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DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networks
- Source :
- Bioinformatics
- Publication Year :
- 2018
- Publisher :
- Cold Spring Harbor Laboratory, 2018.
-
Abstract
- Motivation: Drug discovery demands rapid quantification of compound-protein interaction (CPI). However, there is a lack of methods that can predict compound-protein affinity from sequences alone with high applicability, accuracy, and interpretability. Results: We present a seamless integration of domain knowledges and learning-based approaches. Under novel representations of structurally-annotated protein sequences, a semi-supervised deep learning model that unifies recurrent and convolutional neural networks has been proposed to exploit both unlabeled and labeled data, for jointly encoding molecular representations and predicting affinities. Our representations and models outperform conventional options in achieving relative error in IC$_{50}$ within 5-fold for test cases and 20-fold for protein classes not included for training. Performances for new protein classes with few labeled data are further improved by transfer learning. Furthermore, separate and joint attention mechanisms are developed and embedded to our model to add to its interpretability, as illustrated in case studies for predicting and explaining selective drug-target interactions. Lastly, alternative representations using protein sequences or compound graphs and a unified RNN/GCNN-CNN model using graph CNN (GCNN) are also explored to reveal algorithmic challenges ahead. Availability: Data and source codes are available at https://github.com/Shen-Lab/DeepAffinity Supplementary Information: Supplementary data are available at http://shen-lab.github.io/deep-affinity-bioinf18-supp-rev.pdf<br />https://github.com/Shen-Lab/DeepAffinity
- Subjects :
- FOS: Computer and information sciences
Statistics and Probability
Computer Science - Machine Learning
Source code
Computer science
media_common.quotation_subject
Machine Learning (stat.ML)
Machine learning
computer.software_genre
Biochemistry
Convolutional neural network
Machine Learning (cs.LG)
Domain (software engineering)
03 medical and health sciences
Deep Learning
0302 clinical medicine
Statistics - Machine Learning
Encoding (memory)
Amino Acid Sequence
Molecular Biology
030304 developmental biology
Interpretability
media_common
0303 health sciences
Artificial neural network
business.industry
Deep learning
Proteins
Biomolecules (q-bio.BM)
Original Papers
Computer Science Applications
Computational Mathematics
ComputingMethodologies_PATTERNRECOGNITION
Test case
Quantitative Biology - Biomolecules
Computational Theory and Mathematics
FOS: Biological sciences
Neural Networks, Computer
Artificial intelligence
Transfer of learning
business
computer
Software
030217 neurology & neurosurgery
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- Bioinformatics
- Accession number :
- edsair.doi.dedup.....63fee11f2cea8fc130ea9cf488cc100d
- Full Text :
- https://doi.org/10.1101/351601