Two dimensional quantitative structure activity relationship (2D-QSAR) study was performed on B-ring trifluoromethylated chromenone (flavonoid) analogues as anticancer agents. This study was performed with 25 compounds (data set) using random and manual data selection methods for the division of the data set into training and test set. Multiple linear regression analysis coupled with stepwise v...

Topological indices play an important role in Mathematical Chemistry especially in the quantitative structure-property relationship (QSPR) and quantitative structureactivity relationship (QSAR). Recent research indicates that the augmented Zagreb index (AZI) possess the best correlating ability among several topological indices. The main purpose of the current study is to establish some mathema...

This paper is concerned with the use of AI techniques in ecology. More specifically, we present a novel application of inductive logic programming (ILP) in the area of quantitative structure-activity relationships (QSARs). The activity we want to predict is the biodegradability of chemical compounds in water. In particular, the target variable is the half-life for aerobic aqueous biodegradation...

Molecular activities can be predicted by Quantitative Structure Activity Relationship (QSAR). Because of the high cost of experiments, the number of drug molecules with known activity is much less than that of unknown, to predict molecular activities utilising unlabelled instances will be an interesting issue. Here, Semi-Supervised Learning (SSL) is introduced and a SSL method, Co-Training is i...

Two dimensional quantitative structure activity relationship (QSAR) study on series of substituted 2-azetidinone derivatives was performed by using V-LIFE MDS 3.0 software. Several statistical expression for 2D QSAR were developed using statistical methods like multiple regeression, principle component regression, partial least square regression etc. Out of several models, the best five 2D QSAR...

It has long been a problem with all docking routines to find a scoring function that optimizes not only the chances of successfully predicting the binding mode of a putative binder, but which also tries to broadly reflect the potential binding affinity of the ligand with the modelled receptor. When the many approximations made when performing any docking run are taken into account – often of a ...

A new metric for the evaluation of model performance in the field of virtual screening and quantitative structure– activity relationship applications is described. This metric has been termed the power metric and is defined as the fraction of the true positive rate divided by the sum of the true positive and false positive rates, for a given cutoff threshold. The performance of this metric is c...

In the present study quantitative structure activity relationship studies were performed on a series of N-{(1H-Benzo[d] Imidazol-1-yl) (Phenyl) Methylene} Benzenamine analogues as antifungal activity using Chem Office ultra 8.0.3. Multiple linear regression analysis was performed to derive quantitative structure activity relationship models which were further evaluated internally as well as ext...

In the present study, a quantitative structure activity relationship (QSAR) has been carried out on a series of 2-methyl and 2-aminobenzimidazole derivatives to identify the lipophilicity requirements for their inhibitory activity against bacteria Sarcina lutea. The tested compounds displayed in vitro antibacterial activity and minimum inhibitory concentration (MIC) was determined for all compo...

Among the multitude of learning algorithms that can be employed for deriving quantitative structure-activity relationships, regression trees have the advantage of being able to handle large data sets, dynamically perform the key feature selection, and yield readily interpretable models. A conventional method of building a regression tree model is recursive partitioning, a fast greedy algorithm ...