Journal ArticleDOI
Computational Methods in Developing Quantitative Structure-Activity Relationships (QSAR): A Review
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This review focuses on the methodologies in constructing three main components of QSAR model, namely the methods for describing the molecular structure of compounds, for selection of informative descriptors and for activity prediction.Abstract:
Virtual filtering and screening of combinatorial libraries have recently gained attention as methods complementing the high-throughput screening and combinatorial chemistry. These chemoinformatic techniques rely heavily on quantitative structure-activity relationship (QSAR) analysis, a field with established methodology and successful history. In this review, we discuss the computational methods for building QSAR models. We start with outlining their usefulness in high-throughput screening and identifying the general scheme of a QSAR model. Following, we focus on the methodologies in constructing three main components of QSAR model, namely the methods for describing the molecular structure of compounds, for selection of informative descriptors and for activity prediction. We present both the well-established methods as well as techniques recently introduced into the QSAR domain.read more
Citations
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Journal ArticleDOI
In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling
TL;DR: Some of the in silico methods for pharmacology that are used in drug discovery and applications to specific targets and their limitations will be discussed in the second accompanying part of this review.
Journal ArticleDOI
Virtual screening strategies in drug discovery: a critical review.
Antonio Lavecchia,C. Di Giovanni +1 more
TL;DR: This review provides a comprehensive appraisal of several VS approaches currently available and special emphasis will be given to in silico chemogenomics approaches which utilize annotated ligand-target as well as protein-ligand interaction databases and which could predict or reveal promiscuous binding and polypharmacology.
Journal ArticleDOI
Therapeutic target database update 2012: a resource for facilitating target-oriented drug discovery.
Feng Zhu,Zhe Shi,Chu Qin,Lin Tao,Xin Liu,Feng Xu,Li Zhang,Yang Song,Xianghui Liu,Jingxian Zhang,Bu-Cong Han,Peng Zhang,Yu Zong Chen +12 more
TL;DR: This work added target validation information for 932 targets, and 841 quantitative structure activity relationship models for active compounds of 228 chemical types against 121 targets, to Therapeutic Target Database.
Journal ArticleDOI
Quantitative Structure–Property Relationship Modeling of Diverse Materials Properties
TL;DR: Quantitative Structure Property Relationship Modeling of Diverse Materials Properties Tu Le, V. Chandana Epa, Frank R. Burden, and David A. Winkler.
Journal ArticleDOI
Convolutional Embedding of Attributed Molecular Graphs for Physical Property Prediction
TL;DR: A convolutional neural network is employed for the embedding task of learning an expressive molecular representation by treating molecules as undirected graphs with attributed nodes and edges, and preserves molecule-level spatial information that significantly enhances model performance.
References
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The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
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Classification and regression trees
TL;DR: The methodology used to construct tree structured rules is the focus of a monograph as mentioned in this paper, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.
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Genetic algorithms + data structures = evolution programs (3rd ed.)
TL;DR: Genetic algorithms are a probabilistic search approach which are founded on the ideas of evolutionary processes and applicable to many hard optimization problems such as optimization of functions with linear and nonlinear constraints.
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Readings in computer vision: issues, problems, principles, and paradigms
TL;DR: This book presents sixty research papers, most written since 1980, that address a problem, provides a survey of major issues, ideas, and research projects, and presents reprints of key papers.
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Pattern recognition and signal processing
TL;DR: This volume is the Proceedings of the NATO Advanced Study Institute on Pattern Recognition and Signal Processing and contains what I believed to be a truly outstanding collection of papers which cover all major activities in both pattern recognition and signal processing.