  Predicting whether a chemical structure shares a desired biological effect
can have a significant impact for in-silico compound screening in early drug
discovery. In this study, we developed a deep learning model where compound
structures are represented as graphs and then linked to their biological
footprint. To make this complex problem computationally tractable, compound
differences were mapped to biological effect alterations using Siamese Graph
Convolutional Neural Networks. The proposed model was able to learn new
representations from chemical structures and identify structurally dissimilar
compounds that affect similar biological processes with high precision.
Additionally, by utilizing deep ensembles to estimate uncertainty, we were able
to provide reliable and accurate predictions for chemical structures that are
very different from the ones used during training. Finally, we present a novel
inference approach, where the trained models are used to estimate the signaling
pathways affected by a compound perturbation in a specific cell line, using
only its chemical structure as input. As a use case, this approach was used to
infer signaling pathways affected by FDA-approved anticancer drugs.
