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Advances in Cheminformatics and Nanoinformatics: From Materials Modelling to Precision Toxicology, Medicine and Agriculture

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 (30 November 2021) | Viewed by 33813

Special Issue Editors

Dr. Georgia Melagraki

E-Mail Website
Guest Editor
Hellenic Military Academy, P.O. Box 16673 Vari, Greece
Interests: cheminformatics; nanoinformatics; machine learning; in silico drug discovery; in silico toxicology; risk assessment
Special Issues, Collections and Topics in MDPI journals
Dr. Antreas Afantitis

E-Mail Website
Guest Editor
NovaMechanics Ltd, Cheminformatics & Nanoinformatics Excellence, Digeni Akrita 51, 1070 Nicosia, Cyprus
Interests: cheminformatics; nanoinformatics; virtual screening; QSAR; computer aided drug design; computational toxicology; computational nanotoxicology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Iseult Lynch

grade E-Mail Website
Guest Editor
School of Geography, Earth and Environmental Sciences, University of Birmingham Edgbaston, Birmingham B15 2TT, UK
Interests: environmental interactions of nanoparticles and nanostructured surfaces; nanomaterials safety assessment; fate and sustainable future of plastics; environmental pollution
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will focus on extending and expanding the remit and application areas targeted by chemoinformatics and nanoinformatics, including applications in precision toxicology, personalized medicine, responsive and precision agriculture and, of course, to drug discovery and big data for COVID-19. We particularly take an interest in manuscripts that combine experimental and cheminformatics/nanoinformatics approaches toward delineating computational toxicology and nanosafety issues as well as in approaches that have been redeployed or developed to support the drug discovery process for COVID-19. Reviews that synthesize results and metanalyses are also welcome. Papers dealing with the development of computational methodologies and concepts are of great interest.

Potential topics include, but are not limited to, the following:

  • Nano-QSAR (nanomaterials modeling/QNAR)
  • Nanoinformatics with an emphasis on nanotoxicology and nanomedicine
  • (Nano-)material multiscale simulations
  • Deep learning/artificial intelligence models for chem-/nanoinformatics applications
  • The prediction of toxicity, metabolism, fate, and physicochemical properties of nanomaterials, nanomedicines, nanoagrichemicals, and viruses
  • Data mining for the identification of new leads with reduced toxicity
  • Big data in toxicology: integration, management, and analysis
  • QSAR/QSPR with an emphasis on toxicology
  • In silico lead identification and the optimization of toxicity
  • Web applications for computational toxicology problems (web services, apps, etc.)
  • Approaches for benchmarking of tools and expanding domains of applicability
  • Predictive toxicogenomics modeling using omics data
  • Biokinetics and PBPK modeling for nanomaterials
  • Integrating datasets and databases—best practice adapted for nanomaterials and nanosafety
  • Integrating models into safe-by-design or risk decision frameworks
  • Risk assessment and governance of nanomaterials
  • Software, tools, and applications for chem- and nanoinformatics

Dr. Georgia Melagraki
Dr. Antreas Afantitis
Prof. Dr. Iseult Lynch
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. 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.

Published Papers (5 papers)

