Three-Dimensional QSAR Using the k-Nearest Neighbor Method and Its Interpretation

Citations Over TimeTop 20% of 2005 papers

Abstract

In this paper we report a novel three-dimensional QSAR approach, kNN-MFA, developed based on principles of the k-nearest neighbor method combined with various variable selection procedures. The kNN-MFA approach was used to generate models for three different data sets and predict the activity of test molecules through each of these models. The three data sets used were the standard steroid benchmark, an antiinflammatory and an anticancerous data set. The study resulted in kNN-MFA models having better statistical parameters than the reported CoMFA models for all the three data sets. It was also found that stochastic methods generate better models resulting in more accurate predictions as compared to stepwise forward selection procedures. Thus, kNN-MFA method represents a good alternative to CoMFA-like methods.

Related Papers

• → A Novel Automated Lazy Learning QSAR (ALL-QSAR) Approach: Method Development, Applications, and Virtual Screening of Chemical Databases Using Validated ALL-QSAR Models(2006)243 cited
• Qsar Modelling of Some Anticancer Pgi50 Activity on Hl-60 Cell Lines(2016)
• → Quantitative structure–activity relationship (QSAR) of artemisinin: the development of predictive in vivo antimalarial activity models(2009)12 cited
• → QSAR studies on imidazothienopyrazines as IKK‐β inhibitors: from 2D to 3D(2009)5 cited
• → The average numbers of outliers over groups of various splits into training and test sets: A criterion of the reliability of a QSPR? A case of water solubility(2012)4 cited