A new drug design method, the multiple field three-dimensional quantitative structure-activity relationship (MF-3D-QSAR), is proposed. It is a combination and development of classical 2D-QSAR and traditional 3D-QSAR. In addition to the electrostatic and van der Waals potentials, more potential fields (such as lipophilic potential, hydrogen bonding potential, and nonthermodynamic factors) are in...

A Kohonen self-organizing map (SOM) is used to classify a data set consisting of dihydrofolate reductase inhibitors with the help of an external set of Dragon descriptors. The resultant classification is used to generate training, cross-validation (CV) and prediction sets for QSAR modeling using the ADAPT methodology. The results are compared to those of QSAR models generated using sets created...

The data is obtained from exploring the modulatory activities of bioflavonoids on P-glycoprotein function by ligand-based approaches. Multivariate Linear-QSAR models for predicting the induced/inhibitory activities of the flavonoids were created. Molecular descriptors were initially used as independent variables and a dependent variable was expressed as pFAR. The variables were then used in MLR...

Insubria QSAR PaDEL-Descriptor model for prediction of Endocrine Disruptors Chemicals (EDC) Estrogen Receptor (ER)-binding affinity. 1.2.Other related models: J.Li and P.Gramatica. The importance of molecular structures, endpoints’ values, and predictivity parameters in QSAR research: QSAR analysis of a series of estrogen receptor binders, Mol. Divers. 14, 2010, pp 687-696. [8] 1.3.Software cod...

In this study, we developed nano-mixture QSAR models using molecular dynamic (MD) descriptors to predict the toxicity of MONPs A. fischeri .

A general feeling of disillusionment with QSAR has settled across the modeling community in recent years. Most practitioners seem to agree that QSAR has not fulfilled the expectations set for its ability to predict biological activity. Among the possible reasons that have been proposed recently for this disappointment are chance correlation, rough response surfaces, incorrect functional forms, ...

Quantitative structure activity relationships (QSARs) are functions that predict bioactivity from compound structure. Although almost every form of statistical and machine learning method has been applied to learning QSARs, there is no single best way of learning QSARs. Therefore, currently the QSAR scientist has little to guide her/him on which QSAR approach to choose for a specific problem. T...

Histone deacetylase 2 is a promising target for drug intervention and its inhibitors are useful in treating cancer. QSAR (2D and 3D) studies were performed on a series of N-(2-Aminophenyl)-Benzamide derivatives using Cerius2 software (accelrys). QSAR study performed on 25 analogues of which 21 were used in the training set and the rest 4 considered for the test set. 2DQSAR study performed using...

The quantum chemical and structure-based technique heuristic molecular lipophilicity potential (HMLP) is used in the liver alcohol dehydrogenase (LADH) study of molecular family pyrazole and derivatives. The molecular lipophilic index LM, molecular hydrophilic index HM, lipophilic indices lss, and hydrophilic indices hss of the substitutes (fragments), and atomic lipophilicity indices las are c...

Considerable attention has been given on the search for novel anticancer drugs with respect to the disease sequelae on human health and well-being. Triazole is considered to be an attractive scaffold possessing diverse biological activities. Structural modification on the privileged structures is noted as an effective strategy towards successful design and development of novel drugs. The quanti...