Drug Discovery Today· 2018Q1· Review
The rise of deep learning in drug discovery
- 1,789citations
- Q1SCImago
- 2018year
Short summary
Deep learning, an evolution of artificial neural networks, is demonstrating superior performance over traditional machine learning algorithms in diverse drug discovery applications beyond bioactivity prediction.
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Key points
- Deep learning excels over other machine learning algorithms in tasks like image and voice recognition.
- The utility of deep learning in drug discovery extends beyond bioactivity prediction.
- Applications include de novo molecular design, synthesis prediction, and biological image analysis.
- This technology represents an evolution from earlier artificial neural network research.
AI-generated from the title and abstract; the full text is not read.
Abstract
Over the past decade, deep learning has achieved remarkable success in various artificial intelligence research areas. Evolved from the previous research on artificial neural networks, this technology has shown superior performance to other machine learning algorithms in areas such as image and voice recognition, natural language processing, among others. The first wave of applications of deep learning in pharmaceutical research has emerged in recent years, and its utility has gone beyond bioactivity predictions and has shown promise in addressing diverse problems in drug discovery. Examples will be discussed covering bioactivity prediction, de novo molecular design, synthesis prediction and biological image analysis.
The authors' abstract, as published at the source. Drug Discovery Today, 2018 · DOI ↗
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Field: Computational Theory and Mathematics
Computational Theory and MathematicsComputer Science