28 research outputs found
Defining a novel k-nearest neighbours approach to assess the applicability domain of a QSAR model for reliable predictions
No full textPrediction of the datasets modelability for the building of QSAR classification models by means of the centroid based rivality index
No full textApplicability Domain Analysis (ADAN): A Robust Method for Assessing the Reliability of Drug Property Predictions
No full textMolecular docking for predictive toxicology
No full textMolecular docking is an in silico method widely applied in drug discovery programs to predict the binding mode of a given molecule interacting with a specific biological target. This computational technique is today emerging also in the field of predictive toxicology for regulatory purposes, being for instance successfully applied to develop classification models for the prediction of the endocrine disruptor potential of chemicals. Herein, we describe the protocol for adapting molecular docking to the purposes of predictive toxicology
DFT-Based QSAR Modelling of Inhibitory Activity of Coumarins and Sulfocoumarins on Carbonic Anhydrase (CA) Isoforms (CA I and CA II)
No full textMolecular docking and QSAR analysis of naphthyridone derivatives as ATAD2 bromodomain inhibitors: application of CoMFA, LS-SVM, and RBF neural network
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