Application of nonlinear and local modeling methods for 3D QSAR study of class I antiarrhythmics

Locally weighted regression and artificial neural networks were employed to find a quantitative relationship between recovery time and molecular struc tures for class I antiarrhythmics. Nonlinear and local models have been bui lt between score vectors of columns of Comparative Molecular Field Analysis (CoMFA) as independent variables and recovery time values as dependent var iables. Other method by applying cheaply computed descriptors invariant to roto-tra nslation with artificial neural networks were also used. Predictive ability of the methods was tested by a separate set of compounds, and the pel perf ormance of both procedures proved to be acceptable, and comparable to CoMFA . This study clearly demonstrates the need and ability of nonlinear algorithm s in building of 3D QSARs. The methods presented here do not assume any par ticular functional form for developing quantitative models between molecula r descriptors and biological activity. (C) 2000 Elsevier Science B.V. All r ights reserved.