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Research

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28 pages, 3374 KiB  
Article
In Silico Identification and Evaluation of Natural Products as Potential Tumor Necrosis Factor Function Inhibitors Using Advanced Enalos Asclepios KNIME Nodes
by Dimitra Papadopoulou, Antonios Drakopoulos, Panagiotis Lagarias, Georgia Melagraki, George Kollias and Antreas Afantitis
Int. J. Mol. Sci. 2021, 22(19), 10220; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms221910220 - 23 Sep 2021
Cited by 8 | Viewed by 3802
Abstract
Tumor necrosis factor (TNF) is a regulator of several chronic inflammatory diseases, such as rheumatoid arthritis. Although anti-TNF biologics have been used in clinic, they render several drawbacks, such as patients’ progressive immunodeficiency and loss of response, high cost, and intravenous administration. In [...] Read more.
Tumor necrosis factor (TNF) is a regulator of several chronic inflammatory diseases, such as rheumatoid arthritis. Although anti-TNF biologics have been used in clinic, they render several drawbacks, such as patients’ progressive immunodeficiency and loss of response, high cost, and intravenous administration. In order to find new potential anti-TNF small molecule inhibitors, we employed an in silico approach, aiming to find natural products, analogs of Ampelopsin H, a compound that blocks the formation of TNF active trimer. Two out of nine commercially available compounds tested, Nepalensinol B and Miyabenol A, efficiently reduced TNF-induced cytotoxicity in L929 cells and production of chemokines in mice joints’ synovial fibroblasts, while Nepalensinol B also abolished TNF-TNFR1 binding in non-toxic concentrations. The binding mode of the compounds was further investigated by molecular dynamics and free energy calculation studies, using and advancing the Enalos Asclepios pipeline. Conclusively, we propose that Nepalensinol B, characterized by the lowest free energy of binding and by a higher number of hydrogen bonds with TNF, qualifies as a potential lead compound for TNF inhibitors’ drug development. Finally, the upgraded Enalos Asclepios pipeline can be used for improved identification of new therapeutics against TNF-mediated chronic inflammatory diseases, providing state-of-the-art insight on their binding mode. Full article
(This article belongs to the Special Issue Advances in Cheminformatics and Nanoinformatics: From Materials Modelling to Precision Toxicology, Medicine and Agriculture)
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18 pages, 1584 KiB  
Article
Investigating the Molecular Processes behind the Cell-Specific Toxicity Response to Titanium Dioxide Nanobelts
by Laurent A. Winckers, Chris T. Evelo, Egon L. Willighagen and Martina Kutmon
Int. J. Mol. Sci. 2021, 22(17), 9432; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms22179432 - 30 Aug 2021
Cited by 1 | Viewed by 2346
Abstract
Some engineered nanomaterials incite toxicological effects, but the underlying molecular processes are understudied. The varied physicochemical properties cause different initial molecular interactions, complicating toxicological predictions. Gene expression data allow us to study the responses of genes and biological processes. Overrepresentation analysis identifies enriched [...] Read more.
Some engineered nanomaterials incite toxicological effects, but the underlying molecular processes are understudied. The varied physicochemical properties cause different initial molecular interactions, complicating toxicological predictions. Gene expression data allow us to study the responses of genes and biological processes. Overrepresentation analysis identifies enriched biological processes using the experimental data but prompts broad results instead of detailed toxicological processes. We demonstrate a targeted filtering approach to compare public gene expression data for low and high exposure on three cell lines to titanium dioxide nanobelts. Our workflow finds cell and concentration-specific changes in affected pathways linked to four Gene Ontology terms (apoptosis, inflammation, DNA damage, and oxidative stress) to select pathways with a clear toxicity focus. We saw more differentially expressed genes at higher exposure, but our analysis identifies clear differences between the cell lines in affected processes. Colorectal adenocarcinoma cells showed resilience to both concentrations. Small airway epithelial cells displayed a cytotoxic response to the high concentration, but not as strongly as monocytic-like cells. The pathway-gene networks highlighted the gene overlap between altered toxicity-related pathways. The automated workflow is flexible and can focus on other biological processes by selecting other GO terms. Full article
(This article belongs to the Special Issue Advances in Cheminformatics and Nanoinformatics: From Materials Modelling to Precision Toxicology, Medicine and Agriculture)
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Review

