QSAR and QSPR: Recent Developments and Applications, 3rd Edition

Dear Colleagues,

Since the introduction of Quantitative Structure-Activity Relationships (QSAR) almost 60 years ago when the biological activities of molecules (potential drugs) have been modelled, the application of this methodology has extended to modelling a number of physicochemical properties of molecules (QSPR). From the initial small sets of molecules having a limited number of molecular descriptors with clear chemical interpretation, and the simplest and mostly linear models that linked the structural characteristics of molecules with their biological activity - today in the field of QSAR/QSPR modelling sets of molecules and molecular descriptors are large, functional relationships between molecular descriptors and modelled activity/property are (generally) nonlinear and complex. The clear interpretation of today's QSAR/QSPR models is not an easy task due to the complex inter-relation between input descriptors and between the input descriptors and modelled activity/property. Also, numerous biological activities and physicochemical properties of molecules are modelled meaning that the QSAR/QSPR methodology is applied in various fields of chemistry, chemical engineering, drug design, biotechnological, environmental chemistry and toxicology, polymer chemistry as well as in bioscience and nanoscience. Continuous issues of improvement of QSAR/QSPR methodology is related to:

 (1) development of mathematical and informational tools in encoding structural information at the constitutional, 2D, topological, as well as 3D semi-empirical or DFT level of theory, development of molecular descriptor theory, information content in molecular data sets, inter-relation between descriptors;

 (2) data mining, descriptor (pre) selection, model development tools (including different modelling and knowledge extraction algorithms from multivariate regressions to machine learning and deep learning), model optimization and validation algorithms, model quality parameters (metrics), random accuracy;

 (3) model applicability domain definition, improvement of model interpretability, model predictivity on real (external) sets of molecules, ensemble (consensus) QSAR/QSPR modelling.

The contributions/studies covering each of the mentioned aspects connected to the improvement of QSAR/QSPR methodology are welcome, as well as comparative studies performed on benchmark data sets. Applications of QSAR/QSPR methodology on different problems related to continuous or classification endpoints (activities or properties) in drug design, chemical, pharmaceutical, biotechnological or environmental sciences, chemical engineering, bioscience or nanoscience are welcome.

Dr. Bono Lučić
Prof. Dr. Bakhtiyor Rasulev
Guest Editors

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