. The sum of ranking differences (SRD) was employed for the comparison of the different scoring functions used in the docking calculations. A consensus-ranking scheme was employed for the selection of the top-ranked pose for each docked ligand. The docking results showed that a high number of π interactions, mainly π–sigma, π–alkyl, and π–π type of interactions, together with the simultaneous presence of hydrogen bond interactions contribute to the stability of the ligand–protein complex in the binding site. It was also observed that some interacting residues in hP-gp are the same when compared to those observed in a co-crystallized ligand (PBDE-100) with mouse P-gp (PDB ID: 4XWK). Our in silico approach is consistent with available experimental results regarding P-gp efflux transport assay; therefore it could be useful in the prediction of the role of new compounds in systemic toxicity. Full article

(This article belongs to the Special Issue Molecular Big Data, Computing, and Atomic-Level Simulation for Drug Discovery and Biology)

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Review

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33 pages, 22297 KiB

Open AccessReview

Drug Discovery of Spinal Muscular Atrophy (SMA) from the Computational Perspective: A Comprehensive Review

by Li Chuin Chong, Gayatri Gandhi, Jian Ming Lee, Wendy Wai Yeng Yeo and Sy-Bing Choi

Int. J. Mol. Sci. 2021, 22(16), 8962; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms22168962 - 20 Aug 2021

Cited by 7 | Viewed by 8593

Abstract

Spinal muscular atrophy (SMA), one of the leading inherited causes of child mortality, is a rare neuromuscular disease arising from loss-of-function mutations of the survival motor neuron 1 (SMN1) gene, which encodes the SMN protein. When lacking the SMN protein in neurons, patients suffer from muscle weakness and atrophy, and in the severe cases, respiratory failure and death. Several therapeutic approaches show promise with human testing and three medications have been approved by the U.S. Food and Drug Administration (FDA) to date. Despite the shown promise of these approved therapies, there are some crucial limitations, one of the most important being the cost. The FDA-approved drugs are high-priced and are shortlisted among the most expensive treatments in the world. The price is still far beyond affordable and may serve as a burden for patients. The blooming of the biomedical data and advancement of computational approaches have opened new possibilities for SMA therapeutic development. This article highlights the present status of computationally aided approaches, including in silico drug repurposing, network driven drug discovery as well as artificial intelligence (AI)-assisted drug discovery, and discusses the future prospects. Full article

(This article belongs to the Special Issue Molecular Big Data, Computing, and Atomic-Level Simulation for Drug Discovery and Biology)

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13 pages, 2360 KiB

Open AccessReview

Bioinformatics for Renal and Urinary Proteomics: Call for Aggrandization

by Piby Paul, Vimala Antonydhason, Judy Gopal, Steve W. Haga, Nazim Hasan and Jae-Wook Oh

Int. J. Mol. Sci. 2020, 21(3), 961; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms21030961 - 31 Jan 2020

Cited by 5 | Viewed by 4465

Abstract

The clinical sampling of urine is noninvasive and unrestricted, whereby huge volumes can be easily obtained. This makes urine a valuable resource for the diagnoses of diseases. Urinary and renal proteomics have resulted in considerable progress in kidney-based disease diagnosis through biomarker discovery and treatment. This review summarizes the bioinformatics tools available for this area of proteomics and the milestones reached using these tools in clinical research. The scant research publications and the even more limited bioinformatic tool options available for urinary and renal proteomics are highlighted in this review. The need for more attention and input from bioinformaticians is highlighted, so that progressive achievements and releases can be made. With just a handful of existing tools for renal and urinary proteomic research available, this review identifies a gap worth targeting by protein chemists and bioinformaticians. The probable causes for the lack of enthusiasm in this area are also speculated upon in this review. This is the first review that consolidates the bioinformatics applications specifically for renal and urinary proteomics. Full article

(This article belongs to the Special Issue Molecular Big Data, Computing, and Atomic-Level Simulation for Drug Discovery and Biology)

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