Video-Driven Artificial Intelligence for Predictive Modelling of Antimicrobial Peptide Generation: Literature Review on Advances and Challenges
How video-based methodologies and advanced computer vision algorithms can facilitate the development of antimicrobial peptide (AMP) generation models should be further reviewed, structural and functional patterns should be elucidated, and the generative power of in silico pipelines should be enhance...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/13/7363 |
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| Summary: | How video-based methodologies and advanced computer vision algorithms can facilitate the development of antimicrobial peptide (AMP) generation models should be further reviewed, structural and functional patterns should be elucidated, and the generative power of in silico pipelines should be enhanced. AMPs have drawn significant interest as promising therapeutic agents because of their broad-spectrum efficacy, low resistance profile, and membrane-disrupting mechanisms. However, traditional discovery methods are hindered by high costs, lengthy synthesis processes, and difficulty in accessing the extensive chemical space involved in AMP research. Recent advances in artificial intelligence—especially machine learning (ML), deep learning (DL), and pattern recognition—offer game-changing opportunities to accelerate AMP design and validation. By integrating video analysis with computational modelling, researchers can visualise and quantify AMP–microbe interactions at unprecedented levels of detail, thereby informing both experimental design and the refinement of predictive algorithms. This review provides a comprehensive overview of these emerging techniques, highlights major breakthroughs, addresses critical challenges, and ultimately emphasises the powerful synergy between video-driven pattern recognition, AI-based modelling, and experimental validation in the pursuit of next-generation antimicrobial strategies. |
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| ISSN: | 2076-3417 |