在人工智能驱动的蛋白质-连接体相互作用预测的最新进展
Jaemin Sim1, Dongwoo Kim2, Bomin Kim2
1Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, 08826, Republic of Korea.
Current opinion in structural biology
|February 25, 2025
概括
人工智能 (AI) 正在通过增强蛋白质-连接体相互作用预测来彻底改变基于结构的药物发现. 人工智能模型可以提高带结合部位预测,姿势估计和虚拟查准确度.
科学领域:
- 计算化学是一种计算化学.
- 人工智能在药物发现中的作用
- 分子建模分子建模
背景情况:
- 基于结构的药物发现依赖于蛋白质-连接体相互作用的计算模型.
- 传统的对接方法在准确度上有局限性.
- 人工智能正在成为克服这些局限性的强大工具.
研究的目的:
- 审查人工智能驱动的蛋白质-配体相互作用预测方法的最新进展.
- 突出AI对基于结构的药物发现的关键方面的影响.
- 讨论挑战和未来的方向.
主要方法:
- 审查人工智能模型,包括图形神经网络,混合密度网络,变压器和扩散模型.
- 对人工智能应用在带结合部位预测,结合姿势估计,评分功能开发和虚拟查方面的分析.
- 整合几何深度学习,基于序列的嵌入和物理约束的深度学习.
主要成果:
- 与传统方法相比,人工智能模型显示出更好的预测性能.
- 人工智能改进了连接体结合位点识别和结合姿势预测.
- 人工智能驱动的评分功能改善了绑定亲和度估计和虚拟选策略.
结论:
- 人工智能技术正在显著提高基于结构的药物发现的准确性和效率.
- 跨多样化的蛋白质 - 配体对的泛化仍然是一个持续的挑战.
- 人工智能即将彻底改变分子对接和亲和预测.
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