This brief offers an introduction to the fascinating new field of quantitative read-across structure-activity relationships (q-RASAR) as a cheminformatics modeling approach in the background of quantitative structure-activity relationships (QSAR) and read-across (RA) as data gap-filling methods. It discusses the genesis and model development of q-RASAR models demonstrating practical examples. It also showcases successful case studies on the application of q-RASAR modeling in medicinal chemistry, predictive toxicology, and materials sciences. The book also includes the tools used for q-RASAR model development for new users. It is a valuable resource for researchers and students interested in grasping the development algorithm of q-RASAR models and their application within specific research domains.

lgli/3031520564.pdf

lgrsnf/3031520564.pdf

Springer Nature Switzerland AG

Springer Nature, Cham, 2024

Switzerland, Switzerland

1st ed. 2024, FR, 2024

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Foreword
Preface
Contents
1 Chemical Information and Molecular Similarity
1.1 Introduction
1.2 Chemical Information
1.2.1 Chemical Bonding and Interactions
1.2.2 Structure–Property/Activity Relationships
1.3 Molecular Similarity
1.4 Conclusion
References
2 Read-Across and Quantitative Structure–Activity Relationships (QSAR) for Making Predictions and Data Gap-Filling
2.1 Introduction
2.2 Read-Across
2.2.1 Read-Across Hypothesis—The Read-Across Assessment Framework (RAAF) [6]
2.2.2 Structuring and Reporting Read-Across Predictions
2.2.3 Defining the Applicability Domain (AD) of Read-Across
2.3 Quantitative Structure–Activity Relationship (QSAR)
2.3.1 Steps Involved in QSAR Analysis
2.3.2 Applications of QSAR Modeling
2.3.3 QSAR Model Reporting Format (QMRF)
2.3.4 QsarDB
2.4 Conclusion
References
3 Quantitative Read-Across (q-RA) and Quantitative Read-Across Structure–Activity Relationships (q-RASAR)—Genesis and Model Development
3.1 Introduction
3.2 Quantitative Read-Across
3.2.1 GenRA
3.2.2 ToxRead
3.2.3 Apellis
3.2.4 Isalos Analytics Platform
3.2.5 Deimos
3.2.6 Read-Across-v4.2.1
3.3 Read-Across Structure–Activity Relationship (RASAR)
3.3.1 Quantitative Read-Across Structure–Activity Relationship (q-RASAR)
3.4 Conclusion
References
4 Tools, Applications, and Case Studies (q-RA and q-RASAR)
4.1 Introduction
4.2 Cheminformatic Tools from the DTC Laboratory to Perform Read-Across and RASAR Modeling
4.2.1 Read-Across-v4.2.1
4.2.2 Auto_RA_Optimizer-v1.0
4.2.3 RASAR-Desc-Calc-v3.0.2
4.3 Applications and Case Studies of q-RA and q-RASAR
4.3.1 Applications of q-RA
4.3.2 Applications of q-RASAR
4.4 Conclusion
References
5 Future Prospects
References

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SpringerBriefs in Molecular Science
Erscheinungsdatum: 26.01.2024

2024-02-25