in aplhabetical order QSAR MODELING AND LIBRARY DESIGN STRATEGIES Dr. Wolfram Altenhofen Chemical Computing Group AG, Lörrach, Germany [email protected] The session will be devided into an introduction to basic concepts of QSAR Modeling and Library Design and a hands-on tutorial which will allow participants to experience the basic steps from deriving a QSAR model to designing a focused libra...

A set of benzophenone derivatives was evaluated for the antimalarial activity against Plasmodium berghei in mice and the mean survival time of mice for all the compounds was determined. The QSAR analysis was carried out for the fourteen benzophenone derivatives using different physicochemical descriptors. The multiple linear regression analysis was used to correlate the physicochemical descript...

binary logistic regression (blr) has been developed as non-linear models to establish quantitative structure- activity relationships (qsar) between structural descriptors and biochemical activity of carbonic anhydrase inhibitors. using a training set consisted of 21 compounds with known ki values, the model was trained and tested to solve two-class problems as active or inactive on the basis of...

A common practice in modern QSAR modelling is to derive models by variable selection methods working on large descriptor pools. As pointed out previously, this is intrinsically burdened with the risk of finding random correlations. Therefore it is desirable to perform tests showing the performance of models built on random data. In this contribution, we introduce a simple and freely available s...

Inverse quantitative structure-activity relationship (QSAR) modeling encompasses the generation of compound structures from values of descriptors corresponding to high activity predicted with a given QSAR model. Structure generation proceeds from descriptor coordinates optimized for activity prediction. Herein, we concentrate on the first phase of the inverse QSAR process and introduce a new me...

To design new chemotypes with enhanced potencies against the HIV integrase enzyme, 3D pharmacophore models were generated and QSAR study was carried out on 44 novel indole b-diketo acid derivatives and coumarin-based Inhibitors. A five-point pharmacophore with two hydrogen bond acceptors (A) and three aromatic rings (R) as pharmacophore features was developed by PHASE module of Schrodinger suit...

This article describes a method developed for predicting anticancer/non-anticancer drugs using artificial neural network (ANN). The ANN used in this study is a feed-forward neural network with a standard back-propagation training algorithm. Using 30 'inductive' QSAR descriptors alone, we have been able to achieve 84.28% accuracy for correct separation of compounds with- and without anticancer a...

BACKGROUND The inverse-QSAR problem seeks to find a new molecular descriptor from which one can recover the structure of a molecule that possess a desired activity or property. Surprisingly, there are very few papers providing solutions to this problem. It is a difficult problem because the molecular descriptors involved with the inverse-QSAR algorithm must adequately address the forward QSAR p...

The dataset of curcumin derivatives consists of 45 compounds (Table 1) with their anti cancer biological activity (IC50) against P388 cell line. 45 curcumin derivatives were used in the model development where 30 of these compounds were in the training set and the remaining 15 compounds were in the test set. The development of the QSAR model involved the use of the multiple linear regression an...

Multivariate Image Analysis applied in Quantitative Structure-Activity Relationship (MIA-QSAR) is a simple method to achieve, at least in a variety of examples, QSAR models with predictive abilities comparable to those of sophisticated tridimensional methodologies. MIA-QSAR is based on the correlation between properties (e.g. biological activities) and chemical descriptors, which are pixels of ...