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毒素图网:用于精确预测药物分子毒性的图形神经网络框架

Mayank Chotaliya¹, Smita S Agrawal²

¹Department of Computer Science and Engineering, Institute of Technology, Nirma University, Gujarat, 382481, India. 24mce008@nirmauni.ac.in.

Journal of computer-aided molecular design

|October 24, 2025

概括

图形神经网络模型ToxiGraphNet可以从SMILES字符串中准确预测药物分子毒性 (LD50). 这种方法绕过了传统的描述符,加速了药物发现并降低了实验成本.

科学领域:

计算化学计算化学
药物发现药物发现药物发现
机器学习机器学习

背景情况:

准确预测药物毒性对于降低制药研究成本和不良影响至关重要.
使用手工制作的分子描述器的传统方法难以捕捉复杂的分子细微差别.

研究的目的:

开发ToxiGraphNet,一个图形神经网络 (GNN) 模型,用于从分子SMILES字符串直接预测LD50值.
在不依赖于手工设计的特征的情况下,证明GNN在毒性预测中的有效性.

主要方法:

使用RDKit将分子转换为图形表示,原子作为节点,键作为边缘,并以化学特征进行丰富.
在PyTorch Geometric中使用了边缘条件卷积 (NNConv) GNN架构来进行特征聚合.
该模型包括三个NNConv层,其中包括正常化,掉机,剩余连接和完全连接的层,用于LD50预测.

主要成果:

ToxiGraphNet在一个精心策划的LD50数据集上取得了强大的预测性能.
关键的绩效指标包括MSE:0.3610,MAE:0.4424,RMSE:6009和R2:0.5959. 这些指标包括:
该模型展示了强大的概括和准确的毒性预测.

结论:

图形神经网络对于分子毒性建模是有效的,为药物发现提供了可扩展的解决方案.
毒素图谱网为传统方法提供了一个有希望的替代方案,增强了制药管道中的属性预测.

关键词:

药物药物药物药物药物有边缘条件的GCNs.图表注意力网络图表注意力网络图形卷积网络是指图形卷积网络.图形神经网络是一个神经网络.LD50 LD50 这是一笔大笔钱.有毒性有毒性

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