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

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: https://mol2net-05.sciforum.net/.

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

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