Recent advances have provided a working interactome map for the human malaria parasite Plasmodium falciparum. The aforementioned map, generated from genome-scale analyses, has provided a basis for proteomic studies of the parasite; however, such large-scale approaches commonly suffer from undersampling and lack of coverage. The current map bears no exception, containing only one-quarter of the organism’s proteins. Inspired by the needs of the current map and the wealth of bioinformatics data, we assembled a map of 19 979 interactions among 2321 proteins in P. falciparum. The resultant map was generated by computationally inferring protein-protein interactions from evolutionarily conserved protein interactions, underlying domain interactions, and experimental observations. To compile this information into a repository of meaningful data, we assessed interaction quality by applying a logistic regression method, which correlated the presence of an interaction with relevant cellular parameters. Interestingly, it was found that sub-networks from different sources are quite dissimilar in their topologies and overlap to a very small extent. Applying Markov clustering, we observe a typical cluster composition, featuring common cellular functions that were previously reported absent, making this map a valuable resource for understanding the biology of this organism.