机械语言建模和氧化3D查揭示了抗性前列腺癌中柏柏林和胺胺的协同作用
bioRxiv : the preprint server for biology
|February 9, 2026
概括
我们发现了使用人工智能治疗前列腺癌的新组合疗法. 柏柏林和甲胺一起工作,克服对标准治疗的耐药性,提供了一个有前途的新治疗策略.
科学领域:
- 在瘤学瘤学.
- 计算生物学 计算生物学
- 药理学 药理学是指药理学的学科.
背景情况:
- 前列腺癌治疗面临的挑战是对雄激素受体抑制剂的耐药性.
- 鉴定有效的组合疗法是困难的,因为复杂的相互作用和有限的模型可解释性.
研究的目的:
- 开发一个集成的人工智能和实验框架,以发现协同效应的共同疗法.
- 确定新的协同作用药物组合,以克服前列腺癌的治疗耐药性.
主要方法:
- 结合知识增强的大型语言模型与补充氧气的3D球形状测试.
- 开发了一种基于共识的指标,用于使用模型随机性的预测准确性.
- 选了3592种天然产品以确定协同作用.
主要成果:
- 鉴定了柏柏林和恩扎拉胺之间一种新的协同作用,该协同作用使抗性前列腺癌细胞重新敏感.
- 发现柏柏林会扰乱PI3K/AKT/mTOR和AMPK信号通路.
- 计算预测与实验验证一致,提供了机械的见解.
结论:
- 集成的可解释AI与3D查提供了一种可扩展的方法,用于合理的药物发现.
- 柏柏林-甲胺组合显示出作为抗性前列腺癌的新疗法的潜力.
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