在深度学习时代,为药物发现建模蛋白质-连接体相互作用
Yuzhe Wang1, Yibo Li2, Jiaxiao Chen1
1Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China. lhlai@pku.edu.cn.
Chemical Society reviews
|October 21, 2025
概括
深度学习 (DL) 通过补充基于物理的蛋白质-连接体相互作用方法来增强计算药物发现. 整合这些方法可以提高探索广化学空间的效率和预测准确性.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 生物信息学是一种生物信息学.
背景情况:
- 蛋白质 - 配体相互作用建模对于药物发现至关重要.
- 基于物理学的计算方法在成本,可扩展性和准确性方面面临挑战.
- 深度学习 (DL) 提供数据驱动的解决方案,以补充传统方法.
研究的目的:
- 审查深度学习中的最新进展,用于蛋白质-配体相互作用建模.
- 突出将DL与基于物理的方法集成的策略.
- 讨论计算药物发现的挑战和未来方向.
主要方法:
- 对DL在分子动力学,对接,虚拟选和新药设计中的应用进行了审查.
- 探索用于相互作用预测的基于序列的方法.
- 分析基于物理和数据的综合策略.
主要成果:
- DL的进步提供了强大的数据驱动范式,补充了基于物理的策略.
- 具体的DL应用包括增强的分子动力学,增强的对接,端到端建模,生成的de novo设计和基于序列的预测.
- 整合DL和基于物理的方法显示了提高预测能力和效率的前景.
结论:
- 结合基于物理和数据的方法是推动药物发现的关键.
- 整合提高了在探索化学和生物空间的预测能力和效率.
- 未来的方向包括进一步整合和解决计算建模中的持续挑战.
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