AI瓶中的药物之神

English | April 12, 2024 | ISBN: N/A | ASIN: B0CW1HCFQC | 220 pages | EPUB | 1.05 Mb

Another promising application of AI in drug discovery is the use of natural language processing (NLP) to analyze biomedical literature. NLP algorithms can be used to extract information from scientific papers, such as the structure of proteins, the function of genes, and the mechanisms of disease. This information can then be used to identify new targets for drug discovery, or to design new drugs that target specific pathways.

AI-powered drug discovery is still in its early stages, but it has the potential to revolutionize the way that new drugs are discovered. By providing researchers with new tools to identify and target disease, AI can help to bring new treatments to patients faster.

Here are some specific examples of how AI is being used to power drug discovery

In 2019, a team of researchers from the University of California, San Diego used AI to identify a new drug target for cancer. The researchers used a ML algorithm to analyze a dataset of over 100,000 cancer-related genes, and identified a gene called BRD4 that was found to be overexpressed in cancer cells. The researchers then designed a drug that targeted BRD4, and showed that the drug was able to inhibit the growth of cancer cells in vitro and in mice. In 2020, a team of researchers from the University of Cambridge used AI to design a new drug for Alzheimer's disease. The researchers used a ML algorithm to analyze a dataset of over 100,000 molecules, and identified a molecule called C3 that was found to inhibit the formation of amyloid plaques in the brain. The researchers then tested C3 in mice, and showed that the drug was able to reduce the levels of amyloid plaques and improve cognitive function. In 2021, a team of researchers from the Massachusetts Institute of Technology used AI to identify a new drug for HIV. The researchers used a ML algorithm to analyze a dataset of over 100,000 molecules, and identified a molecule called GS-9674 that was found to inhibit the replication of HIV. The researchers then tested GS-9674 in mice, and showed that the drug was able to reduce the levels of HIV in the blood and improve the immune response.

These are just a few examples of the many ways that AI is being used to power drug discovery. As AI algorithms continue to improve, we can expect to see even more breakthroughs in the field of drug discovery. These breakthroughs have the potential to bring new treatments to patients faster, and to improve the lives of millions of people.

另一个在药物发现中具有巨大潜力的应用是利用自然语言处理（NLP）来分析生物医学文献。NLP算法可用于从科学论文中提取信息，例如蛋白质的结构、基因的功能以及疾病的机制。这些信息随后可用于识别新的药物靶标，或设计针对特定途径的新药。 基于人工智能的药物发现目前仍处于早期阶段，但它有潜力彻底改变新药的发现方式。通过为研究人员提供新的工具来识别和针对疾病，AI可以帮助更快地将新疗法带给患者。 以下是人工智能如何被用于推动药物发现的一些具体例子： 2019年，来自加州大学圣地亚哥分校的一支研究团队使用人工智能鉴定出了一种癌症的新靶标。该团队利用机器学习算法分析了超过10万份与癌症相关的基因数据，并识别出了一种名为BRD4的基因，在癌细胞中发现其表达水平过高。研究人员随后设计了一种针对BRD4的药物，并展示了该药物能够在体外和小鼠体内抑制癌细胞的生长。 2020年，来自剑桥大学的一支研究团队利用人工智能为阿尔茨海默病设计了一种新的药物。该团队使用机器学习算法分析了超过10万份分子数据，并识别出一种名为C3的分子，在大脑中能够抑制β-淀粉样斑块的形成。随后研究人员在小鼠身上测试了C3，发现这种药物能够在体内降低β-淀粉样斑块水平并改善认知功能。 2021年，麻省理工学院的一支研究团队利用人工智能鉴定出了一种用于HIV的新药。该团队使用机器学习算法分析了超过10万份分子数据，并识别出了一种名为GS-9674的分子，在体内能够抑制HIV的复制。随后研究人员在小鼠身上测试了GS-9674，发现这种药物能够在血液中降低HIV水平并改善免疫反应。 这只是人工智能被用于推动药物发现方式之一的例子。随着AI算法继续改进，我们预计将在药物发现领域看到更多的突破性进展。这些突破有可能更快地为患者带来新的治疗方法，并改善数百万人的生活。

---

Download from free file storage

 Download from rapidgator.net  Download from nitroflare.com

---

我要反馈

---

本站不对文件进行储存，仅提供文件链接，请自行下载，本站不对文件内容负责，请自行判断文件是否安全，如发现文件有侵权行为，请联系管理员删除。