Drug resistance is one of the fundamental challenges in modern medicine. Using combinations of drugs is an effective solution to counter drug resistance as is harder to develop resistance to multiple drugs simultaneously. Finding the correct dosage for each drug in the combination remains to be a challenging task. Testing all possible drug-drug combinations on various cell lines for different dosages in wet-lab experiments is infeasible since there are many combinations of drugs as well as their dosages yet the drugs and the cell lines are limited in availability and each wet-lab test is costly and time-consuming. Efficient and accurate in silico prediction methods are surely needed. Here we present a novel computational method, \(PartialFibers\) , to address this challenge. Unlike existing prediction methods \(PartialFibers\) takes advantage of the distribution of the missing drug-drug interactions and effectively predicts the dosage of a drug in the combination. Our results on real datasets demonstrate that \(PartialFibers\) is more flexible, scalable, and achieves higher accuracy in less time than the state of the art algorithms.