论文标题
一项关于可靠分子监督学习的基准研究
A benchmark study on reliable molecular supervised learning via Bayesian learning
论文作者
论文摘要
虚拟筛选旨在通过使用计算方法从化学库中找到理想的化合物。为此，通过机器学习，可以将可解释为预测概率的模型输出将是有益的，因为高预测分数对应于高正确的概率。在这项工作中，我们介绍了一项研究，以培训最近提出的贝叶斯学习算法的图形神经网络的预测性能和可靠性。我们的工作表明，贝叶斯学习算法可以对各种GNN架构和分类任务进行良好的预测。此外，我们展示了可靠预测对虚拟筛查的含义，贝叶斯学习可能会在寻找命中化合物方面取得更高的成功。
Virtual screening aims to find desirable compounds from chemical library by using computational methods. For this purpose with machine learning, model outputs that can be interpreted as predictive probability will be beneficial, in that a high prediction score corresponds to high probability of correctness. In this work, we present a study on the prediction performance and reliability of graph neural networks trained with the recently proposed Bayesian learning algorithms. Our work shows that Bayesian learning algorithms allow well-calibrated predictions for various GNN architectures and classification tasks. Also, we show the implications of reliable predictions on virtual screening, where Bayesian learning may lead to higher success in finding hit compounds.
