用机器学习加速抗生素发现，应对全球耐药危机
A Comprehensive Guide to Enhancing Antibiotic Discovery Using Machine Learning Derived Bio-computation
- 利用机器学习分析数据集，快速筛选潜在药物分子
- 突破传统研发周期长、成本高的瓶颈，提升发现效率
- 适合药物研发、生物信息学及公共卫生领域研究者
传统药物研发耗时长、成本高且流程复杂。人工智能（AI）与机器学习（ML）的进步正在改变这一局面。本文全面综述了可用于加速药物研发的各类AI与ML工具。通过使用数据集训练机器学习模型，可快速高效地发现候选药物或类药物化合物。同时，文章指出当前基于AI的药物研发面临数据质量不足与伦理挑战等局限性。此外，还强调了AI对制药行业的日益深远影响，并探讨了如何借助AI与ML加速新型抗生素的发现，以应对全球范围内的抗菌药物耐药性（AMR）问题。
原文摘要 · Abstract (English)
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).
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