In silico medicinal chemistry : computational methods to support drug design / Nathan Brown.

Format:

Book

Author/Creator:

Brown, Nathan, author.

Contributor:

Royal Society of Chemistry (Great Britain), issuing body.

Series:

RSC theoretical and computational chemistry series ; 8.

RSC Theoretical and Computational Chemistry Series, 2041-3181 ; 8

Language:

English

Subjects (All):

Pharmaceutical chemistry.

Pharmaceutical chemistry--Data processing.

Cheminformatics.

Physical Description:

1 online resource (232 p.)

Place of Publication:

Cambridge, England : Royal Society of Chemistry, 2016.

Language Note:

English

Summary:

Exploring the methodologies and applications ofcomputational tools in drug design, this book is a practical introduction to chemoinformatics, molecular modelling and computational chemistry for researchers.

Contents:

Cover; In Silico Medicinal Chemistry Computational Methods to Support Drug Design; Preface; Contents; Part 1 Introduction; Chapter 1 - Introduction; 1.1 Overview; Part 2 Molecular Representations; Chapter 2 - Chemistry and Graph Theory; 2.1 Overview; 2.2 Graph Theory and Chemistry; 2.3 Graph Theory in Chemistry; 2.4 Mathematical Chemistry and Chemical Graph Theory; 2.5 Summary; References; Chapter 3 - Structure Representation; 3.1 Overview; 3.2 The Need for Machine-Readable Structure Representations; 3.3 Adjacency Matrix; 3.4 Connection Table; 3.5 Line Notations

3.5.1 WLN: Wiswesser Line Notation3.5.2 SMILES: Simplified Molecular-Input Line-Entry Specification; 3.5.3 InChI: IUPAC International Chemical Identifier; 3.6 Summary; References; Chapter 4 - Molecular Similarity; 4.1 Overview; 4.2 Molecular Similarity; 4.3 Similar Property Principle; 4.4 Molecular Descriptors; 4.5 Calculation of Molecular Similarity; 4.5.1 Similarity Coefficients; 4.6 Molecular Diversity; 4.7 Summary; References; Part 3 Molecular Descriptors; Chapter 5 - Molecular Property Descriptors; 5.1 Overview; 5.2 Molecular Weight (MW or MWt)

6.3.3.2 Hopfen Fingerprints6.4 Summary; References; Chapter 7 - Topographical Descriptors; 7.1 Overview; 7.2 Topographic Descriptors; 7.3 Pharmacophores; 7.4 ROCS: Rapid Overlay of Chemical Structures; 7.5 USR: Ultrafast Shape Recognition; 7.6 XED: Cresset Group; 7.7 Conformer Generation and the Conformer Problem; 7.8 Summary; References; Part 4 Statistical Learning; Chapter 8 - Statistical Learning; 8.1 Overview; 8.2 Statistical Learning; 8.3 Unsupervised Learning; 8.3.1 Overview; 8.3.2 Cluster Analysis; 8.3.3 k-Means Clustering; 8.3.4 Stirling Numbers of the Second Kind

8.3.5 Self-Organising Maps8.3.6 Principal Component Analysis; 8.4 Supervised Learning; 8.4.1 Naïve Bayesian Classification; 8.4.2 Support Vector Machine; 8.4.3 Partial Least Squares; 8.5 Best Modelling Practice; 8.6 Summary; References; Part 5 Modelling Methodologies; Chapter 9 - Similarity Searching; 9.1 Overview; 9.2 Similar Property Principle; 9.3 Molecular Similarity and Virtual Screening; 9.4 Data Fusion; 9.5 Enrichment; 9.5.1 Lift Plots; 9.5.2 Confusion Matrix; 9.5.3 Receiver Operating Characteristic Curves; 9.5.4 Enrichment Factors; 9.6 Summary; References

Chapter 10 - Bioisosteres and Scaffolds

Notes:

Description based upon print version of record.

Includes bibliographical references at the end of each chapters and index.

Description based on online resource; title from PDF title page (ebrary, viewed December 10, 2015).

ISBN:

9781782622604

1782622608