A combination of Molecular Dynamics (MD) simulations and docking calculations was employed to model and predict polymer-drug interactions in self-assembled nanoparticles consisting of ABA-type triblock copolymers, where A-blocks are poly(ethylene glycol) units and B-blocks are low molecular weight tyrosine-derived polyarylates. This new computational approach was tested on three representative model compounds: nutraceutical curcumin, anti-cancer drug paclitaxel and pre-hormone vitamin D3. Based on this methodology, the calculated binding energies of polymer-drug complexes can be correlated with maximum drug loading determined experimentally. Furthermore, the modeling results provide an enhanced understanding of polymer-drug interactions, revealing subtle structural features that can significantly affect the effectiveness of drug loading (as demonstrated for a fourth tested compound, anticancer drug camptothecin). The present study suggests that computational calculations of polymer-drug pairs hold the potential of becoming a powerful prescreening tool in the process of discovery, development and optimization of new drug delivery systems, reducing both the time and the cost of the process.