2D autocorrelation, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were undertaken for a series of pyrido[2,3-d]pyrimidin-7-ones to correlate cyclin-dependent kinase (CDK) cyclin D/CDK4 inhibition with 2D and 3D structural properties of 60 known compounds. QSAR models with considerable internal as well as external predictive ability were obtained. The relevant 2D autocorrelation descriptors for modeling CDK4/D inhibitory activity were selected by linear and nonlinear genetic algorithms (GAs) using multiple linear regression (MLR) and Bayesian-regularized genetic neural network (BRGNN) approaches, respectively. Both models showed good predictive statistics; but BRGNN model enables better external predictions. A weight-based input ranking scheme and Kohonen self-organized maps (SOMs) were carried out to interpret the final net weights. The 2D autocorrelation space brings different descriptors for CDK4/D inhibition, and suggests the atomic properties relevant for the inhibitors to interact with CDK4/D active site. CoMFA and CoMSIA analyses were developed with a focus on interpretative ability using coefficient contour maps. CoMSIA produced significantly better results. The results indicate a strong correlation between the inhibitory activity of the modeled compounds and the electrostatic and hydrophobic fields around them.