DeepEGFR是一个图形神经网络,用于EGFR抑制剂的生物活性分类
Aijaz Ahmad Malik1, Costerwell Khyriem2, Sven Hauns3
1Center for Applied and Translational Genomics, Mohammed Bin Rashid University of Medicine and Health Sciences, P.O. Box 505055, Dubai, United Arab Emirates.
Scientific reports
|November 1, 2025
概括
一个新的机器学习模型,DeepEGFR,准确地对抑制表皮生长因子受体 (EGFR) 活性的小化合物进行了分类. 该工具通过识别有希望的,未被充分研究的EGFR抑制剂,加速发现新型癌症治疗方法.
科学领域:
- 在瘤学瘤学.
- 计算化学计算化学
- 生物信息学是一种生物信息学.
背景情况:
- 表皮生长因子受体 (EGFR) 是癌症治疗的关键标.
- 开发新的EGFR抑制剂对于推进癌症治疗至关重要.
- 机器学习为预测分子相互作用和药物疗效提供了强大的工具.
研究的目的:
- 开发一种新的机器学习 (ML) 方法,用于精确分类针对EGFR的小化合物.
- 为了识别具有EGFR活性,中间或非活性调节的化合物.
- 加速对EGFR驱动癌症的新治疗剂的发现.
主要方法:
- 开发了DeepEGFR,一个多类图形神经网络 (GNN) 模型.
- 集成的多个分子表示 (SMILES,Klekota-Roth,PubChem指纹).
- 构建了详细的分子图,捕捉了原子和键特性.
主要成果:
- 在所有活动类别中,DeepEGFR实现了~94%的F1分数,超过了基线ML算法.
- 确定了前20个与FDA批准的EGFR抑制剂生物学相关的特征.
- 发现了300种未被充分研究的化合物,具有潜在的EGFR向能力.
结论:
- 深度EGFR在分类EGFR抑制剂活性方面表现出高效率.
- 该模型的可解释性证实了已识别的特征的生物相关性.
- 深度EGFR可以显著加快新型向癌症疗法的识别.
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