Chemoinformatics and Advanced Machine Learning Perspectives: Complex Computational Methods and Collaborative Techniques

  • 9h 50m
  • Huma Lodhi, Yoshihiro Yamanishi (eds)
  • IGI Global
  • 2011

Chemoinformatics is a scientific area that endeavours to study and solve complex chemical problems using computational techniques and methods.

Chemoinformatics and Advanced Machine Learning Perspectives: Complex Computational Methods and Collaborative Techniques provides an overview of current research in machine learning and applications to chemoinformatics tasks. As a timely compendium of research, this book offers perspectives on key elements that are crucial for complex study and investigation.

About the Editor

Huma Lodhi obtained her Ph.D. in computer science from University of London. She is a researcher with the department of Computing, Imperial College London. She has published in leading international journals, books, conference proceedings and has edited a volume Elements of Computational Systems Biology (Wiley Series in Bioinformatics), (2010) by Huma M Lodhi and Stephen H Muggleton (Editors), Wiley. Her research interests are machine learning and data mining and their application to tasks in bioinformatics, chemoinformatics and computation systems biology.

Yoshihiro Yamanishi is a faculty member at Centre for Computational Biology, Mines ParisTech, France. He is also a researcher in the department of Bioinformatics and Computational Systems Biology of Cancer, Mines ParisTech - Institut Curie - INSERM U900. He is working on statistics and machine learning for bioinformatics, chemoinformatics, and genomic drug discovery. He obtained his Ph.D in 2005 from Kyoto University in Japan. He was a post-doctoral research fellow at Center for Geostatistics, Ecole des Mines de Paris from 2005 to 2006. He was an assistant professor at Institute for Chemical Research, Kyoto University from 2006 to 2007.

In this Book

  • Graph Kernels for Chemoinformatics
  • Optimal Assignment Kernels for ADME in Silico Prediction
  • 3D Ligand-Based Virtual Screening with Support Vector Machines
  • A Simulation Study of the Use of Similarity Fusion for Virtual Screening
  • Structure-Activity Relationships by Autocorrelation Descriptors and Genetic Algorithms
  • Graph Mining in Chemoinformatics
  • Protein Homology Analysis for Function Prediction with Parallel Sub-Graph Isomorphism
  • Advanced PLS Techniques in Chemometrics and Their Applications to Molecular Design
  • Nonlinear Partial Least Squares: An Overview
  • Virtual Screening Methods Based on Bayesian Statistics
  • Learning Binding Affinity from Augmented High Throughput Screening Data
  • Application of Machine Leaning in Drug Discovery and Development
  • Learning and Prediction of Complex Molecular Structure-Property Relationships: Issues and Strategies for Modeling Intestinal Absorption for Drug Discovery
  • Learning Methodologies for Detection and Classification of Mutagens
  • Brain-like Processing and Classification of Chemical Data: An Approach Inspired by the Sense of Smell
  • Prediction of Compound-Protein Interactions with Machine Learning Methods
  • Chemoinformatics on Metabolic Pathways: Attaching Biochemical Information on Putative Enzymatic Reactions