Modeling drug mechanism of action with large scale gene-expression profiles using GPAR, an artificial intelligence platform

Gao, Shengqiao; Han, Lu; Luo, Dan; Liu, Gang; Xiao, Zhiyong; Shan, Guangcun; Zhang, Yongxiang; Zhou, Wenxia

doi:10.1186/s12859-020-03915-6

Cited by 27 publications

(19 citation statements)

References 41 publications

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“…The prediction of a compound’s MoA from biological response data has gained considerable attraction in the machine learning community [31,32]. This is evident by the recent release of the CTD 2 Pancancer Drug Activity DREAM Challenge, which tasked the community to predict a compound’s MoA based on post-transcriptional and cell viability data [32].…”

Section: Discussionmentioning

confidence: 99%

DeepSNEM: Deep Signaling Network Embeddings for compound mechanism of action identification

Fotis

Alevizos

Meimetis

et al. 2021

Preprint

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MotivationThe analysis and comparison of compounds’ transcriptomic signatures can help elucidate a compound’s Mechanism of Action (MoA) in a biological system. In order to take into account the complexity of the biological system, several computational methods have been developed that utilize prior knowledge of molecular interactions to create a signaling network representation that best explains the compound’s effect. However, due to their complex structure, large scale datasets of compound-induced signaling networks and methods specifically tailored to their analysis and comparison are very limited. Our goal is to develop graph deep learning models that are optimized to transform compound-induced signaling networks into high-dimensional representations and investigate their relationship with their respective MoAs.ResultsWe created a new dataset of compound-induced signaling networks by applying the CARNIVAL network creation pipeline on the gene expression profiles of the CMap dataset. Furthermore, we developed a novel unsupervised graph deep learning pipeline, called deepSNEM, to encode the information in the compound-induced signaling networks in fixed-length high-dimensional representations. The core of deepSNEM is a graph transformer network, trained to maximize the mutual information between whole-graph and sub-graph representations that belong to similar perturbations. By clustering the deepSNEM embeddings, using the k-means algorithm, we were able to identify distinct clusters that are significantly enriched for mTOR, topoisomerase, HDAC and protein synthesis inhibitors respectively. Additionally, we developed a subgraph importance pipeline and identified important nodes and subgraphs that were found to be directly related to the most prevalent MoA of the assigned cluster. As a use case, deepSNEM was applied on compounds’ gene expression profiles from various experimental platforms (MicroArrays and RNA sequencing) and the results indicate that correct hypotheses can be generated regarding their MoA.Availability and ImplementationThe source code and pre-trained deepSNEM models are available at https://github.com/BioSysLab/deepSNEM.ContactEmail for correspondence: leo@mail.ntua.gr.Supplementary informationAccompanying supplementary material are available online.

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Section: Discussionmentioning

confidence: 99%

DeepSNEM: Deep Signaling Network Embeddings for compound mechanism of action identification

Fotis

Alevizos

Meimetis

et al. 2021

Preprint

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“…212 This highlights the need for additional compound representation beyond chemical structure. Examples of such descriptors are the expression response of the 978 LINCS ''landmark genes'' 213 or cell morphology changes in the form of microscopy images or calculated features. 214 After the selection of appropriate compound features, supervised ML is carried out by training a model (fitting a function linking the descriptors to the end-point) and then testing it on a held-out test set to understand how well the model performs with new 'unseen' data, with an optional validation set used to optimise various hyperparameters of the models.…”

Section: Unsupervised Machine Learningmentioning

confidence: 99%

Computational analyses of mechanism of action (MoA): data, methods and integration

2022

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“…The machine learning method is an effective way for revealing drug mechanisms of action (MOAs). However, due to the lack of code-free and user friendly applications, it is not easy for pharmacologists to model MOA by this method (Gao et al, 2021;Hamid et al, 2019).…”

Section: In-silicomentioning

confidence: 99%

To explore the pharmacological mechanism of action using digital twin

al.¹

2022

Int. j. adv. appl. sci.

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With the advent of medical technology and science, the number of animals used in research has increased. For decades, the use of animals in research and product testing has been a point of conflict. Experts and pharmaceutical manufacturers are harming animals worldwide during laboratory research. Animals have also played a significant role in the advancement of science; animal testing has enabled the discovery of various novel drugs. The misery, suffering, and deaths of animals are not worth the potential human benefits. As a result, animals must not be exploited in research to assess the drug mechanism of action (MOA). Apart from the ethical concern, animal testing has a few more downsides, including the requirement for skilled labor, lengthy processes, and cost. Because it is critical to investigate adverse effects and toxicities in the development of potentially viable drugs. Assessment of each target will consume the range of resources as well as disturb living nature. As the digital twin works in an autonomous virtual world without influencing the physical structure and biological system. Our proposed framework suggests that the digital twin is a great reliable model of the physical system that will be beneficial in assessing the possible MOA prior to time without harming animals. The study describes the creation of a digital twin to combine the information and knowledge obtained by studying the different drug targets and diseases. Mechanism of Action using Digital twin (MOA-DT) will enable the experts to use an innovative approach without physical testing to save animals, time, and resources. DT reflects and simulates the actual drug and its relationships with its target, however presenting a more accurate depiction of the drug, which leads to maximize efficacy and decrease the toxicity of a drug. In conclusion, it has been shown that drug discovery and development can be safe, effective, and economical in no time through the combination of the digital and physical models of a pharmaceutical as compared to experimental animals.

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Modeling drug mechanism of action with large scale gene-expression profiles using GPAR, an artificial intelligence platform

Cited by 27 publications

References 41 publications

DeepSNEM: Deep Signaling Network Embeddings for compound mechanism of action identification

DeepSNEM: Deep Signaling Network Embeddings for compound mechanism of action identification

Computational analyses of mechanism of action (MoA): data, methods and integration

To explore the pharmacological mechanism of action using digital twin

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