论文标题
高斯过程分子属性预测
Gaussian Process Molecule Property Prediction with FlowMO
论文作者
论文摘要
我们提出FlowMO：用于使用高斯过程的分子性质预测的开源Python库。 FlowMO建立在GPFLOW和RDKIT的基础上，使用户能够以良好的不确定性估计值进行预测，这是主动学习和分子设计应用的中心的输出。高斯工艺对于建模小分子数据集特别有吸引力，这是许多现实世界虚拟筛选活动的特征，在这些虚拟筛选活动中，高质量的实验数据很少。跨三个小数据集的计算实验表现出与深度学习方法相当的预测性能，但具有出色的不确定性校准。
We present FlowMO: an open-source Python library for molecular property prediction with Gaussian Processes. Built upon GPflow and RDKit, FlowMO enables the user to make predictions with well-calibrated uncertainty estimates, an output central to active learning and molecular design applications. Gaussian Processes are particularly attractive for modelling small molecular datasets, a characteristic of many real-world virtual screening campaigns where high-quality experimental data is scarce. Computational experiments across three small datasets demonstrate comparable predictive performance to deep learning methods but with superior uncertainty calibration.
