Standard supervised classification methods make the assumption that the training data is fully annotated thus requiring an a-priory labelling process which is both costly and time-consuming. To relax this requirement, many different flavors of weakly supervised learning have been proposed. Among weakly supervised learning strategies, Positive Unlabelled learning (PUL) is gaining attention from the research community due to the wide spectrum of applications it can fit. However, the majority of research studies related to PUL only consider binary classification tasks while real-world applications commonly involve multiple categories. To deal with this limitation, Multi-Positive Unlabelled learning (MPUL) has been recently introduced to learn from examples labelled with multiple positive labels and a single unknown negative label. Up to today, only a limited number of research works were proposed to cope with this more general setting. In this paper, we propose a new MPUL framework based on deep learning strategies. Our framework, named ProtoMPUL (Prototype based Multi-Positive and Unlabelled Learning), combines metric learning and clustering strategies to model the set of positive classes as well as to characterize the unknown negative one. Experimental evaluations on real-world benchmarks considering recent MPUL competitors demonstrates that the proposed framework achieves state-of-the-art performances, thus supporting the validity of the proposed approach.