Chemoinformatics and Bioinformatics Tools in Structure-Activity Modelling in Molecular Sciences 2.0

Dear Colleagues,

At a time of universal digitization of data in various fields of research, including molecular sciences, there are more and more studies modeling continuous or classification endpoints (activities/properties) of molecules. In doing so, endpoints of molecules are most often classified (digitized) into two classes—active or inactive, and the classification is often carried out by grouping data into three or more classes.

Quantitative structure–activity/property relationships (QSAR/QSPR) are the most common, but not the only, forms of structure–endpoint models in molecular sciences. The accuracy of models is expressed by validation procedures, and many quality parameters are defined in the OECD document related to regulatory structure–activity models for the purpose of health and environmental protection [1]. In this document, the accuracy parameters of classification models are very sparsely presented. However, numerous accuracy parameters are used today, and those used for classification models are calculated from the confusion matrix elements [2]. There is also an increased need for a better definition of procedures for validation of regulatory structure–activity models in the OECD document [1]. Their application in environmental and health protection (toxicity, bioavailability, sorption, biodegradability, etc.) has been defined by EU REACH regulations [3].

The development of structure–activity modeling of different types of endpoints of molecules (usually various types of biological activities) is accelerated using chemoinformatics and bioinformatics tools, servers, algorithms, and databases developed for small molecules and proteins.

The research activities in the development of novel chemoinformatics and bioinformatics tools are particularly important topics for this Special Issue, such as the development of:

• Valuable databases, servers, and data mining tools;
• Drug or lead structure identification or dereplication approaches used in bioprospecting research;
• Structure optimization tools;
• Molecular descriptors;
• Modeling and variable selection algorithms;
• Computational model validation methods;
• Multivariate linear and nonlinear methods;
• Machine learning and deep learning algorithms;
• Predictive or descriptive structure–activity models;
• Different visualization tools;
• Protein–ligand (target/small compound) interactions;
• Protein-protein interactions;
• Molecular docking, etc.

All these topics are of the highest importance for structure–activity modeling in molecular sciences.

This Special Issue aims to collect relevant contributions (papers) belonging to one or more of the topics listed above (and those related to them), which are important for the acceleration of structure–activity research in molecular sciences. Applications aimed at modeling a broad spectrum of chemical, biological, pharmaceutical, biochemical, and environmentally relevant activities and properties of molecules are also appreciated.

All forms of scientific articles covering mentioned or related topics are welcomed, i.e., original papers, reviews, and communications. 

[1] Guidance Document on the Validation of (Quantitative) Structure-Activity Relationship [(Q)SAR] Models, [https://www.oecd.org/env/guidance-document-on-the-validation-of-quantitative-structure-activity-relationship-q-sar-models-9789264085442-en.htm]

[2] D. M. W. Powers, Evaluation: from precision, recall and f-measure to roc, informedness, markedness & correlation, J. Machine Learning Techn., 2011, 2, 37-63

[3] Regulation (EC) No 1907/2006: REACH - Registration, Evaluation, Authorisation and Restriction of Chemicals. [http://ec.europa.eu/enterprise/sectors/chemicals/reach/index_en.htm].

Dr. Bono Lučić
Guest Editor

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