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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2411.06009 |
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| _version_ | 1866929585947410432 |
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| author | Uppalapati, Khartik Dandamudi, Eeshan Ice, S. Nick Chandra, Gaurav Bischof, Kirsten Lorson, Christian L. Singh, Kamal |
| author_facet | Uppalapati, Khartik Dandamudi, Eeshan Ice, S. Nick Chandra, Gaurav Bischof, Kirsten Lorson, Christian L. Singh, Kamal |
| contents | Traditional drug discovery is a long, expensive, and complex process. Advances in Artificial Intelligence (AI) and Machine Learning (ML) are beginning to change this narrative. Here, we provide a comprehensive overview of different AI and ML tools that can be used to streamline and accelerate the drug discovery process. By using data sets to train ML algorithms, it is possible to discover drugs or drug-like compounds relatively quickly, and efficiently. Additionally, we address limitations in AI-based drug discovery and development, including the scarcity of high-quality data to train AI models and ethical considerations. The growing impact of AI on the pharmaceutical industry is also highlighted. Finally, we discuss how AI and ML can expedite the discovery of new antibiotics to combat the problem of worldwide antimicrobial resistance (AMR). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_06009 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Comprehensive Guide to Enhancing Antibiotic Discovery Using Machine Learning Derived Bio-computation Uppalapati, Khartik Dandamudi, Eeshan Ice, S. Nick Chandra, Gaurav Bischof, Kirsten Lorson, Christian L. Singh, Kamal Artificial Intelligence Traditional drug discovery is a long, expensive, and complex process. Advances in Artificial Intelligence (AI) and Machine Learning (ML) are beginning to change this narrative. Here, we provide a comprehensive overview of different AI and ML tools that can be used to streamline and accelerate the drug discovery process. By using data sets to train ML algorithms, it is possible to discover drugs or drug-like compounds relatively quickly, and efficiently. Additionally, we address limitations in AI-based drug discovery and development, including the scarcity of high-quality data to train AI models and ethical considerations. The growing impact of AI on the pharmaceutical industry is also highlighted. Finally, we discuss how AI and ML can expedite the discovery of new antibiotics to combat the problem of worldwide antimicrobial resistance (AMR). |
| title | A Comprehensive Guide to Enhancing Antibiotic Discovery Using Machine Learning Derived Bio-computation |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2411.06009 |