基于深度学习模型的药物标结合亲和力的预测
Hao Zhang1, Xiaoqian Liu1, Wenya Cheng1
1College of Science, Nanjing Agricultural University, Nanjing, 210095, China.
深度学习模型通过改进药物标结合亲和力 (DTA) 预测来彻底改变药物发现. 本综述涵盖了用于DTA预测的深度学习算法,数据集和指标,为未来的机遇和挑战提供了洞察力.
科学领域:
- 计算化学是一种计算化学.
- 药理学 药理学是指药理学的学科.
- 人工智能在药物发现中的作用
背景情况:
- 药物标结合亲和力 (DTA) 的预测对于有效的药物发现至关重要.
- 传统的虚拟查方法在提高药物开发成功率方面存在局限性.
- 深度学习 (DL) 提供了一种有希望的方法来提高DTA预测的准确性.
研究的目的:
- 审查目前关于深度学习模型的文献,用于药物标结合亲和力预测.
- 总结关键方面,包括数据集,指标和用于基于DL的DTA预测的算法.
- 讨论DL在药物发现中的机遇,挑战和未来前景.
主要方法:
- 对使用深度学习进行DTA预测的研究进行了全面的文献综述.
- 分析各种深度学习框架,如CNN,GCN和RNN.
- 检查输入表示,性能指标和模型可解释性.
主要成果:
- 深度学习模型在提高DTA预测准确度方面显示出显著的潜力.
- 广泛的DL算法和架构正在应用于DTA预测.
- 标准化数据集和强大的评估指标对于模型比较至关重要.
结论:
- 深度学习代表了DTA预测的范式转变,超越了传统的机器学习.
- 解决模型解释性和数据标准化方面的挑战是未来进步的关键.
- DL框架对加速药物发现和开发具有重大前景.
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