In drug discovery, aqueous solubility is an important pharmacokinetic
property which affects absorption and assay availability of drug. Thus, in
silico prediction of solubility has been studied for its utility in virtual
screening and lead optimization. Recently, machine learning (ML) methods using
experimental data has been popular because physics-based methods like quantum
mechanics and molecular dynamics are not suitable for high-throughput tasks due
to its computational costs. However, ML method can exhibit over-fitting problem
in a data-deficient condition, and this is the case for most chemical property
datasets. In addition, ML methods are regarded as a black box function in that
it is difficult to interpret contribution of hidden features to outputs,
hindering analysis and modification of structure-activity relationship. To deal
with mentioned issues, we developed Bayesian graph neural networks (GNNs) with
the self-attention readout layer. Unlike most GNNs using self-attention in node
updates, self-attention applied at readout layer enabled a model to improve
prediction performance as well as to identify atom-wise importance, which can
help lead optimization as exemplified for three FDA-approved drugs. Also,
Bayesian inference enables us to separate more or less accurate results
according to uncertainty in solubility prediction task We expect that our
accurate, reliable and interpretable model can be used for more careful
decision-making and various applications in the development of drugs.

利用贝叶斯图神经网络和自我注意力机制来预测药物的溶解度，该模型能识别原子贡献，有助于药物研发中的结构活性关系分析和优化决策。

使用注意力机制和贝叶斯学习精准、可靠和可解释的药物分子溶解度预测

Accurate, reliable and interpretable solubility prediction of druglike molecules with attention pooling and Bayesian learning

Meta-learning for few-shot learning allows a machine to leverage previously
acquired knowledge as a prior, thus improving the performance on novel tasks
with only small amounts of data. However, most mainstream models suffer from
catastrophic forgetting and insufficient robustness issues, thereby failing to
fully retain or exploit long-term knowledge while being prone to cause severe
error accumulation. In this paper, we propose a novel Continual Meta-Learning
approach with Bayesian Graph Neural Networks (CML-BGNN) that mathematically
formulates meta-learning as continual learning of a sequence of tasks. With
each task forming as a graph, the intra- and inter-task correlations can be
well preserved via message-passing and history transition. To remedy
topological uncertainty from graph initialization, we utilize Bayes by Backprop
strategy that approximates the posterior distribution of task-specific
parameters with amortized inference networks, which are seamlessly integrated
into the end-to-end edge learning. Extensive experiments conducted on the
miniImageNet and tieredImageNet datasets demonstrate the effectiveness and
efficiency of the proposed method, improving the performance by 42.8% compared
with state-of-the-art on the miniImageNet 5-way 1-shot classification task.

提出了一种新颖的连续元学习方法，使用贝叶斯图神经网络 (CML-BGNN) 将元学习数学公式化为一系列任务的连续学习，在图形上保留任务内部和任务之间的相关性，利用 Amortized inference networks 解决了图形初始化的拓扑不确定性，提高了 minImageNet  5-way 1-shot 分类任务的分类性能。