DeepSIBA: Chemical Structure-based Inference of Biological Alterations
This work addresses the challenge of in-silico compound screening in early drug discovery, offering a tool to predict biological alterations from chemical structures, which is incremental as it builds on existing graph-based deep learning methods.
The authors tackled the problem of predicting biological effects from chemical structures for drug discovery by developing a deep learning model that maps compound differences to biological alterations using Siamese Graph Convolutional Neural Networks. The model achieved high precision in identifying structurally dissimilar compounds with similar biological effects and provided reliable predictions for novel compounds using uncertainty estimation.
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.