Infectious diseases pose a serious threat to global public health, making the research, development, and quality control of anti-infective drugs of critical importance. In recent years, artificial intelligence (AI) and big data have accelerated the drug discovery process and improved drug quality control levels by leveraging their efficient data processing capabilities and pattern recognition abilities. This article reviews the application of AI and big data in anti-infective drug molecular screening, resistance mechanism research, and broad-spectrum candidate drug development, while also summarizing their key roles in pharmaceutical production and quality control processes. However, the application of AI and big data technology in the development and quality control of anti-infective drugs still has limitations. In the future, it is necessary to promote the integration of interdisciplinary approaches and improve algorithm accuracy to drive the development and quality control of anti-infective drugs toward a new phase of efficiency and precision.
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |