Autoencoder-based drug synergy framework for malignant diseases
Pooja Rani1, Kamlesh Dutta1, Vijay Kumar2
1Computer Science and Engineering Department, National Institute of Technology, Hamirpur, HP, 177005, India.
Abstract:
Drug combination emerges as a viable option for the treatment of malignant diseases. Drug combination outperforms monotherapy by improving therapeutic efficacy, reducing toxicity, and overcoming drug resistance. To find viable drug combinations it is difficult to traverse empirically because of enormous combinational space. Machine learning and deep learning approaches are used to uncover novel synergistic drug combinations in enormous combinational space. Here, AESyn, a novel autoencoder-based drug synergy framework for malignant diseases using a bag of words encoding is proposed. The bag of word encoding technique is used to extract drug-targeted genes. The framework utilized screening data from NCI-ALMANAC, and O'Neil datasets. Autoencoders take drug embeddings with drug-targeted genes as input for processing. The autoencoder in the proposed framework is used to extract drug features. The proposed framework is evaluated on classification and regression metrics. The performance of the proposed framework is compared with existing methods of drug synergy. According to the findings, the proposed framework achieved high performance with an accuracy of 95%, AUROC of 94.2%, and MAPE of 7.2. The autoencoder-based framework for malignant diseases using an encoding technique provides a stable, order-independent drug synergy prediction.
Insights
This study introduces AESyn, an autoencoder-based framework that accurately predicts synergistic drug combinations for cancer treatment. It efficiently navigates vast drug spaces, outperforming existing methods for improved cancer therapies.
Area of Science:
- Computational biology
- Bioinformatics
- Machine learning in drug discovery
Background:
- Drug combinations offer improved efficacy, reduced toxicity, and overcome resistance in treating malignant diseases compared to monotherapy.
- The empirical exploration of potential drug combinations is challenging due to the vast combinatorial space.
- Machine learning and deep learning methods are increasingly employed to identify synergistic drug combinations within large datasets.
Purpose of the Study:
- To propose AESyn, a novel autoencoder-based framework for predicting drug synergy in malignant diseases.
- To utilize a bag-of-words encoding technique for extracting drug-targeted genes and drug features.
- To evaluate the framework's performance using classification and regression metrics and compare it with existing methods.
Main Methods:
- Developed AESyn, an autoencoder-based framework utilizing bag-of-words encoding to represent drug-targeted genes.
- Inputted drug embeddings and drug-targeted genes into autoencoders for feature extraction.
- Trained and validated the framework using screening data from the NCI-ALMANAC and O'Neil datasets.
- Evaluated performance using classification and regression metrics, including accuracy, AUROC, and MAPE.
Main Results:
- The proposed AESyn framework achieved high predictive performance.
- Achieved an accuracy of 95% and an Area Under the Receiver Operating Characteristic curve (AUROC) of 94.2%.
- Demonstrated a Mean Absolute Percentage Error (MAPE) of 7.2, indicating precise regression predictions.
Conclusions:
- The autoencoder-based AESyn framework provides a stable and order-independent method for predicting drug synergy in malignant diseases.
- The framework effectively extracts drug features and predicts synergistic combinations, offering a promising computational approach.
- AESyn demonstrates superior performance compared to existing methods, paving the way for more efficient drug discovery in oncology.
Related Concept Videos
02:50Combination Therapies and Personalized Medicine
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
01:27Combined Effects of Drugs: Synergism
Such synergistic combinations...
01:30Combined Effects of Drugs: Antagonism
The most common type is receptor antagonism, where one drug acts as an antagonist to block the effects of another drug by...
01:14Agonism and Antagonism: Quantification
To quantify these effects, researchers use a dose-response curve, which provides valuable information about the potency and efficacy of a drug. Potency refers to...