Extended-connectivity fingerprints (ECFPs) are a novel class of topological fingerprints for molecular characterization. Historically, topological fingerprints were developed for substructure and similarity searching. ECFPs were developed specifically for structure−activity modeling. ECFPs are circular fingerprints with a number of useful qualities: they can be very rapidly calculated; they are not predefined and can represent an essentially infinite number of different molecular features (including stereochemical information); their features represent the presence of particular substructures, allowing easier interpretation of analysis results; and the ECFP algorithm can be tailored to generate different types of circular fingerprints, optimized for different uses. While the use of ECFPs has been widely adopted and validated, a description of their implementation has not previously been presented in the literature.

Extended-Connectivity Fingerprints

The Environmental Protection Agency (EPA) provides access to information on a variety of topics related to the environment and strives to inform citizens of health risks. The EPA also has an extensive library network that consists of 26 libraries throughout the United States, which provide access to a plethora of information to EPA employees, scientists, and researchers. The EPA implemented a reorganization project to digitize their materials so they would be more accessible to a wider range of users, but this plan was drastically accelerated when the EPA was threatened with a budget cut. It chose to close and reduce the hours and services of some of their libraries. As a result, the agency was accused of denying users the “right to know” by making information unavailable, not providing an adequate strategic plan, and discarding vital materials. This case study explores the background of the digitization project, the practices implemented, and the critiques of the project.

/pdf/the-environmental-protection-agency-3gx4opzrjm.pdf

The Environmental Protection Agency

Introduction
PaDEL-Descriptor is a software for calculating molecular descriptors and fingerprints. The software currently calculates 797 descriptors (663 1D, 2D descriptors, and 134 3D descriptors) and 10 types of fingerprints. These descriptors and fingerprints are calculated mainly using The Chemistry Development Kit. Some additional descriptors and fingerprints were added, which include atom type electrotopological state descriptors, McGowan volume, molecular linear free energy relation descriptors, ring counts, count of chemical substructures identified by Laggner, and binary fingerprints and count of chemical substructures identified by Klekota and Roth.

Methods
PaDEL-Descriptor was developed using the Java language and consists of a library component and an interface component. The library component allows it to be easily integrated into quantitative structure activity relationship software to provide the descriptor calculation feature while the interface component allows it to be used as a standalone software. The software uses a Master/Worker pattern to take advantage of the multiple CPU cores that are present in most modern computers to speed up calculations of molecular descriptors.

Results
The software has several advantages over existing standalone molecular descriptor calculation software. It is free and open source, has both graphical user interface and command line interfaces, can work on all major platforms (Windows, Linux, MacOS), supports more than 90 different molecular file formats, and is multithreaded.

Conclusion
PaDEL-Descriptor is a useful addition to the currently available molecular descriptor calculation software. The software can be downloaded at http://padel.nus.edu.sg/software/padeldescriptor. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2011

https://onlinelibrary.wiley.com/doi/pdf/10.1002/jcc.21707

PaDEL‐descriptor: An open source software to calculate molecular descriptors and fingerprints

This paper emphasizes the importance of rigorous validation as a crucial, integral component of Quantitative Structure Property Relationship (QSPR) model development. We consider some examples of published QSPR models, which in spite of their high fitted accuracy for the training sets and apparent mechanistic appeal, fail rigorous validation tests, and, thus, may lack practical utility as reliable screening tools. We present a set of simple guidelines for developing validated and predictive QSPR models. To this end, we discuss several validation strategies including (1) randomization of the modelled property, also called Y-scrambling, (2) multiple leave-many-out cross-validations, and (3) external validation using rational division of a dataset into training and test sets. We also highlight the need to establish the domain of model applicability in the chemical space to flag molecules for which predictions may be unreliable, and discuss some algorithms that can be used for this purpose. We advocate the broad use of these guidelines in the development of predictive QSPR models.

The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models

The recent REACH Policy of the European Union has led to scientists and regulators to focus their attention on establishing general validation principles for QSAR models in the context of chemical regulation (previously known as the Setubal, nowadays, the OECD principles). This paper gives a brief analysis of some principles: unambiguous algorithm, Applicability Domain (AD), and statistical validation. Some concerns related to QSAR algorithm reproducibility and an example of a fast check of the applicability domain for MLR models are presented. Common myths and misconceptions related to popular techniques for verifying internal predictivity, particularly for MLR models (for instance crossvalidation, bootstrap), are commented on and compared with commonly used statistical techniques for external validation. The differences in the two validating approaches are highlighted, and evidence is presented that only models that have been validated externally, after their internal validation, can be considered reliable and applicable for both external prediction and regulatory purposes. (“Validation is one of those words...that is constantly used and seldom defined” as stated by A. R. Feinstein in the book Multivariate Analysis: An Introduction, Yale University Press, New Haven, 1996).

