In this paper, we present Spec2Mol, an end-to-end DL architecture for translating MS/MS spectra to molecular structures. Spec2Mol is intended for recommending molecular structures that can explain observed MS/MS spectra. We represent molecular structures as sequences using the SMILES notation and MS/MS spectra as vectors of fragment intensities. Spec2Mol consists of an encoder, that learns an embedding for the MS/MS spectrum, and a decoder that generates the SMILES sequences of the recommended chemical molecules. Due to the limited amount of available spectra data our approach is based on unsupervised pre-training on a large dataset of unlabeled molecules. In particular, we pre-trained the decoder as part of an auto-encoder (AE) architecture which is trained to reconstruct a molecule through its SMILES sequence. The encoder is subsequently trained such that the spectra embeddings match the embeddings that the AE has learnt. The data used to develop and evaluate the model, the architecture of Spec2Mol, as well as, the evaluation of the model are described in the following sections.