A new approach to predicting the biological activity of small molecule pharmaceutics is demonstrated. Structural features of medicinal chemistry building blocks are used as 2-D molecular descriptors. These descriptors include predefined structural features and macrostructures obtained from a supervised process in which features in the core library are reassembled to provide larger features that strongly differentiate the desired biological response variable. Chemical features derived in this manner can serve as predictor variables for diverse modeling algorithms, and application using partial least squares techniques is demonstrated here. Models are presented for inhibition by benzofuran and benzothiophene biphenyl analogues of protein tyrosine phosphatase 1B (PTP1B), a target for insulin-resistant disease states. Results are compared to models for PTP1B inhibitors available in the literature based on CoMFA-related techniques and 3-D molecular descriptors.