Journal ArticleDOI
Molecular similarity and diversity in chemoinformatics: from theory to applications.
TLDR
The approaches used to define and descript the concepts of molecular similarity and diversity in the context of chemoinformatics are discussed and the descriptions and analyses of different methods and techniques are introduced.Abstract:
This review is dedicated to a survey on molecular similarity and diversity. Key findings reported in recent investigations are selectively highlighted and summarized. Even if this overview is mainly centered in chemoinformatics, applications in other areas (pharmaceutical and medical chemistry, combinatorial chemistry, chemical databases management, etc.) are also introduced. The approaches used to define and descript the concepts of molecular similarity and diversity in the context of chemoinformatics are discussed in the first part of this review. We introduce, in the second and third parts, the descriptions and analyses of different methods and techniques. Finally, current applications and problems are enumerated and discussed in the last part.read more
Citations
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Journal ArticleDOI
Machine learning in chemoinformatics and drug discovery.
TL;DR: Basic principles and recent case studies are presented to demonstrate the utility of machine learning techniques in chemoinformatics analyses; and limitations and future directions are discussed to guide further development in this evolving field.
Journal ArticleDOI
Computational drug discovery
TL;DR: Over the past decades, computational drug discovery methods such as molecular docking, pharmacophore modeling and mapping, de novo design, molecular similarity calculation and sequence-based virtual screening have been greatly improved.
Journal ArticleDOI
Scaffold diversity of natural products: inspiration for combinatorial library design
TL;DR: An application of the self-organizing map technique is presented for natural product-derived compound and library design and compares properties and pharmacophoric features of drugs and natural products.
Journal ArticleDOI
ShaEP: molecular overlay based on shape and electrostatic potential.
TL;DR: ShaEP overlays drug-sized molecules on a subsecond timescale, allowing for the screening of large virtual libraries, and aims to capture the strengths of both field-based and volumetric approaches.
Journal ArticleDOI
How drugs get into cells: tested and testable predictions to help discriminate between transporter-mediated uptake and lipoidal bilayer diffusion.
TL;DR: The view that Phospholipid Bilayer diffusion Is Negligible (PBIN) provides a starting hypothesis for assessing cellular drug uptake that is much better supported by the available evidence, and is both more productive and more predictive.
References
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Book
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?
Book
Computers and Intractability: A Guide to the Theory of NP-Completeness
TL;DR: The second edition of a quarterly column as discussed by the authors provides a continuing update to the list of problems (NP-complete and harder) presented by M. R. Garey and myself in our book "Computers and Intractability: A Guide to the Theory of NP-Completeness,” W. H. Freeman & Co., San Francisco, 1979.
Journal ArticleDOI
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Journal ArticleDOI
Hierarchical Grouping to Optimize an Objective Function
TL;DR: In this paper, a procedure for forming hierarchical groups of mutually exclusive subsets, each of which has members that are maximally similar with respect to specified characteristics, is suggested for use in large-scale (n > 100) studies when a precise optimal solution for a specified number of groups is not practical.