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| Main Authors: | , , , , , , |
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| Format: | Artículo científico |
| Language: | en |
| Published: |
Journal of microbiological methods
2025
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| Subjects: | |
| Online Access: | https://pubmed.ncbi.nlm.nih.gov/40846079/ |
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| _version_ | 1868266162719031297 |
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| author | Mulat, Mulugeta Banicod, Riza Jane S Tabassum, Nazia Javaid, Aqib Kim, Tae-Hee Kim, Young-Mog Khan, Fazlurrahman |
| author_facet | Mulat, Mulugeta Banicod, Riza Jane S Tabassum, Nazia Javaid, Aqib Kim, Tae-Hee Kim, Young-Mog Khan, Fazlurrahman Mulat, Mulugeta Banicod, Riza Jane S Tabassum, Nazia Javaid, Aqib Kim, Tae-Hee Kim, Young-Mog Khan, Fazlurrahman |
| collection | PubMed - marine biology |
| contents | Application of artificial intelligence in microbial drug discovery: Unlocking new frontiers in biotechnology. Mulat, Mulugeta Banicod, Riza Jane S Tabassum, Nazia Javaid, Aqib Kim, Tae-Hee Kim, Young-Mog Khan, Fazlurrahman Drug Discovery Artificial Intelligence Humans Biotechnology Anti-Infective Agents Computational Biology Deep Learning Machine Learning Bacteria Artificial intelligence (AI) is revolutionizing antimicrobial drug discovery by delivering major improvements in precision, innovation, and efficiency for combating bacterial, fungal, and viral pathogens. Traditional approaches to developing treatments for microbial infections are often hampered by high costs, lengthy timelines, and frequent failures. Modern AI technologies, particularly deep learning, machine learning, computational biology, and big data analytics, provide robust solutions to these challenges by analyzing large-scale biological datasets to predict molecular interactions, identify promising treatment candidates, and expedite both preclinical and clinical development. Innovative techniques such as generative adversarial networks for novel compound discovery, reinforcement learning for optimizing antimicrobial candidates, and natural language processing for extracting knowledge from biomedical literature are now vital to infectious disease research. These approaches facilitate early toxicity prediction, microbial target identification, virtual screening, and the development of more individualized therapies. Notwithstanding these advances, challenges remain, including inconsistent data quality, limited interpretability, and unresolved ethical or legal concerns. This review examines recent advancements in AI applications for microbial drug discovery, with a focus on de novo molecular design, ligand- and structure-based screening, and AI-enabled biomarker identification. Remaining application barriers and promising future directions in AI-driven antimicrobial drug development are also elucidated. Collectively, these innovations are poised to accelerate the discovery of new therapies, reduce costs, and enhance patient outcomes in the fight against infectious diseases. |
| format | Artículo científico |
| id | pubmed_40846079 |
| institution | PubMed |
| language | en |
| publishDate | 2025 |
| publisher | Journal of microbiological methods |
| record_format | pubmed |
| spellingShingle | Application of artificial intelligence in microbial drug discovery: Unlocking new frontiers in biotechnology. Mulat, Mulugeta Banicod, Riza Jane S Tabassum, Nazia Javaid, Aqib Kim, Tae-Hee Kim, Young-Mog Khan, Fazlurrahman Drug Discovery Artificial Intelligence Humans Biotechnology Anti-Infective Agents Computational Biology Deep Learning Machine Learning Bacteria Application of artificial intelligence in microbial drug discovery: Unlocking new frontiers in biotechnology. Mulat, Mulugeta Banicod, Riza Jane S Tabassum, Nazia Javaid, Aqib Kim, Tae-Hee Kim, Young-Mog Khan, Fazlurrahman Drug Discovery Artificial Intelligence Humans Biotechnology Anti-Infective Agents Computational Biology Deep Learning Machine Learning Bacteria Artificial intelligence (AI) is revolutionizing antimicrobial drug discovery by delivering major improvements in precision, innovation, and efficiency for combating bacterial, fungal, and viral pathogens. Traditional approaches to developing treatments for microbial infections are often hampered by high costs, lengthy timelines, and frequent failures. Modern AI technologies, particularly deep learning, machine learning, computational biology, and big data analytics, provide robust solutions to these challenges by analyzing large-scale biological datasets to predict molecular interactions, identify promising treatment candidates, and expedite both preclinical and clinical development. Innovative techniques such as generative adversarial networks for novel compound discovery, reinforcement learning for optimizing antimicrobial candidates, and natural language processing for extracting knowledge from biomedical literature are now vital to infectious disease research. These approaches facilitate early toxicity prediction, microbial target identification, virtual screening, and the development of more individualized therapies. Notwithstanding these advances, challenges remain, including inconsistent data quality, limited interpretability, and unresolved ethical or legal concerns. This review examines recent advancements in AI applications for microbial drug discovery, with a focus on de novo molecular design, ligand- and structure-based screening, and AI-enabled biomarker identification. Remaining application barriers and promising future directions in AI-driven antimicrobial drug development are also elucidated. Collectively, these innovations are poised to accelerate the discovery of new therapies, reduce costs, and enhance patient outcomes in the fight against infectious diseases. |
| title | Application of artificial intelligence in microbial drug discovery: Unlocking new frontiers in biotechnology. |
| topic | Drug Discovery Artificial Intelligence Humans Biotechnology Anti-Infective Agents Computational Biology Deep Learning Machine Learning Bacteria |
| url | https://pubmed.ncbi.nlm.nih.gov/40846079/ |