Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data
Open accessAbstract
Deep learning is rapidly advancing many areas of science and technology with multiple success stories in image, text, voice and video recognition, robotics, and autonomous driving. In this paper we demonstrate how deep neural networks (DNN) trained on large transcriptional response data sets can classify various drugs to therapeutic categories solely based on their transcriptional profiles. We used the perturbation samples of 678 drugs across A549, MCF-7, and PC-3 cell lines from the LINCS Project and linked those to 12 therapeutic use categories derived from MeSH. To train the DNN, we utilized both gene level transcriptomic data and transcriptomic data processed using a pathway activation scoring algorithm, for a pooled data set of samples perturbed with different concentrations of the drug for 6 and 24 hours. In both pathway and gene level classification, DNN achieved high classification accuracy and convincingly outperformed the support vector machine (SVM) model on every multiclass classification problem, however, models based on pathway level data performed significantly better. For the first time we demonstrate a deep learning neural net trained on transcriptomic data to recognize pharmacological properties of multiple drugs across different biological systems and conditions. We also propose using deep neural net confusion matrices for drug repositioning. This work is a proof of principle for applying deep learning to drug discovery and development.
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A DNN trained on pathway scores outperformed a DNN trained on the set of landmark genes and achieved an F1 score of 0.701 for the three drug pharmacological classes.
“DNN trained on the pathway scores outperformed DNN trained on the set of landmark genes and achieved the F1 score, the weighted average of precision and recall, of 0.701 for the three drug pharmacological classes.”
From Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare (2018) · cites this paper as [33]
Signaling pathway-based dimensionality reduction allowed for more robust performance on the validation set, while classifiers trained on gene expression data demonstrated a significant decrease in predictive accuracy on the validation set compared to training set performance.
“signaling pathway-based dimensionality reduction allowed for the more robust performance on the validation set, while classifiers trained on gene expression data demonstrated a significant decrease in predictive accuracy on the validation set compared to the training set performance.”
From Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare (2018) · cites this paper as [33]
DNNs have been used in pattern recognition and machine learning, sparking their use in pharmacology and drug discovery and becoming a source for numerous recent reviews.
“DNNs have been used in pattern recognition and machine learning25, sparking their use in pharmacology and drug discovery26 and becoming a source for numerous recent reviews12.”
From Exploiting machine learning for end-to-end drug discovery and development (2019) · cites this paper as [26]
The increasing number of drug-repositioning investigations suggests that reuse of medications for common, rare or orphan diseases is a viable approach.
“The increasing number of drug-repositioning investigations, suggests that the reuse of medications for common, rare or orphan diseases is a viable approach26.”
From Exploiting machine learning for end-to-end drug discovery and development (2019) · cites this paper as [26]
Aliper et al. combined processed gene expression data with DNNs to identify pharmacological properties of multiple drugs under different biological systems and conditions.
“By scoring pathway activation and regarding “landmark genes” as new features to perform dimensionality reduction, Aliper et al. combined processed gene expression data with DNNs to identify the pharmacological properties of multiple drugs under different biological systems and conditions [156].”
From Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis (2018) · cites this paper as [156]
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- Authors
- Aliper, A., Plis, S., Artemov, A., Ulloa, A., Mamoshina, P., & Zhavoronkov, A.
- Affiliations
- Johns Hopkins University (US)
- Mind Research Network (US)
- Corresponding author
- Aliper, Alexander
- Zhavoronkov, Alex
- Venue
- Molecular Pharmaceutics
- Publisher
- American Chemical Society (ACS)
- Published
- 2016-05-20
- Volume
- 13
- Issue
- 7
- Pages
- 2524-2530
- ISSN
- 1543-8384 (ISSN-L)
- 1543-8392
- PMC
- PMC4965264
- OpenAlex
- W2397757171
- Cited by
- 610
- References
- 40
- Citations by year
- 2026: 33 · 2025: 66 · 2024: 92 · 2023: 78 · 2022: 63 · 2021: 71 · 2020: 54 · 2019: 64 · 2018: 61 · 2017: 18 · 2016: 10
- FWCI
- 46.36
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Molecular Pharmaceutics (publishedVersion, other-oa)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Nvidia
- National Institute of Biomedical Imaging and Bioengineering (R01EB020407, R01EB005846, R01EB006841)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- drug repurposing, transcriptomic data, deep neural network, confusion matrix, drug discovery
- MeSH terms
- A549 Cells, Algorithms, Humans, Neural Networks, Computer, Drug Discovery, Drug Repositioning, Transcriptome, Support Vector Machine, MCF-7 Cells
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