A distributed and privatized framework for drug-target interaction prediction

Drug-Target interaction prediction has significantly sped up the process of drug discovery. However, existing studies focused on a centralized analytic framework, which may have many limitations when applying on the modern distributed data environments. In this paper, we present a distributed and privatized framework for drug-target interaction prediction, which assumes distributed repositories are stored on and analyzed by local computers which could exchange privatized information for improving their own prediction performance. Under this framework, we propose a ‘conquer-and-divide’ style prediction approach based on the recently introduced matrix factorization technique, and demonstrated its effectiveness on real-world cheminformatics data sets.