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Complex Networks, Bio-Molecular Systems, and Machine Learning

This special issue belongs to the section “Molecular Informatics“.

Special Issue Information

Dear Colleague,

Both, Artificial Intelligence and/or Machine Learning (AI/ML) and Complex Networks algorithms are important tools for the computational study of molecular systems. Some of these methods are Artificial Neural Networks (ANN), Deep Learning Networks, Support Vector Machines (SVM), Random Forests (RF), Genetic Algorithms (GA), Deep Neural Networks (DNN), Deep Belief Networks (DBN), Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN), etc. We can use structural parameters, molecular descriptors, experimental conditions, chemometrics measurements, etc. as input to train these AI/ML algorithms. As a reult we can obtain predictive models for Drug Discovery, Vaccine Design, Nanotechnoloy, etc.

On the other side, Complex networks are very useful for the study of complex bio-molecular systems. In fact, we can use complex networks to represent complex structural-function patterns in complex molecular bio-systems. This includes, but is not limited to, the structure chemical compounds, synthetic chemical reactions routes, proteins, polymers, viral structure, RNA secondary structure, etc. The method is highly flexible, so we can also represent larger bio-systems such as: metabolic pathways, protein interaction networks (PINs), gene regulatory networks, brain cortex, ecosystems, internet, market, social networks, etc. In this approach, it is common to represent the parts of the system (atoms, aminoacids, monomers, proteins, reactions, neurons, organisms, etc.) as nodes and the structure-function relationships among them (chemical bonds, hydrogen bonds, reactions, activation, co-expression, etc.) as edges or links. This opens a gate to the study of complex bio-molecular systems with graph and complex networks theory. In consequence, we can calculate multiple graph invariants (numeric parameters) useful to quantify the complex structure of these systems. It includes software/algorithms for the representation, study of distributions, emergent properties, transport phenomena, multiplex networks, dynamic systems properties, etc. In addition, though not mandatory, we can also train AI/ML algorithms using as input the numerical parameters of complex networks and bio-molecular systems in order to predict the structure-function relationships and in consequence the properties of these systems.

This framework opens the door to the development of new methods, algorithms, databases, and software for the study of complex bio-molecular systems using AI/ML and/or Complex Networks algorithms. Consequently, the topic of the issue is: “Complex Networks, Bio-Molecular Systems, and Machine Learning.” Authors are welcome to submit papers using AI/ML algorithms alone and we aslo welcome papers using Complex Network algorithms only. All in all, we especially welcome papers combining both AI/ML and Complex Networks areas. Accepted papers will be published in the International Journal of Molecular Sciences (IJMS), which is an open access journal published by MDPI (https://www.mdpi.com/journal/ijms). The authors of the papers can opt also to publish online short communications or posters about their papers in the MOL2NET International Conference Series on Multidisciplinary Sciences, 2020. The conference has multiple workshops/sessions in universities of USA, Europe, China, India, Brazil, etc. The conference is published at the Sciforum platform, supported by MDPI editorial. MOL2NET 2020 link: https://mol2net-06.sciforum.net/.

Prof. Dr. Humberto González-Díaz
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 250 words) can be sent to the Editorial Office for assessment.

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

Complex Networks
Bio-molecular systems
Protein Interaction Networks (PIN)
Metabolic Pathway networks
Brain Networks
Social, Financial, and Legal Networks
Machine Learning
Bioinformatics
Cheminformatics and Drug Discovery
Graph theory
Artificial Neural Networks
Support Vector Machines
Deep Learning

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Int. J. Mol. Sci. - ISSN 1422-0067

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