The olfactory system employs an ensemble of odorant receptors (ORs) to sense odorants and to derive olfactory percepts. We trained artificial neural networks to represent the chemical space of odorants and used this representation to predict human olfactory percepts. We hypothesized that ORs may be considered 3D convolutional filters that extract molecular features and, as such, can be trained using machine learning methods. First, we trained a convolutional autoencoder, called DeepNose, to deduce a low-dimensional representation of odorant molecules which were represented by their 3D spatial structure. Next, we tested the ability of DeepNose features in predicting physical properties and odorant percepts based on 3D molecular structure alone. We found that, despite the lack of human expertise, DeepNose features often outperformed molecular descriptors used in computational chemistry in predicting both physical properties and human perceptions. We propose that DeepNose network can extract {\it de novo} chemical features predictive of various bioactivities and can help understand the factors influencing the composition of ORs ensemble.