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20 pages, 4067 KiB  
Review
Importance of Surface Topography in Both Biological Activity and Catalysis of Nanomaterials: Can Catalysis by Design Guide Safe by Design?
by Mary Gulumian, Charlene Andraos, Antreas Afantitis, Tomasz Puzyn and Neil J. Coville
Int. J. Mol. Sci. 2021, 22(15), 8347; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms22158347 - 03 Aug 2021
Cited by 9 | Viewed by 3397
Abstract
It is acknowledged that the physicochemical properties of nanomaterials (NMs) have an impact on their toxicity and, eventually, their pathogenicity. These properties may include the NMs’ surface chemical composition, size, shape, surface charge, surface area, and surface coating with ligands (which can carry [...] Read more.
It is acknowledged that the physicochemical properties of nanomaterials (NMs) have an impact on their toxicity and, eventually, their pathogenicity. These properties may include the NMs’ surface chemical composition, size, shape, surface charge, surface area, and surface coating with ligands (which can carry different functional groups as well as proteins). Nanotopography, defined as the specific surface features at the nanoscopic scale, is not widely acknowledged as an important physicochemical property. It is known that the size and shape of NMs determine their nanotopography which, in turn, determines their surface area and their active sites. Nanotopography may also influence the extent of dissolution of NMs and their ability to adsorb atoms and molecules such as proteins. Consequently, the surface atoms (due to their nanotopography) can influence the orientation of proteins as well as their denaturation. However, although it is of great importance, the role of surface topography (nanotopography) in nanotoxicity is not much considered. Many of the issues that relate to nanotopography have much in common with the fundamental principles underlying classic catalysis. Although these were developed over many decades, there have been recent important and remarkable improvements in the development and study of catalysts. These have been brought about by new techniques that have allowed for study at the nanoscopic scale. Furthermore, the issue of quantum confinement by nanosized particles is now seen as an important issue in studying nanoparticles (NPs). In catalysis, the manipulation of a surface to create active surface sites that enhance interactions with external molecules and atoms has much in common with the interaction of NP surfaces with proteins, viruses, and bacteria with the same active surface sites of NMs. By reviewing the role that surface nanotopography plays in defining many of the NMs’ surface properties, it reveals the need for its consideration as an important physicochemical property in descriptive and predictive toxicology. Through the manipulation of surface topography, and by using principles developed in catalysis, it may also be possible to make safe-by-design NMs with a reduction of the surface properties which contribute to their toxicity. Full article
(This article belongs to the Special Issue Advances in Cheminformatics and Nanoinformatics: From Materials Modelling to Precision Toxicology, Medicine and Agriculture)
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19 pages, 522 KiB  
Review
Linear B-Cell Epitope Prediction for In Silico Vaccine Design: A Performance Review of Methods Available via Command-Line Interface
by Kosmas A. Galanis, Katerina C. Nastou, Nikos C. Papandreou, Georgios N. Petichakis, Diomidis G. Pigis and Vassiliki A. Iconomidou
Int. J. Mol. Sci. 2021, 22(6), 3210; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms22063210 - 22 Mar 2021
Cited by 54 | Viewed by 6660
Abstract
Linear B-cell epitope prediction research has received a steadily growing interest ever since the first method was developed in 1981. B-cell epitope identification with the help of an accurate prediction method can lead to an overall faster and cheaper vaccine design process, a [...] Read more.
Linear B-cell epitope prediction research has received a steadily growing interest ever since the first method was developed in 1981. B-cell epitope identification with the help of an accurate prediction method can lead to an overall faster and cheaper vaccine design process, a crucial necessity in the COVID-19 era. Consequently, several B-cell epitope prediction methods have been developed over the past few decades, but without significant success. In this study, we review the current performance and methodology of some of the most widely used linear B-cell epitope predictors which are available via a command-line interface, namely, BcePred, BepiPred, ABCpred, COBEpro, SVMTriP, LBtope, and LBEEP. Additionally, we attempted to remedy performance issues of the individual methods by developing a consensus classifier, which combines the separate predictions of these methods into a single output, accelerating the epitope-based vaccine design. While the method comparison was performed with some necessary caveats and individual methods might perform much better for specialized datasets, we hope that this update in performance can aid researchers towards the choice of a predictor, for the development of biomedical applications such as designed vaccines, diagnostic kits, immunotherapeutics, immunodiagnostic tests, antibody production, and disease diagnosis and therapy. Full article
(This article belongs to the Special Issue Advances in Cheminformatics and Nanoinformatics: From Materials Modelling to Precision Toxicology, Medicine and Agriculture)
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22 pages, 690 KiB  
Review
Advances in De Novo Drug Design: From Conventional to Machine Learning Methods
by Varnavas D. Mouchlis, Antreas Afantitis, Angela Serra, Michele Fratello, Anastasios G. Papadiamantis, Vassilis Aidinis, Iseult Lynch, Dario Greco and Georgia Melagraki
Int. J. Mol. Sci. 2021, 22(4), 1676; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms22041676 - 07 Feb 2021
Cited by 126 | Viewed by 16217
Abstract
De novo drug design is a computational approach that generates novel molecular structures from atomic building blocks with no a priori relationships. Conventional methods include structure-based and ligand-based design, which depend on the properties of the active site of a biological target or [...] Read more.
De novo drug design is a computational approach that generates novel molecular structures from atomic building blocks with no a priori relationships. Conventional methods include structure-based and ligand-based design, which depend on the properties of the active site of a biological target or its known active binders, respectively. Artificial intelligence, including ma-chine learning, is an emerging field that has positively impacted the drug discovery process. Deep reinforcement learning is a subdivision of machine learning that combines artificial neural networks with reinforcement-learning architectures. This method has successfully been em-ployed to develop novel de novo drug design approaches using a variety of artificial networks including recurrent neural networks, convolutional neural networks, generative adversarial networks, and autoencoders. This review article summarizes advances in de novo drug design, from conventional growth algorithms to advanced machine-learning methodologies and high-lights hot topics for further development. Full article
(This article belongs to the Special Issue Advances in Cheminformatics and Nanoinformatics: From Materials Modelling to Precision Toxicology, Medicine and Agriculture)
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