Predicting protein network topology clusters from chemical structure using deep learning

Sreenivasan, Akshai P. (author): Uppsala universitet,Institutionen för farmaceutisk biovetenskap,Klinisk kemi

Harrison, Philip J (author): Uppsala universitet,Institutionen för farmaceutisk biovetenskap

Schaal, Wesley, PhD (author): Uppsala universitet,Institutionen för farmaceutisk biovetenskap

Matuszewski, Damian J. (author): Uppsala universitet,Avdelningen för visuell information och interaktion,Bildanalys och människa-datorinteraktion,Science for Life Laboratory, SciLifeLab

Spjuth, Ola, Professor, 1977- (author): Uppsala universitet,Institutionen för farmaceutisk biovetenskap

(creator_code:org_t)

Related links:: https://doi.org/10.1...; show more...; https://uu.diva-port... (primary) (Raw object); https://urn.kb.se/re...; https://doi.org/10.1...; show less...

Abstract Subject headings

Comparing chemical structures to infer protein targets and functions is a common approach, but basing comparisons on chemical similarity alone can be misleading. Here we present a methodology for predicting target protein clusters using deep neural networks. The model is trained on clusters of compounds based on similarities calculated from combined compound-protein and protein-protein interaction data using a network topology approach. We compare several deep learning architectures including both convolutional and recurrent neural networks. The best performing method, the recurrent neural network architecture MolPMoFiT, achieved an F1 score approaching 0.9 on a held-out test set of 8907 compounds. In addition, in-depth analysis on a set of eleven well-studied chemical compounds with known functions showed that predictions were justifiable for all but one of the chemicals. Four of the compounds, similar in their molecular structure but with dissimilarities in their function, revealed advantages of our method compared to using chemical similarity.

By the author/editor: Sreenivasan, Aks ...; Harrison, Philip ...; Schaal, Wesley, ...; Matuszewski, Dam ...; Kultima, Kim; Spjuth, Ola, Pro ...

About the subject

NATURAL SCIENCES: NATURAL SCIENCES; and Computer and Inf ...; and Bioinformatics

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.