Current Computer - Aided Drug Design - Volume 3, Issue 4, 2007

Increasing efforts to estimate the activity or any other biological property of a given compound have been made since the advent of in silico approaches for drug design. In ligand-based QSAR/QSPR methodologies, descriptors are used to correlate samples set with the corresponding dependent variables (bioactivities, toxicity, etc.), in lieu of testing experimentally the response of a drug-like compound. A large amount Read More

This review describes an overview of multivariate QSAR methods, from classical analysis to 3D approaches and new perspectives. Data exploration, multivariate regression and molecular descriptors are some topics also appraised here. Special emphasis is given to a recently developed 2D image-based approach, known as MIA-QSAR, which is an improved method in many aspects, namely computing cost, simplicity and pre Read More

Multivariate quantitative structure-activity relationship (QSAR) modeling, involving simultaneous modeling of activities towards several related endpoints, has emerged recently as an alternative to creating a group of separate models of each activity. The development of multivariate QSAR modeling has been driven by three factors. First, the number of aspects considered vital at earlier stages in the drug development pipeli Read More

This review deals with three problems: the selection of suitable chemical descriptors from a pool of variables by a simultaneous one-regression/one-observation leaving-out resampling, the comparison of the results with a generalized-regression artificial-neural network, using an unconstrained genetic algorithm (GRNN), and the prediction of protein subdomains as potential molecular drug targets. As an example, Read More

Quantitative structure-activity relationships (QSAR) are one of the most important methodologies for rational drug design. In QSAR, compounds are represented by chemical structure descriptors, and then statistical models are built to predict biological activities of candidate structures. In this paper, two principal topics in QSAR, variable selection and 3D-QSAR, are picked up and are reviewed in recent trend. The aim of variabl Read More

Recently, a new promising nonlinear method, the support vector machine (SVM), was proposed by Vapnik. It rapidly found numerous applications in chemistry, biochemistry and pharmacochemistry. Several attempts using SVM in drug design have been reported. It became an attractive nonlinear approach in this field. In this review, the theoretical basis of SVM in classification and regression is briefly described. Its applica Read More

Computer-aided molecular design (CAMD) is becoming increasingly important to the drug discovery process. Although molecular mechanics (MM) has traditionally been the computational method of choice in medicinal chemistry, the MM method has significant deficiencies when used to study electron-based properties within the drug-receptor microenvironment. Quantum mechanical methods represent a solution to this proble Read More

The identification of the molecular components involved in the aberrant processes that control proliferation, differentiation and apoptosis, is necessary for the development of chemotherapeutic interventions to restore or to destroy selectively the transformed cells. The discovery of new chemotherapeutic agents is probably one of the most reliable ways to improve our success against cancer, and intelligent drug design is Read More