The tumor proliferation pathways for each individual patient encompass variations and a successful treatment regime based on targeted drugs necessitates the estimation of the influences of target inhibition on cell viability. In this article, we consider an inference approach to decipher the significant blocks of protein targets and the effect of their inhibition on tumor proliferation. Our framework is based on sequential search and non-linear optimization for estimating the block parameters. The proposed algorithm is tested on extensive synthetic data and provides high accuracy estimates for model parameters. We furthermore evaluated the performance of the framework in presence of noise and were able to achieve high precision cell viability prediction.