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Special Issue "Cheminformatics, Past, Present, and Future: From Chemistry to Nanotechnology and Complex Systems"

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

Deadline for manuscript submissions: 15 November 2020.

Special Issue Editors

Dr. Aliuska Duardo-Sanchez
Guest Editor
Researcher Chair in Law & The Human Genome Research Group, PANELFIT H2020 Project Manager & EDC Board Coordinator, Europe Commission. Lecturer Criminal Law, Faculty of Law, University of The Basque Country UPV/EHU, Leioa (Great Bilbao), Biscay, Basque Country, Spain
Interests: data protection; bioethics; criminal law; legal informatics; social network analysis
Special Issues and Collections in MDPI journals
Prof. Dr. Alejandro Pazos Sierra
Website1 Website2
Guest Editor
Chair and Director of Department of Computer Science and Information Technology, University of Coruña (UDC), Coruña, Spain
Interests: big data analysis; machine learning, artificial intelligence; bioinformatics; cheminformatics
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Cheminformatics techniques form have been interesting tools in the effort to explore large chemical spaces, reducing at the same time animal testing as well as costs in terms of materials and human resources on drug discovery processes in medicinal chemistry. In so doing, most cheminformatics methods combine techniques for the calculation of molecular descriptors (mainly chemical graph theory and quantum-mechanics/molecular-mechanics (QM/MM)) with data analysis techniques (regression, machine learning, cluster analysis, etc.). Most classic cheminformatics models focus on the study of the activity of a more or less homogeneous series of compounds over one specific molecular target (protein most of times) in an specific set of conditions of assay. Important efforts have also been made in the study of toxicity, cytotoxicity, eco-toxicity, and the ADME process. However, with the advent of high-throughput screening, combinatorial chemistry, high-throughput sequencing, and other technologies, we have witnessed the emergence of large collections of bioactivity data. These collections have been ordered in large databases like ChEMBL, DrugBank, PubChem, etc. In many cases, the data have Big Data or Quasi Big Data features that can be summarized more or less as 5V + C. This indicates that these new kinds of data present problems for processing due to limitations caused by data volume, veracity, variability, and value, along with problems due to processing velocity or high data complexity. In particular, data complexity emerges, in part, due to the interaction of drugs (forming drug-target networks) with multiple targets that are forming, at the same time, complex protein interaction networks (PINs), metabolic networks, gene regulatory networks, etc. All in all, in many cases, new cheminformatics is facing problems of data fusion due to the necessity of processing preclinical data for large series of chemical compounds with multiple targets, cell lines, assay organisms, etc. together with target sequence data from proteomics/genomics, image data from neurosciences (EECG, fNMRI, etc.), and other sources.

In addition, with the emergence of nanotechnologies, cheminformatics needs to deal with drug–nanoparticle systems used as drug delivery systems, drug co-therapy systems, etc. This brings to mind the application of cheminformatics in areas beyond drug discovery such as the fuel industry, polymer sciences, materials science, and biomedical engineering. All this leads to the use of more advanced Artificial Intelligence (AI) and deep learning techniques. Last but not the least, many of the previous aspects acquire more relevance if we consider interpersonal variations of the data following the appearance of personalized medicine. In consequence, cheminformatics also needs to process (integrate) data not only from preclinical assays (as in ChEMBL databases) but also from clinical assays or the use of drugs in the market with the cheminformatic study of emergence of drug side effects, drug activity in epidemiological networks, etc. (pharmaco-epidemiology). All in all, cheminformatics is also entering the age of regulatory cheminformatics, facing regulatory issues-related data protection, etc.; see, e.g., GDPR in Europe, REACH, OECD requisites.

In this context, we propose to open this new issue to discuss with all colleagues worldwide all the past, present, and future challenges of cheminformatics. The present Special Issue is also associated with MOL2NET-05, the International Conference on Multidisciplinary Sciences, ISSN: 2624-5078, MDPI SciForum, Basel, Switzerland, 2019. The conference has its HQs in University of Basque Country (UPV/EHU) and is supported by Professors of Ikerbasque: Basque Foundation for Sciences, Harvard Medicine School, UNC Chapel Hill, EMBL-EBI United Kingdom, CNAM Paris, Miami Dade College (MDC), University of Coruña (UDC), etc. The MOL2NET series is hosting more than 10 workshops with in-person and/or online participation every year in universities in the USA, Europe, Brazil, China, India, etc. In addition, the conference hosts the USEDAT: USA-Europe Data Analysis Training School, focused on training students worldwide in data analysis, with an emphasis in cheminformatics. The members of the committee have also guest edited other Special Issues in multiple MDPI journals. Please see the link of the conference:

We especially encourage submissions of papers from colleagues worldwide to the conference (short communications) and complete versions (full papers) to the present Special Issue.


