QSPR/QSAR Analysis Using SMILES and Quasi-SMILES

Alla P. Toropova is a researcher from Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy.  Her main direction of research activity is establishing quantitative structure-property/activity relationships (QSPRs/QSARs) for inorganic and metalorganic compounds. She has published about 280 papers in international journals dedicated to QSPR/QSAR analysis. She took part in the development and evolution of so-called optimal descriptors that were calculated with a simplified molecular input-line entry system (SMILES). In addition, she collaborated on developing a new approach to the QSPR/QSAR analysis based on so-called quasi-SMILES. She is the website developer(http://www.insilico.eu/coral) for the CORAL software that implements conceptions of the Monte Carlo optimization of optimal descriptors calculated with SMILES and quasi-SMILES.Andrey A. Toropov is a researcher from Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy. His research activity is dedicated to quantitative structure-property/activity relationships (QSPRs/QSARs). He is co-author of about 360 papers in international journals devoted to QSPR/QSAR analysis. He is the developer of CORAL software that is applied to building up QSPR/QSAR models for different endpoints. His current activity is dedicated to developing the system of quasi-SMILES as input data for the CORAL software (http://www.insilico.eu/coral). It can become a new approach for building up QSPR/QSAR for polymers, peptides, and nanomaterials.

• Part I - Theoretical conceptions.- Fundamentals of mathematical modeling of chemicals through QSPR/QSAR.- Molecular descriptors in QSPR/QSAR modeling.- Application of SMILES to cheminformatics and generation of optimum SMILES descriptors using CORAL software.- Part II - SMILES based descriptors.- All SMILES Variational Autoencoder for Molecular Property Prediction and Optimization.- SMILES based bioactivity descriptors to model the anti-Dengue virus activity: A case study.- Part III - SMILES for QSPR/QSAR with optimal descriptors.- QSPR models for prediction of redox potentials using optimal descriptors.- Building up QSPR for polymers endpoints by using SMILES-based optimal descriptors.- Part IV - Quasi-SMILES for QSPR/QSAR.- Quasi-SMILES based QSPR/QSAR modeling.- Quasi-SMILES Based Mathematical Model for the Prediction of Percolation Threshold for Conductive Polymer Composites.- On the possibility to build up the QSAR model of different kinds of inhibitory activity for a large list of Human Intestinal Transporter using quasi-SMILES.- Quasi-SMILES as a tool for peptide QSAR modelling.- Part V - SMILES and quasi-SMILES for QSPR/QSAR.- SMILES and quasi-SMILES descriptors in QSAR/QSPR modeling of diverse materials properties in safety and environment application.- SMILES and quasi-SMILES in QSAR Modeling for Prediction of Physicochemical and Biochemical Properties.- Part VI - Possible ways of nano-QSPR/nano-QSAR evolution.- The CORAL software as a tool to develop models for nanomaterials’ endpoints.- Employing Quasi-SMILES notation in development of nano-QSPR models for nanofluids.- Part VII - Possible ways of QSPR/QSAR evolution in the future.- On complementary approaches of assessing the predictive potential of QSPR/QSAR-models.- CORAL: Predictions of Quality of Rice based on Retention index using a combination of Correlation intensity index and Consensus modelling.