MoleProLink-RL:在药物向相互作用预测领域的政策强化学习的几何传输
Donghao Xu1,2, Bibo Wang3, Dexing Zhu1,4
1Shanghai Enginerring Research Center of Colorectal Cancer Minimally Invasive Technology, Shanghai, China.
MoleProLink-RL通过将分子表示与稳定性意识的决策进行整合,提高了药物向相互作用 (DTI) 的预测. 这种新的方法可以在各种条件下提高模型性能,促进发现工作流.
科学领域:
- 计算化学是一种计算化学.
- 生物信息学是一种生物信息学.
- 机器学习在药物发现中的作用
背景情况:
- 准确的药物向相互作用 (DTI) 预测对于药物发现至关重要.
- 现有的模型因化学型,蛋白质家族或测试条件的变化而难以处理性能转变.
研究的目的:
- 开发一个强大的DTI预测模型,MoleProLink-RL,在不同的条件下保持性能.
- 在遇到新的化学或生物环境时,应对模型低性能的挑战.
主要方法:
- 结合化学忠实表示与几何意识的对齐和稳定意识的决策.
- 使用图形转换器药物编码器,残留蛋白质嵌入和无监督分布对齐.
- 采用强化学习目标来对子环境中的稳定性进行排名.
主要成果:
- MoleProLink-RL训练了代表性,对齐和端到端的决策,以改善候选人优先级.
- 在人类,C. elegans和戴维斯基准上得到验证,表现出强大的性能.
- 在不断变化的条件下实现了顶级精密维护.
结论:
- MoleProLink-RL提供了一个实用,数据效率高的DTI预测解决方案.
- 该模型有效地缩小了基准性能和实验室影响之间的差距.
- 端到端的培训提高了药物发现管道中的模型适应性和可靠性.
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