文献精选 > 高引权威"AI药物发现"文献

AlphaFold accelerates artificial intelligence powered drug discovery: efficient discovery of a novel CDK20 small molecule inhibitor.

文献信息

DOI	10.1039/d2sc05709c
PMID	36794205
期刊	Chemical science
影响因子	7.4
JCR 分区	Q1
发表年份	2023
被引次数	91
关键词	人工智能, 药物发现, CDK20抑制剂, AlphaFold, 结构生物学
文献类型	Journal Article
ISSN	2041-6520
页码	1443-1452
期号	14(6)
作者	Feng Ren, Xiao Ding, Min Zheng, Mikhail Korzinkin, Xin Cai, Wei Zhu, Alexey Mantsyzov, Alex Aliper, Vladimir Aladinskiy, Zhongying Cao, Shanshan Kong, Xi Long, Bonnie Hei Man Liu, Yingtao Liu, Vladimir Naumov, Anastasia Shneyderman, Ivan V Ozerov, Ju Wang, Frank W Pun, Daniil A Polykovskiy, Chong Sun, Michael Levitt, Alán Aspuru-Guzik, Alex Zhavoronkov

一句话小结

本研究首次将AlphaFold应用于药物发现的命中识别，成功从靶点选择到化合物合成，仅用30天便发现了一种针对细胞周期依赖性激酶20（CDK20）的新型小分子化合物ISM042-2-048，其具有良好的抑制活性和选择性。该研究不仅展示了AI在药物开发中的潜力，还为缺乏实验结构信息的靶点提供了创新的药物设计思路。

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人工智能 · 药物发现 · CDK20抑制剂 · AlphaFold · 结构生物学

摘要

人工智能（AI）的应用被认为是药物发现和开发中的一场革命性变革。2020年，AlphaFold计算程序预测了整个人类基因组的蛋白质结构，这被视为AI应用和结构生物学领域的重大突破。尽管预测结构的可信度各不相同，但这些结构仍可能对基于结构的新靶点药物设计产生显著贡献，尤其是那些没有或有限结构信息的靶点。在本研究中，我们成功地将AlphaFold应用于我们的端到端AI驱动的药物发现引擎，包括生物计算平台PandaOmics和生成化学平台Chemistry42。我们在一个成本和时间高效的方式下，从靶点选择到命中分子的识别，确定了一种针对没有实验结构的新靶点的创新命中分子。PandaOmics提供了对肝细胞癌（HCC）治疗的感兴趣蛋白，而Chemistry42基于AlphaFold预测的结构生成了分子，所选分子经过合成并在生物测定中进行了测试。通过这种方法，我们在靶点选择后30天内，合成了7种化合物，发现了一种小分子命中化合物，针对细胞周期依赖性激酶20（CDK20），其结合常数Kd值为9.2 ± 0.5 μM（n = 3）。根据现有数据，进行了第二轮AI驱动的化合物生成，通过这一过程，发现了一种更具活性的命中分子ISM042-2-048，其平均Kd值为566.7 ± 256.2 nM（n = 3）。化合物ISM042-2-048在CDK20抑制活性方面也表现良好，IC50值为33.4 ± 22.6 nM（n = 3）。此外，ISM042-2-048在CDK20过表达的HCC细胞系Huh7中显示出选择性的抗增殖活性，IC50为208.7 ± 3.3 nM，相比之下对照筛选细胞系HEK293的IC50为1706.7 ± 670.0 nM。本研究是首次将AlphaFold应用于药物发现中的命中识别过程。

英文摘要

The application of artificial intelligence (AI) has been considered a revolutionary change in drug discovery and development. In 2020, the AlphaFold computer program predicted protein structures for the whole human genome, which has been considered a remarkable breakthrough in both AI applications and structural biology. Despite the varying confidence levels, these predicted structures could still significantly contribute to structure-based drug design of novel targets, especially the ones with no or limited structural information. In this work, we successfully applied AlphaFold to our end-to-end AI-powered drug discovery engines, including a biocomputational platform PandaOmics and a generative chemistry platform Chemistry42. A novel hit molecule against a novel target without an experimental structure was identified, starting from target selection towards hit identification, in a cost- and time-efficient manner. PandaOmics provided the protein of interest for the treatment of hepatocellular carcinoma (HCC) and Chemistry42 generated the molecules based on the structure predicted by AlphaFold, and the selected molecules were synthesized and tested in biological assays. Through this approach, we identified a small molecule hit compound for cyclin-dependent kinase 20 (CDK20) with a binding constant Kd value of 9.2 ± 0.5 μM (n = 3) within 30 days from target selection and after only synthesizing 7 compounds. Based on the available data, a second round of AI-powered compound generation was conducted and through this, a more potent hit molecule, ISM042-2-048, was discovered with an average Kd value of 566.7 ± 256.2 nM (n = 3). Compound ISM042-2-048 also showed good CDK20 inhibitory activity with an IC50 value of 33.4 ± 22.6 nM (n = 3). In addition, ISM042-2-048 demonstrated selective anti-proliferation activity in an HCC cell line with CDK20 overexpression, Huh7, with an IC50 of 208.7 ± 3.3 nM, compared to a counter screen cell line HEK293 (IC50 = 1706.7 ± 670.0 nM). This work is the first demonstration of applying AlphaFold to the hit identification process in drug discovery.

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主要研究问题

1. 在药物发现中，AlphaFold如何与其他AI技术结合以提高发现效率？
2. 除了CDK20，AlphaFold在其他靶点的药物发现中表现如何？
3. 采用AlphaFold预测的蛋白质结构对药物设计的影响有哪些具体案例？
4. 在使用AlphaFold进行药物发现时，如何评估预测结构的可靠性？
5. 针对不同类型的癌症，AlphaFold的应用潜力和局限性是什么？

核心洞察

研究背景和目的

本研究旨在利用AlphaFold技术加速人工智能驱动的药物发现，特别是开发一种新的CDK20小分子抑制剂。CDK20在细胞周期调控中起重要作用，其抑制剂的开发可能对癌症治疗具有重要意义。

主要方法/材料/实验设计

本研究采用了一系列合成步骤来制备新的小分子抑制剂。以下是主要的实验流程图：

1. 合成ISM042-2-001：使用DMF作为溶剂，K2CO3作为碱，经过多步反应合成目标化合物。反应后进行水洗和有机相提取，最后通过硅胶柱层析纯化。
2. 分析和表征：使用1H NMR和LCMS对合成的化合物进行结构确认和纯度分析。
3. 生物活性测试：评估合成的小分子抑制剂对CDK20的抑制效果。

关键结果和发现

• 成功合成了多种CDK20抑制剂，包括ISM042-2-001，ISM042-2-004等。
• 通过不同的合成路径获得了不同的化合物，并评估了其在细胞中的生物活性。
• 结果显示，合成的小分子在抑制CDK20活性方面表现出显著的效果。

主要结论/意义/创新性

本研究证明了AlphaFold在药物发现中的应用潜力，通过计算机辅助设计加速了新药的合成和筛选过程。新合成的CDK20抑制剂展示了良好的生物活性，为未来的癌症治疗提供了新的候选药物。

研究局限性和未来方向

• 目前的研究主要集中在体外实验，缺乏在动物模型中的验证。
• 未来研究应着重于优化合成路线，提高化合物的生物利用度和选择性。
• 进一步探索AlphaFold与其他AI技术结合的可能性，以提升药物发现的效率和准确性。

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