克服药物发现问题中的阶级不平衡:图表神经网络和平衡方法
Rafael Lopes Almeida1, Vinícius Gonçalves Maltarollo2, Frederico Gualberto Ferreira Coelho3
1Graduate Program in Electrical Engineering - Universidade Federal de Minas Gerais, Av. Antônio Carlos 6627, Belo Horizonte, 31270-901, MG, Brazil.
Journal of molecular graphics & modelling
|October 6, 2023
概括
图形神经网络 (GNN) 可以提高具有成本效益的药物开发. 过量采样技术显示出在分子图分析中处理不平衡数据集的前景,尽管需要进一步研究.
科学领域:
- 计算化学是一种计算化学.
- 机器学习在药物发现中的作用
背景情况:
- 药物开发是昂贵和耗时的.
- 数据集中的类失衡 (活性与非活性化合物) 给机器学习模型带来了挑战.
- 图形神经网络 (GNN) 为分子数据分析提供了一个有前途的方法.
研究的目的:
- 调查GNN在提高药物开发成本效益方面的有效性.
- 在各种数据集上比较不同GNN架构的性能.
- 评估在药物发现数据集中处理类不平衡的策略.
主要方法:
- 在三个不同的数据集中对三个GNN架构进行基准测试.
- 每个架构-数据集组合培训300个模型,并进行超参数调整.
- 采用过量采样,不足采样和损失函数操纵来实现类不平衡.
主要成果:
- 过量采样技术在八个实验设置中表现出卓越的性能.
- 类平衡技术通常在不平衡的数据集上提高模型性能.
- 均衡技术的有效性取决于数据集的特点和具体的问题.
结论:
- GNNs显示了提高药物发现和设计效率的潜力.
- 过量采样是分子图数据集的有益技术,但需要进一步优化.
- 需要进行额外的研究,以探索其他类不平衡解决方案,并完善GNN在这个领域的应用.
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