针对机器学习模型的适用性域扩展研究揭示了新的CYP2B6抑制剂
Patricia A Vignaux1, Joshua S Harris1, Fabio Urbina1
1Collaborations Pharmaceuticals, Inc, Raleigh, North Carolina.
概括
机器学习模型可以预测与CYP2B6的药物相互作用,但需要更多的数据. 扩大模型的范围.
科学领域:
- 药理学和毒理学 药理学和毒理学
- 计算化学计算化学
- 药物新陈代谢 药物新陈代谢
背景情况:
- 细胞染色体P450 2B6 (CYP2B6) 对于第一阶段药物代谢至关重要;其抑制可能导致药物不良事件.
- 预测CYP2B6相互作用的现有机器学习模型受到稀缺的公共领域数据的限制.
- 提高模型适用性领域和预测能力需要扩展训练数据集以新型化合物.
研究的目的:
- 通过将其训练集扩展到多种化合物来改进CYP2B6抑制的机器学习模型.
- 用基于距离的选择方法评估适用性域扩展技术的有效性.
- 通过对精选的药物重用库化合物的体外测试来确定CYP2B6的新型抑制剂.
主要方法:
- 基于距离的方法被用来定义模型的适用领域,并测量化学多样性.
- t分布式随机邻居嵌入 (t-SNE) 图形可视化了训练集的化学空间.
- 从药物重用库中选择的化合物是根据训练组的最大欧几里德距离进行选择,并在体外测试CYP2B6抑制.
主要成果:
- 该方法增加了训练集的多样性,但没有显著改善模型性能或适用性.
- 在体外测试中确定了vilanterol和allylestrenol作为CYP2B6抑制剂,其IC50值为微粒级以下至低.
- 该研究强调了扩大机器学习模型应用领域的复杂性,即使有针对性的努力.
结论:
- 机器学习模型适用性领域的故意扩展具有挑战性,但可以产生新的见解.
- 即使是对训练数据进行多样化的保守努力,也可以发现新的药物代谢酶抑制剂.
- 维兰特醇和烯被确定为新型CYP2B6抑制剂,需要进一步调查.
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