Deep learning-driven prediction of drug mechanism of action from large-scale chemical-genetic interaction profiles.

Autor: Liu C; Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB, Canada., Hogan AM; Department of Microbiology, University of Manitoba, Winnipeg, MB, Canada., Sturm H; Department of Chemistry, University of Manitoba, Winnipeg, MB, Canada., Khan MW; Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, MB, Canada., Islam MM; Department of Computer Science, University of Manitoba, Winnipeg, MB, Canada., Rahman ASMZ; Department of Microbiology, University of Manitoba, Winnipeg, MB, Canada., Davis R; Department of Chemistry, University of Manitoba, Winnipeg, MB, Canada., Cardona ST; Department of Microbiology, University of Manitoba, Winnipeg, MB, Canada.; Department of Medical Microbiology & Infectious Diseases, University of Manitoba, Winnipeg, Canada., Hu P; Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB, Canada. pingzhao.hu@umanitoba.ca.; Department of Computer Science, University of Manitoba, Winnipeg, MB, Canada. pingzhao.hu@umanitoba.ca.; Department of Biochemistry and Medical Genetics, University of Manitoba, Room 308, Basic Medical Sciences Building, 745 Bannatyne Avenue, Winnipeg, MB, R3E 0J9, Canada. pingzhao.hu@umanitoba.ca.
Jazyk: angličtina
Zdroj: Journal of cheminformatics [J Cheminform] 2022 Mar 12; Vol. 14 (1), pp. 12. Date of Electronic Publication: 2022 Mar 12.
DOI: 10.1186/s13321-022-00596-6
Abstrakt: Motivation: Chemical-genetic interaction profiling is a genetic approach that quantifies the susceptibility of a set of mutants depleted in specific gene product(s) to a set of chemical compounds. With the recent advances in artificial intelligence, chemical-genetic interaction profiles (CGIPs) can be leveraged to predict mechanism of action of compounds. This can be achieved by using machine learning, where the data from a CGIP is fed into the machine learning platform along with the chemical descriptors to develop a chemogenetically trained model. As small molecules can be considered non-structural data, graph convolutional neural networks, which can learn from the chemical structures directly, can be used to successfully predict molecular properties. Clustering analysis, on the other hand, is a critical approach to get insights into the underlying biological relationships between the gene products in the high-dimensional chemical-genetic data.
Methods and Results: In this study, we proposed a comprehensive framework based on the large-scale chemical-genetics dataset built in Mycobacterium tuberculosis for predicting CGIPs using graph-based deep learning models. Our approach is structured into three parts. First, by matching M. tuberculosis genes with homologous genes in Escherichia coli (E. coli) according to their gene products, we grouped the genes into clusters with distinct biological functions. Second, we employed a directed message passing neural network to predict growth inhibition against M. tuberculosis gene clusters using a collection of 50,000 chemicals with the profile. We compared the performance of different baseline models and implemented multi-label tasks in binary classification frameworks. Lastly, we applied the trained model to an externally curated drug set that had experimental results against M. tuberculosis genes to examine the effectiveness of our method. Overall, we demonstrate that our approach effectively created M. tuberculosis gene clusters, and the trained classifier is able to predict activity against essential M. tuberculosis targets with high accuracy.
Conclusion: This work provides an analytical framework for modeling large-scale chemical-genetic datasets for predicting CGIPs and generating hypothesis about mechanism of action of novel drugs. In addition, this work highlights the importance of graph-based deep neural networks in drug discovery.
(© 2022. The Author(s).)
Databáze: MEDLINE
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