Principles of QSAR models validation: internal and external

Users guide notations acronyms list of molecular descriptors. Appendices: counting and topological descriptors calculation of descriptors tables of molecular descriptor values.

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Handbook of Molecular Descriptors

Quantitative structure–activity relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists toward collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR stu...

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QSAR Modeling: Where have you been? Where are you going to?

Volume I: ALPHABETICAL LISTING Introduction Historical Perspective QSAR/QSPR Modeling How to Learn From This Book Users Guide Notations and Symbols Alphabetical Listing of approx. 3300 entries Greek Alphabet Entries Numerical Entries Volume II: APPENDICES, REFERENCES Full bibliography of more than 6000 references, selected from 450 journals and covering the period from the beginning of molecular descriptor research until the year 2008 Greek alphabets Acronyms Molecular structures

Molecular descriptors for chemoinformatics

The common steps to calibrate and validate classification models based on partial least squares discriminant analysis are discussed in the present tutorial. All issues to be evaluated during model training and validation are introduced and explained using a chemical dataset, composed of toxic and non-toxic sediment samples. The analysis was carried out with MATLAB routines, which are available in the ESI of this tutorial, together with the dataset and a detailed list of all MATLAB instructions used for the analysis.

Classification tools in chemistry. Part 1: linear models. PLS-DA

ChemoinformaticsChemoinformaticsTransporters as Drug CarriersMolecular Descriptors for Chemoinformatics, 2 Volume SetComputational Approaches in Cheminformatics and BioinformaticsChemokine Receptors as Drug TargetsRecent Advances in QSAR StudiesMolecular Descriptors for ChemoinformaticsA Primer on QSAR/QSPR ModelingChemoinformatics and Computational Chemical BiologyAn Introduction to ChemoinformaticsProtein Kinases as Drug TargetsAdvances in Mathematical Chemistry and Applications:Handbook of Molecular DescriptorsAspartic Acid Proteases as Therapeutic TargetsEncyclopedia of Physical Organic Chemistry, 6 Volume SetCheminformatics and its ApplicationsAdvances in QSAR ModelingChemoinformaticsVirtual ScreeningAdvanced Methods and Applications in ChemoinformaticsHandbook of ChemoinformaticsChemoinformatics and Advanced Machine Learning Perspectives: Complex Computational Methods and Collaborative TechniquesEcometabolomicsIn Silico Medicinal ChemistryKurzlehrbuch Physikalische ChemieStatistical Modelling of Molecular Descriptors in QSAR/QSPRChemical GenomicsHandbook of Bibliometric IndicatorsProdrugs and Targeted DeliveryHandbook of Computational ChemistryThe Harary Index of a GraphChemoinformaticsThe Data Analysis HandbookMedical Product Safety EvaluationPharmacokinetics and Metabolism in Drug DesignAdvances in Mathematical Chemistry and Applications:Multiscale Modeling for Process Safety ApplicationsHandbook of Chemoinformatics AlgorithmsTutorials in Chemoinformatics This essential guide to the knowledge and tools in the field includes everything from the basic concepts to modern methods, while also forming a bridge to bioinformatics. The textbook offers a very clear and didactical structure, starting from the basics and the theory, before going on to provide an overview of the methods. Learning is now even easier thanks to exercises at the end of each section or chapter. Software tools are explained in detail, so that the students not only learn the necessary theoretical background, but also how to use the different software packages available. The wide range of applications is presented in the corresponding book Applied Chemoinformatics Achievements and Future Opportunities (ISBN 9783527342013). For Master and PhD students in chemistry, biochemistry and computer science, as well as providing an excellent introduction for other newcomers to the field.Chemokines are hormone-like signaling molecules secreted by cells to signal infection and guide the immune response. Following a decade of basic chemokine research, the pharmaceutical industry has now begun to exploit this crucial signaling pathway for the development of innovative drugs against AIDS, cancer, neural and autoimmune diseases. Here is the first reference focusing on these novel drug development opportunities. Opening with a general introduction on chemokine function and chemokine receptor biology, the second part covers the known implications of these signaling molecules in human diseases, such as cancer, neural disorders, and viral infection, including AIDS. The third part systematically surveys current drug development efforts at targeting individual chemokine receptors, as well as other chemokine interaction partners, including up-to-date reports from the pharmaceutical industry.Well-recognized pioneers and investigators from diverse professional environments survey the key concepts in the field, describe cutting-edge methods, and provide exemplary pharmaceutical applications. The authors explain the theory behind the crucial concepts of molecular similarity and diversity, describe the challenging efforts to use chemoinformatics approaches to virtual and high-throughput

Viviana Consonni

Papers

Handbook of Molecular Descriptors

QSAR Modeling: Where have you been? Where are you going to?

Molecular descriptors for chemoinformatics

Classification tools in chemistry. Part 1: linear models. PLS-DA

Molecular Descriptors for Chemoinformatics : Volume I : Alphabetical Listing / Volume II : Appendices, References