深度学习方法用于从DNA编码的化学库中发现瘤向的小型有机干
Wen Torng1, Ilaria Biancofiore2, Sebastian Oehler2
1Google Research, 1600 Amphitheatre Parkway, Mountain View, California 94043, United States.
ACS omega
|July 24, 2023
概括
与机器学习相结合的DNA编码化学图书馆 (DEL) 确定了新型瘤向化酶IX (CAIX) 的瘤向配体. 这种方法成功地将化合物转化为小鼠体内的瘤向.
科学领域:
- 药用化学 医学化学
- 计算化学计算化学
- 在瘤学瘤学.
背景情况:
- 用DNA编码的化学库 (DELs) 提供大规模的蛋白质 - 配体相互作用数据,以实现高效的药物发现.
- 碳酸无水酶IX (CAIX) 是瘤缺氧和清细胞细胞癌的经过验证的生物标志物.
研究的目的:
- 开发一种结合DEL查和深度学习的新策略,用于识别针对CAIX的瘤向配体.
- 在现有的DEL库之外生成新的化学动机,用于CAIX抑制.
主要方法:
- 实例级深度学习模型在CAIX选的DEL数据集上进行了训练.
- 机器学习模型预测了与原始 DEL 元素不相似的新型 CAIX 结合配体.
- 击中到领先优化涉及优先考虑基于预测的亲和力和可行性的合成类型.
主要成果:
- 在152个新的预测中,70%显示出微小分子酶抑制 (IC50<1μM).
- 三个主要候选者在表达CAIX的瘤细胞上表现出累积.
- 最主要的化合物在小鼠中表现出5.7nM的KD和选择性的体内瘤向.
结论:
- 德尔和机器学习之间的协同作用加快了新发现的发现和 in vivo瘤向剂的开发.
- 这种综合方法成功地将DEL衍生的潜在潜在治疗候选者转化为CAIX阳性癌症.
- 这项研究突出了用于识别和优化向癌症治疗的强大平台.
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