通过深度学习模型预测蛋白质 - 配体结合亲和力
1School of Physics and Engineering, Henan University of Science and Technology, Luoyang 471023, China.
Briefings in bioinformatics
|March 6, 2024
概括
深度学习模型对预测蛋白质 - 配体结合亲和力非常有希望,这对于药物发现至关重要. 本综述涵盖了计算方法,数据库和准确预测的挑战.
科学领域:
- 计算化学和化学信息学
- 生物物理和结构生物学
- 人工智能在药物设计中的应用
背景情况:
- 准确预测蛋白质 - 配体结合亲和力对于药物查和优化至关重要.
- 传统的计算方法在预测结合亲和力方面面临着挑战.
- 深度学习 (DL) 的最新进展为这项任务提供了新的潜力.
结论:
- 深度学习具有显著的潜力,可以改善蛋白质-连接体结合亲和力预测.
- 解决目前数据和模型的局限性是推动该领域发展的关键.
- 需要进一步的研究,以充分利用DL来有效地发现药物.
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