AndroPred:一种基于人工智能的模型,用于预测雄激素受体抑制剂
Rohit Gagare1, Anju Sharma1, Prabha Garg1
1Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar, Punjab, India.
Journal of biomolecular structure & dynamics
|July 26, 2023
概括
人工智能,特别是深度学习,可以有效地预测前列腺癌药物发现的雄激素受体抑制剂. 这种人工智能方法通过分析高准确度的复合数据来加快新疗法的识别.
科学领域:
- 在瘤学瘤学.
- 计算化学计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 雄激素受体 (AR) 在前列腺癌 (PCa) 发展,推动细胞增殖和存活中至关重要.
- 目前用于PCa的AR抑制药物由于识别新型抑制剂的困难和成本而受到限制.
- 开发新的AR抑制剂对于推进前列腺癌治疗至关重要.
研究的目的:
- 采用人工智能 (AI) 算法来预测前列腺癌 (PCa) 的新型雄激素受体 (AR) 抑制剂.
- 为了加速AR向治疗的药物发现过程.
主要方法:
- 利用2242个化合物的数据集来训练预测模型.
- 应用了四种机器学习 (ML) 和深度学习 (DL) 算法.
- 模型使用分子描述符进行训练,包括1D,2D和分子指纹.
主要成果:
- 基于深度学习 (DL) 的预测模型表现出卓越的性能.
- 在训练数据集上达到92.18%的高精度,在测试数据集上达到93.05%的高精度.
- DNN模型显示了预测AR抑制剂的显著潜力.
结论:
- 深度学习 (DL),特别是DNN模型,是预测AR抑制剂的强大而有效的方法.
- 这种由人工智能驱动的策略可以在前列腺癌药物发现中显著简化新型AR抑制剂的识别.
- 建议进行进一步的实验验证,以确认这些模型的预测准确性和实际适用性.
关键词:
雄激素受体 (AR) 是一种深度神经网络 (DNN) 是一个深度神经网络.抑制剂 抑制剂 抑制剂k-最近的邻居 (kNN)机器学习 (ML) 是指机器学习.随机的森林 (RF) 随机的森林 (RF)支持矢量机器 (SVM) 的使用.更多相关视频
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