Summary of A Comprehensive Guide to Enhancing Antibiotic Discovery Using Machine Learning Derived Bio-computation, by Khartik Uppalapati et al.
A Comprehensive Guide to Enhancing Antibiotic Discovery Using Machine Learning Derived Bio-computation
by Khartik Uppalapati, Eeshan Dandamudi, S. Nick Ice, Gaurav Chandra, Kirsten Bischof, Christian L. Lorson, Kamal Singh
First submitted to arxiv on: 8 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a comprehensive overview of Artificial Intelligence (AI) and Machine Learning (ML) tools that can accelerate and streamline the drug discovery process. The authors demonstrate how training ML algorithms on large datasets enables rapid discovery of drugs or drug-like compounds. However, they also highlight limitations, such as the scarcity of high-quality data and ethical considerations. Furthermore, the paper explores the growing impact of AI on the pharmaceutical industry and discusses its potential in expediting the discovery of new antibiotics to combat antimicrobial resistance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper shows how AI and ML can help find new medicines faster. It explains that by using big datasets to train computer algorithms, scientists can quickly discover new drugs or drug-like compounds. The authors also talk about some challenges, like not having enough good data to train the models and worrying about ethics. Additionally, it discusses how AI is changing the pharmaceutical industry and how it can help find antibiotics faster. |
Keywords
» Artificial intelligence » Machine learning
