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QSPR/QSAR Analysis Using SMILES and Quasi-SMILES

  • Book
  • © 2023

Overview

Editors:
  1. Alla P. Toropova

    1. Department of Environmental Health Science, Institute of Pharmacological Research Mario Negri IRCCS, Milan, Italy

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    You can also search for this editor in PubMed   Google Scholar

  2. Andrey A. Toropov

    1. Department of Environmental Health Science, Institute of Pharmacological Research Mario Negri IRCCS, Milan, Italy

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  • Overviews the simplifying molecular input-line entry system (SMILES)
  • Presents a fresh extending of space for model design
  • Features contributions from leading experts in the field

Part of the book series: Challenges and Advances in Computational Chemistry and Physics (COCH, volume 33)

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Table of contents (17 chapters)

  1. Front Matter

    Pages i-xviii
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  2. Theoretical Conceptions

    1. Front Matter

      Pages 1-1
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    2. Fundamentals of Mathematical Modeling of Chemicals Through QSPR/QSAR

      • Andrey A. Toropov, Maria Raskova, Ivan Raska Jr., Alla P. Toropova
      Pages 3-24

    3. Molecular Descriptors in QSPR/QSAR Modeling

      • Shahin Ahmadi, Sepideh Ketabi, Marjan Jebeli Javan
      Pages 25-56

    4. Application of SMILES to Cheminformatics and Generation of Optimum SMILES Descriptors Using CORAL Software

      • Andrey A. Toropov, Alla P. Toropova
      Pages 57-82

  3. SMILES Based Descriptors

    1. Front Matter

      Pages 83-83
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    2. All SMILES Variational Autoencoder for Molecular Property Prediction and Optimization

      • Zaccary Alperstein, Artem Cherkasov, Jason Tyler Rolfe
      Pages 85-115

    3. SMILES-Based Bioactivity Descriptors to Model the Anti-dengue Virus Activity: A Case Study

      • Soumya Mitra, Sumit Nandi, Amit Kumar Halder, M. Natalia D. S. Cordeiro
      Pages 117-136

  4. SMILES for QSPR/QSAR with Optimal Descriptors

    1. Front Matter

      Pages 137-137
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    2. QSPR Models for Prediction of Redox Potentials Using Optimal Descriptors

      • Karel NesmÄ›rák, Andrey A. Toropov
      Pages 139-166

    3. Building Up QSPR for Polymers Endpoints by Using SMILES-Based Optimal Descriptors

      • Valentin O. Kudyshkin, Alla P. Toropova
      Pages 167-187

  5. Quasi-SMILES for QSPR/QSAR

    1. Front Matter

      Pages 189-189
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    2. Quasi-SMILES-Based QSPR/QSAR Modeling

      • Shahin Ahmadi, Neda Azimi
      Pages 191-210

    3. Quasi-SMILES-Based Mathematical Model for the Prediction of Percolation Threshold for Conductive Polymer Composites

      • Swayam Aryam Behera, Alla P. Toropova, Andrey A. Toropov, P. Ganga Raju Achary
      Pages 211-239

    4. 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

      • P. Ganga Raju Achary, P. Kali Krishna, Alla P. Toropova, Andrey A. Toropov
      Pages 241-268

    5. Quasi-SMILES as a Tool for Peptide QSAR Modelling

      • Md. Moinul, Samima Khatun, Sk. Abdul Amin, Tarun Jha, Shovanlal Gayen
      Pages 269-294

  6. SMILES and Quasi-SMILES for QSPR/QSAR

    1. Front Matter

      Pages 295-295
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    2. SMILES and Quasi-SMILES Descriptors in QSAR/QSPR Modeling of Diverse Materials Properties in Safety and Environment Application

      • Yong Pan, Xin Zhang, Juncheng Jiang
      Pages 297-325

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

      • Siyun Yang, Supratik Kar, Jerzy Leszczynski
      Pages 327-348

  7. Possible Ways of Nano-QSPR/Nano-QSAR Evolution

    1. Front Matter

      Pages 349-349
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Keywords

About this book

This contributed volume overviews recently presented approaches for carrying out QSPR/QSAR analysis by using a simplifying molecular input-line entry system (SMILES) to represent the molecular structure. In contrast to traditional SMILES, quasi-SMILES is a sequence of special symbols-codes that reflect molecular features and codes of experimental conditions. SMILES and quasi-SMILES serve as a basis to develop QSPR/QSAR as well Nano-QSPR/QSAR via the Monte Carlo calculation that provides the so-called optimal descriptors for QSPR/QSAR models.  The book presents a reliable technology for developing Nano-QSPR/QSAR while it also includes the description of the algorithms of the Monte Carlo optimization. It discusses the theory and practice of the technique of variational authodecoders (VAEs) based on SMILES and analyses in detail the index of ideality of correlation (IIC) and the correlation intensity index (CII) which are new criteria for the predictive potential of the model. The mathematical apparatus used is simple so that students of relevant specializations can easily follow. This volume is a valuable contribution to the field and will be of great interest to developers of models of physicochemical properties and biological activity, chemical technologists, and toxicologists involved in the area of drug design.





Editors and Affiliations

  • Department of Environmental Health Science, Institute of Pharmacological Research Mario Negri IRCCS, Milan, Italy

    Alla P. Toropova, Andrey A. Toropov

About the editors

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.

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