Accelerating drug repurposing for COVID-19 via modeling drug mechanism
of action with large scale gene-expression profiles
- URL: http://arxiv.org/abs/2005.07567v2
- Date: Tue, 5 Oct 2021 15:31:18 GMT
- Title: Accelerating drug repurposing for COVID-19 via modeling drug mechanism
of action with large scale gene-expression profiles
- Authors: Lu Han, G.C. Shan, B.F. Chu, H.Y. Wang, Z.J. Wang, S.Q. Gao, W.X. Zhou
- Abstract summary: The novel coronavirus disease, named COVID-19, emerged in China in December 2019, and has rapidly spread around the world.
The development of methods for identifying drug uses based on phenotypic data can improve the efficiency of drug development.
This work reported one state-of-the-art machine learning method to identify drug uses based on the cell image features of 1024 drugs generated in the LINCS program.
- Score: 2.524526956420465
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The novel coronavirus disease, named COVID-19, emerged in China in December
2019, and has rapidly spread around the world. It is clearly urgent to fight
COVID-19 at global scale. The development of methods for identifying drug uses
based on phenotypic data can improve the efficiency of drug development.
However, there are still many difficulties in identifying drug applications
based on cell picture data. This work reported one state-of-the-art machine
learning method to identify drug uses based on the cell image features of 1024
drugs generated in the LINCS program. Because the multi-dimensional features of
the image are affected by non-experimental factors, the characteristics of
similar drugs vary greatly, and the current sample number is not enough to use
deep learning and other methods are used for learning optimization. As a
consequence, this study is based on the supervised ITML algorithm to convert
the characteristics of drugs. The results show that the characteristics of ITML
conversion are more conducive to the recognition of drug functions. The
analysis of feature conversion shows that different features play important
roles in identifying different drug functions. For the current COVID-19,
Chloroquine and Hydroxychloroquine achieve antiviral effects by inhibiting
endocytosis, etc., and were classified to the same community. And Clomiphene in
the same community inibited the entry of Ebola Virus, indicated a similar MoAs
that could be reflected by cell image.
Related papers
- Equivariant Graph Attention Networks with Structural Motifs for Predicting Cell Line-Specific Synergistic Drug Combinations [0.0]
Cancer is the second leading cause of death, with chemotherapy as one of the primary forms of treatment.
Current methods of drug combination screening, such as in vivo and in vitro, are inefficient due to stark time and monetary costs.
I employ a geometric deep-learning model utilizing a graph attention network that is equivariant to 3D rotations, translations, and reflections with structural motifs.
arXiv Detail & Related papers (2024-11-07T14:29:05Z) - Regressor-free Molecule Generation to Support Drug Response Prediction [83.25894107956735]
Conditional generation based on the target IC50 score can obtain a more effective sampling space.
Regressor-free guidance combines a diffusion model's score estimation with a regression controller model's gradient based on number labels.
arXiv Detail & Related papers (2024-05-23T13:22:17Z) - drGAT: Attention-Guided Gene Assessment of Drug Response Utilizing a Drug-Cell-Gene Heterogeneous Network [9.637695046701493]
drGAT is a graph deep learning model that can predict sensitivity to drugs.
drGAT has superior performance over existing models, achieving 78% accuracy and 76% F1 score for 269 DNA-damaging compounds.
Our method can be used to accurately predict sensitivity to drugs and may be useful in the identification of biomarkers relating to the treatment of cancer patients.
arXiv Detail & Related papers (2024-05-14T22:16:52Z) - Emerging Opportunities of Using Large Language Models for Translation
Between Drug Molecules and Indications [6.832024637226738]
We propose a new task, which is the translation between drug molecules and corresponding indications.
The creation of molecules from indications, or vice versa, will allow for more efficient targeting of diseases.
arXiv Detail & Related papers (2024-02-14T21:33:13Z) - Drug Synergistic Combinations Predictions via Large-Scale Pre-Training
and Graph Structure Learning [82.93806087715507]
Drug combination therapy is a well-established strategy for disease treatment with better effectiveness and less safety degradation.
Deep learning models have emerged as an efficient way to discover synergistic combinations.
Our framework achieves state-of-the-art results in comparison with other deep learning-based methods.
arXiv Detail & Related papers (2023-01-14T15:07:43Z) - SSM-DTA: Breaking the Barriers of Data Scarcity in Drug-Target Affinity
Prediction [127.43571146741984]
Drug-Target Affinity (DTA) is of vital importance in early-stage drug discovery.
wet experiments remain the most reliable method, but they are time-consuming and resource-intensive.
Existing methods have primarily focused on developing techniques based on the available DTA data, without adequately addressing the data scarcity issue.
We present the SSM-DTA framework, which incorporates three simple yet highly effective strategies.
arXiv Detail & Related papers (2022-06-20T14:53:25Z) - Self-supervised learning for analysis of temporal and morphological drug
effects in cancer cell imaging data [0.0]
We train a convolutional autoencoder on 1M images dataset with random augmentations and multi-crops to use as feature extractor.
We use distance-based analysis and dynamic time warping to cluster temporal patterns of 31 drugs.
We increase top-3 classification accuracy by 8% on average and mine examples of morphological feature importance maps.
arXiv Detail & Related papers (2022-03-07T14:48:13Z) - Deep learning for drug repurposing: methods, databases, and applications [54.08583498324774]
Repurposing existing drugs for new therapies is an attractive solution that accelerates drug development at reduced experimental costs.
In this review, we introduce guidelines on how to utilize deep learning methodologies and tools for drug repurposing.
arXiv Detail & Related papers (2022-02-08T09:42:08Z) - A k-mer Based Approach for SARS-CoV-2 Variant Identification [55.78588835407174]
We show that preserving the order of the amino acids helps the underlying classifiers to achieve better performance.
We also show the importance of the different amino acids which play a key role in identifying variants and how they coincide with those reported by the USA's Centers for Disease Control and Prevention (CDC)
arXiv Detail & Related papers (2021-08-07T15:08:15Z) - Genetic Constrained Graph Variational Autoencoder for COVID-19 Drug
Discovery [0.0]
We propose a new model called Genetic Constrained Graph Variational Autoencoder (GCGVAE) to solve this problem.
We trained our model based on the data of various viruses' protein structure, including that of the SARS, HIV, Hep3, and MERS, and used it to generate possible drugs for SARS-CoV-2.
Our generated molecules have great effectiveness in inhibiting SARS-CoV-2.
arXiv Detail & Related papers (2021-04-23T16:10:15Z) - Classification of COVID-19 in CT Scans using Multi-Source Transfer
Learning [91.3755431537592]
We propose the use of Multi-Source Transfer Learning to improve upon traditional Transfer Learning for the classification of COVID-19 from CT scans.
With our multi-source fine-tuning approach, our models outperformed baseline models fine-tuned with ImageNet.
Our best performing model was able to achieve an accuracy of 0.893 and a Recall score of 0.897, outperforming its baseline Recall score by 9.3%.
arXiv Detail & Related papers (2020-09-22T11:53:06Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.