Deep learning in drug discovery: opportunities, challenges and future prospects
Abstract
Artificial Intelligence (AI) is an area of computer science that simulates the structures and operating principles of the human brain. Machine learning (ML) belongs to the area of AI and endeavors to develop models from exposure to training data. Deep Learning (DL) is another subset of AI, where models represent geometric transformations over many different layers. This technology has shown tremendous potential in areas such as computer vision, speech recognition and natural language processing. More recently, DL has also been successfully applied in drug discovery. Here, I analyze several relevant DL applications and case studies, providing a detailed view of the current state-of-the-art in drug discovery and highlighting not only the problematic issues, but also the successes and opportunities for further advances.
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Drug molecules and associated features used in in silico models are transformed into vector format; data include molecular descriptors, molecular fingerprints, SMILES strings, and grids for convolutional neural networks.
“The drug molecules and the associated features used in the in silico models are transformed into vector format so they can be read by the learning systems. Generally, the data used here include molecular descriptors (e.g., physicochemical properties) and molecular fingerprints (molecular structure) as well as simplified molecular input line entry system (SMILES) strings and grids for convolutional”
From The rise of artificial intelligence in healthcare applications (2020) · cites this paper as [17]
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The rise of artificial intelligence in healthcare applications
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- Authors
- Lavecchia, A.
- Affiliations
- University of Naples Federico II (IT)
- Corresponding author
- Lavecchia, Antonio
- ORCID
- Lavecchia, Antonio
- Venue
- Drug Discovery Today
- Publisher
- Elsevier BV
- Published
- 2019-10
- Volume
- 24
- Issue
- 10
- Pages
- 2017-2032
- ISSN
- 1359-6446 (ISSN-L)
- OpenAlex
- W2964677890
- Semantic Scholar
- e110138a24e9e5a4d4c9ce177c38a9abb624caf3
- Cited by
- 353
- References
- 157
- Citations by year
- 2026: 29 · 2025: 57 · 2024: 76 · 2023: 62 · 2022: 50 · 2021: 53 · 2020: 25
- FWCI
- 2.65
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Drug Discovery Today (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- deep learning, drug discovery, machine learning, artificial intelligence, future prospects
- MeSH terms
- Deep Learning, Forecasting, Humans, Drug Discovery
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