SMILES and Quasi-SMILES in QSAR Modeling for Prediction of Physicochemical and Biochemical Properties

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QSAR modeling of diverse physicochemical and biochemical properties of organic chemicals and nanomaterials utilizing the simplified molecular-input line-entry system (SMILES) and quasi-SMILES representation is quite a popular approach nowadays. Along with the SMILES, the quasi-SMILES approach offers the likelihood to identify and weigh the statistical importance of various eclectic data accessible for computational systematization and analysis. Therefore, the quasi-SMILES can be helpful as a tool for drug design, environmental risk assessment, and regulation caused by applying nanomaterials and organic chemicals as the method gives the possibility to consider building up corresponding models. The Monte Carlo method is applied to build up the QSAR modeling employing information collected from SMILES and quasi-SMILES. The model can be freely developed using open-access CORrelation And Logic (CORAL) software. The quasi-SMILES is an ideal approach for complex chemical systems like nanomaterials where there is no limitation to choose the list of eclectic data to make a reliable, efficient, and predictive QSAR model. In the present book chapter, we will talk about the fundamental of SMILES and quasi-SMILES-based QSAR models and their major applications in physicochemical and biochemical properties prediction.

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Challenges and Advances in Computational Chemistry and Physics

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