ReSimNet: drug response similarity prediction using Siamese neural networks
Autor: | Donghyeon Park, Hwisang Jeon, Yonghwa Choi, Miyoung Ko, Minji Jeon, Jinhyuk Lee, Sunkyu Kim, Aik Choon Tan, Jaewoo Kang |
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Rok vydání: | 2018 |
Předmět: |
Statistics and Probability
Chemical compound Computer science Biochemistry Machine Learning 03 medical and health sciences chemistry.chemical_compound 0302 clinical medicine Similarity (network science) Drug Discovery medicine Molecular Biology 030304 developmental biology Complement (set theory) 0303 health sciences Artificial neural network business.industry Drug discovery Pattern recognition Computer Science Applications Computational Mathematics Computational Theory and Mathematics chemistry Mechanism of action 030220 oncology & carcinogenesis Embedding Artificial intelligence Neural Networks Computer medicine.symptom business Software |
Zdroj: | Bioinformatics (Oxford, England). 35(24) |
ISSN: | 1367-4811 |
Popis: | Motivation Traditional drug discovery approaches identify a target for a disease and find a compound that binds to the target. In this approach, structures of compounds are considered as the most important features because it is assumed that similar structures will bind to the same target. Therefore, structural analogs of the drugs that bind to the target are selected as drug candidates. However, even though compounds are not structural analogs, they may achieve the desired response. A new drug discovery method based on drug response, which can complement the structure-based methods, is needed. Results We implemented Siamese neural networks called ReSimNet that take as input two chemical compounds and predicts the CMap score of the two compounds, which we use to measure the transcriptional response similarity of the two compounds. ReSimNet learns the embedding vector of a chemical compound in a transcriptional response space. ReSimNet is trained to minimize the difference between the cosine similarity of the embedding vectors of the two compounds and the CMap score of the two compounds. ReSimNet can find pairs of compounds that are similar in response even though they may have dissimilar structures. In our quantitative evaluation, ReSimNet outperformed the baseline machine learning models. The ReSimNet ensemble model achieves a Pearson correlation of 0.518 and a precision@1% of 0.989. In addition, in the qualitative analysis, we tested ReSimNet on the ZINC15 database and showed that ReSimNet successfully identifies chemical compounds that are relevant to a prototype drug whose mechanism of action is known. Availability and implementation The source code and the pre-trained weights of ReSimNet are available at https://github.com/dmis-lab/ReSimNet. Supplementary information Supplementary data are available at Bioinformatics online. |
Databáze: | OpenAIRE |
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