Computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades. These methods are broadly classified as either structure-based or ligand-based methods. Structure-based methods are in principle analogous to high-throughput screening in that both target and ligand structure information is imperative. Structure-based approaches include ligand docking, pharmacophore, and ligand design methods. The article discusses theory behind the most important methods and recent successful applications. Ligand-based methods use only ligand information for predicting activity depending on its similarity/dissimilarity to previously known active ligands. We review widely used ligand-based methods such as ligand-based pharmacophores, molecular descriptors, and quantitative structure-activity relationships. In addition, important tools such as target/ligand data bases, homology modeling, ligand fingerprint methods, etc., necessary for successful implementation of various computer-aided drug discovery/design methods in a drug discovery campaign are discussed. Finally, computational methods for toxicity prediction and optimization for favorable physiologic properties are discussed with successful examples from literature.

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Computational Methods in Drug Discovery

This article provides an overview of methods for reliability assessment of quantitative structure-activity relationship (QSAR) models in the context of regulatory acceptance of human health and environmental QSARs. Useful diagnostic tools and data analytical approaches are highlighted and exemplified. Particular emphasis is given to the question of how to define the applicability borders of a QSAR and how to estimate parameter and prediction uncertainty. The article ends with a discussion regarding QSAR acceptability criteria. This discussion contains a list of recommended acceptability criteria, and we give reference values for important QSAR performance statistics. Finally, we emphasize that rigorous and independent validation of QSARs is an essential step toward their regulatory acceptance and implementation.

Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs.

The original formulation of the Zagreb indices is presented and their relationship to topological indices such as self-returning walks, Platt, Gordon-Scantlebury and connectivity indices is discussed. Their properties are also listed. Modified Zagreb indices are introduced and the Zagreb complexity indices reviewed. Their use in QSPR is illustrated by modeling the structure-boiling point relationship of C3–C8 alkanes using the CROMRsel procedure. The obtained models are in fair agreement with experimental data and are better than many models in the literature. However, in general, the Zagreb indices do not contribute to the best structure-boiling point models of alkanes. Nevertheless, it is interesting to note that the best five-descriptor model that we found in the literature contains the Zagreb M2 index.

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The Zagreb Indices 30 Years After

Assessing the factors responsible for ionic liquid toxicity to aquatic organisms via quantitative structure–property relationship modeling

Calculation of molecular lipophilicity: State-of-the-art and comparison of log P methods on more than 96,000 compounds.

1. No-Free-Lunch Molecular Descriptor in QSAR and QSPAR 2. The Graph Description of Chemical Structures 3. Matrices and Structural Descriptors Computed from Molecular Graph Distances 4. Molecular Connectivity Chi Indices for Database Analysis and Structure-Property Modeling 5. Novel Strategies in the Search of Topological Indices 6. The Electrotopological State: Structure Modeling for QSAR and Database Analysis 7. Autocorrelation Descriptors for Modeling (Eco)Toxicological Endpoints 8. A Hierarchical Approach to the Development of QSAR Models Using Topological, Geometrical and Quantum, Chemical Parameters 9. Algorithms and Software for the Computation of Topological Indices and Structure-Property Models

James Devillers

Papers

Topological Indices and Related Descriptors in QSAR and QSPR

Vertex- and Edge-Weighted Molecular Graphs and Derived Structural Descriptors

Algorithms and Software for the Computation of Topological Indices and Structure-Property Models