Prof. Dr. Humbert González-Díaz
Dr. Aliuska Duardo-Sanchez
Prof. Dr. Alejandro Pazos Sierra
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at 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 papers will be 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 2000 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.


  • Classic Cheminformatics, QSAR, QSPR, QSTR Models
  • Cheminformatics, Quantum Chemistry, Chemical Reactivity, and Catalysis
  • Cheminformatics Multi-ouput, Multi-label, Classification Models
  • Machine Learning and Artificial Intelligence in Cheminformatics and Bioinformatics
  • Cheminformatics Natural Computing, Genetic, Ant Colony, Artificial Immune System, Algorithms
  • Quantum Computing Introduction and Applications in Cheminformatics and Bioinformatics
  • Cheminformatics Big Data Analysis, Deep Learning, Machine Learning, HPC & Cloud Computing Platforms
  • Cheminformatics and Docking Algorithms
  • Cheminformatics and Quantum/Molecular Mechanics (QM/MM) Algorithms
  • Cheminformatics, Information Fusion, Data Fusion
  • Webservers & Databases for Cheminformatics
  • Cheminformatics, Software Development
  • Cheminformatics Python, R, Matlab, Script Coding
  • Cheminformatics & Complex Gene, Protein, Metabolic Networks
  • Cheminformatics & Computational Nanotechnology, Polymers, and Materials Sciences
  • Cheminformatics & Biotechnology, Biomarkers Discovery, Vaccine Design, Biofuel Production
  • Cheminformatics & Biomedical Engineering, Mapping Drug Chemical Structure vs. HTS Genome Mapping, Proteome, EEG/ECG, Brain fNMR Imaging Signals, etc.
  • Cheminformatics & Chemomtrics in Inorganic, Polymers, Materials, and Analytical Chemistry
  • Cheminformatics Legal Issues, Software Copyright Protection, OECD/REACH Regulation, GDPR Personal Data Protection
  • Cheminformatics, Toxicology, and Environmental Sciences

Published Papers (2 papers)

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Open AccessArticle
A Refractive Index Study of a Diverse Set of Polymeric Materials by QSPR with Quantum-Chemical and Additive Descriptors
Molecules 2020, 25(17), 3772; - 19 Aug 2020
Predicting the activities and properties of materials via in silico methods has been shown to be a cost- and time-effective way of aiding chemists in synthesizing materials with desired properties. Refractive index (n) is one of the most important defining characteristics of an [...] Read more.
Predicting the activities and properties of materials via in silico methods has been shown to be a cost- and time-effective way of aiding chemists in synthesizing materials with desired properties. Refractive index (n) is one of the most important defining characteristics of an optical material. Presented in this work is a quantitative structure–property relationship (QSPR) model that was developed to predict the refractive index for a diverse set of polymers. A number of models were created, where a four-variable model showed the best predictive performance with R2 = 0.904 and Q2LOO = 0.897. The robustness and predictability of the best model was validated using the leave-one-out technique, external set and y-scrambling methods. The predictive ability of the model was confirmed with the external set, showing the R2ext = 0.880. For the refractive index, the ionization potential, polarizability, 2D and 3D geometrical descriptors were the most influential properties. The developed model was transparent and mechanistically explainable and can be used in the prediction of the refractive index for new and untested polymers. Full article
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Open AccessArticle
Chemometric Models of Differential Amino Acids at the Navα and Navβ Interface of Mammalian Sodium Channel Isoforms
Molecules 2020, 25(15), 3551; - 03 Aug 2020
(1) Background: voltage-gated sodium channels (Navs) are integral membrane proteins that allow the sodium ion flux into the excitable cells and initiate the action potential. They comprise an α (Navα) subunit that forms the channel pore and are coupled [...] Read more.
(1) Background: voltage-gated sodium channels (Navs) are integral membrane proteins that allow the sodium ion flux into the excitable cells and initiate the action potential. They comprise an α (Navα) subunit that forms the channel pore and are coupled to one or more auxiliary β (Navβ) subunits that modulate the gating to a variable extent. (2) Methods: after performing homology in silico modeling for all nine isoforms (Nav1.1α to Nav1.9α), the Navα and Navβ protein-protein interaction (PPI) was analyzed chemometrically based on the primary and secondary structures as well as topological or spatial mapping. (3) Results: our findings reveal a unique isoform-specific correspondence between certain segments of the extracellular loops of the Navα subunits. Precisely, loop S5 in domain I forms part of the PPI and assists Navβ1 or Navβ3 on all nine mammalian isoforms. The implied molecular movements resemble macroscopic springs, all of which explains published voltage sensor effects on sodium channel fast inactivation in gating. (4) Conclusions: currently, the specific functions exerted by the Navβ1 or Navβ3 subunits on the modulation of Navα gating remain unknown. Our work determined functional interaction in the extracellular domains on theoretical grounds and we propose a schematic model of the gating mechanism of fast channel sodium current inactivation by educated guessing. Full article
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