药物Appy - 一个端到端的深度学习框架用于计算药物发现
Elisa Poyatos-Racionero1, Lucía Paniagua-Herranz2, Cristian Privat1
1Cancerappy S.L., 48950, Erandio, Biscay, Spain.
Computers in biology and medicine
|October 19, 2025
概括
一个新的计算工具DrugAppy通过整合人工智能和化学方法来加速癌症药物发现. 它成功地发现了PARP和TEAD蛋白的强大抑制剂,与当前药物疗效相匹配或超过.
科学领域:
- 在瘤学瘤学.
- 计算化学的计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 针对癌症的脆弱性是晚期癌症治疗的关键,但药物开发是昂贵和缓慢的.
- 计算工具提供了一个有前途的策略,以加快和优化药物设计过程.
- 现有的方法需要大量的时间和资源来识别有效的抗癌剂.
研究的目的:
- 推出DrugAppy,一种用于识别新型癌症药物抑制剂的混合计算工具.
- 为了利用人工智能 (AI) 和计算化学进行高效的药物发现.
- 通过对PARP和TEAD蛋白家族的案例研究来验证该工具的有效性.
主要方法:
- 药物应用程序集成AI算法与计算和药物化学技术.
- 使用SMINA和GNINA的高通量虚拟选 (HTVS),以及使用GROMACS的分子动力学 (MD).
- 结合AI模型来预测药物药理动力学,选择性和活性.
主要成果:
- 药物Appy成功地发现了多 (ADP-ribose) 聚合酶 (PARP) 和转录增强关联域 (TEAD) 家族的新型抑制剂.
- 针对PARP1的两个分子的活性与现有的药物olaparib相提并论.
- 对于TEAD4的一种已识别的化合物在体外表现优于参考抑制剂IK-930.
- 确认了针对已识别的活性化合物的目标参与.
结论:
- 药物应用程序的工作流有效地发现具有显著治疗潜力的新型分子结构.
- 该工具展示了一种可行的方法,可以加速识别强效的抗癌剂.
- 这种方法提供了一个强大的平台,用于向癌症研究中的关键蛋白质.
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