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

High-throughput and virtual screening are important components of modern drug discovery research. Typically, these screening technologies are considered distinct approaches, as one is experimental and the other is theoretical in nature. However, given their similar tasks and goals, these approaches are much more complementary to each other than often thought. Various statistical, informatics and filtering methods have recently been introduced to foster the integration of experimental and in silico screening and maximize their output in drug discovery. Although many of these ideas and efforts have not yet proceeded much beyond the conceptual level, there are several success stories and good indications that early-stage drug discovery will benefit greatly from a more unified and knowledge-based approach to biological screening, despite the many technical advances towards even higher throughput that are made in the screening arena.

Integration of virtual and high-throughput screening.

Using a simple model of ligand−receptor interactions, the interactions between ligands and receptors of varying complexities are studied and the probabilities of binding calculated. It is observed that as the systems become more complex the chance of observing a useful interaction for a randomly chosen ligand falls dramatically. The implications of this for the design of combinatorial libraries is explored. A large set of drug leads and optimized compounds is profiled using several different properties relevant to molecular recognition. The changes observed for these properties during the drug optimization phase support the hypothesis that less complex molecules are more common starting points for the discovery of drugs. An extreme example of the use of simple molecules for directed screening against thrombin is provided.

Molecular Complexity and Its Impact on the Probability of Finding Leads for Drug Discovery

Molecular Informatics utilises many ideas and concepts to find relationships between molecules. The concept of similarity, where molecules may be grouped according to their biological effects or physicochemical properties has found extensive use in drug discovery. Some areas of particular interest have been in lead discovery and compound optimisation. For example, in designing libraries of compounds for lead generation, one approach is to design sets of compounds ‘similar’ to known active compounds in the hope that alternative molecular structures are found that maintain the properties required while enhancing e.g. patentability, medicinal chemistry opportunities or even in achieving optimised pharmacokinetic profiles. Thus the practical importance of the concept of molecular similarity has grown dramatically in recent years. The predominant users are pharmaceutical companies, employing similarity methods in a wide range of applications e.g. virtual screening, estimation of absorption, distribution, metabolism, excretion and toxicity (ADME/Tox) and prediction of physicochemical properties (solubility, partitioning etc.). In this perspective, we discuss the representation of molecular structure (descriptors), methods of comparing structures and how these relate to measured properties. This leads to the concept of molecular similarity, its various definitions and uses and how these have evolved in recent years. Here, we wish to evaluate and in some cases challenge accepted views and uses of molecular similarity. Molecular similarity, as a paradigm, contains many implicit and explicit assumptions in particular with respect to the prediction of the binding and efficacy of molecules at biological receptors. The fundamental observation is that molecular similarity has a context which both defines and limits its use. The key issues of solvation effects, heterogeneity of binding sites and the fundamental problem of the form of similarity measure to use are addressed.

Molecular similarity: a key technique in molecular informatics.

In the absence of three-dimensional (3D) structures of potential drug targets, ligand-based drug design is one of the popular approaches for drug discovery and lead optimization. 3D structure-activity relationships (3D QSAR) and pharmacophore modeling are the most important and widely used tools in ligand-based drug design that can provide crucial insights into the nature of the interactions between drug target and ligand molecule and provide predictive models suitable for lead compound optimization. This review article will briefly discuss the features and potential application of recent advances in ligand-based drug design, with emphasis on a detailed description of a novel 3D QSAR method based on the conformationally sample pharmacophore (CSP) approach (denoted CSP-SAR). In addition, data from a published study are used to compare the CSP-SAR approach to the Catalyst method, emphasizing the utility of the CSP approach for ligand-based model development.

/pdf/recent-advances-in-ligand-based-drug-design-relevance-and-4sm6li8guu.pdf

Recent advances in ligand-based drug design: relevance and utility of the conformationally sampled pharmacophore approach.

4D-QSAR analysis incorporates conformational and alignment freedom into the development of 3D-QSAR models for training sets of structure−activity data by performing ensemble averaging, the fourth “dimension”. The descriptors in 4D-QSAR analysis are the grid cell (spatial) occupancy measures of the atoms composing each molecule in the training set realized from the sampling of conformation and alignment spaces. Grid cell occupancy descriptors can be generated for any atom type, group, and/or model pharmacophore. A single “active” conformation can be postulated for each compound in the training set and combined with the optimal alignment for use in other molecular design applications including other 3D-QSAR methods. The influence of the conformational entropy of each compound on its activity can be estimated. Serial use of partial least-squares, PLS, regression and a genetic algorithm, GA, is used to perform data reduction and identify the manifold of top 3D-QSAR models for a training set. The unique manifo...

Construction of 3D-QSAR Models Using the 4D-QSAR Analysis Formalism

The 4D-QSAR model developed for a training set of 47 glucose analogue inhibitors of glycogen phosphorylase, and reported in the previous paper in this issue, was used as a basis for developing virt...

Construction of a Virtual High Throughput Screen by 4D-QSAR Analysis: Application to a Combinatorial Library of Glucose Inhibitors of Glycogen Phosphorylase b

Construction of a Virtual High Throughput Screen by 4D‐QSAR Analysis: Application to a Combinatorial Library of Glucose Inhibitors of Glycogen Phosphorylase b.

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Construction of 3D-QSAR Models Using the 4D-QSAR Analysis Formalism

Construction of a Virtual High Throughput Screen by 4D-QSAR Analysis: Application to a Combinatorial Library of Glucose Inhibitors of Glycogen Phosphorylase b

Construction of a Virtual High Throughput Screen by 4D‐QSAR Analysis: Application to a Combinatorial Library of Glucose Inhibitors of Glycogen Phosphorylase b.