We consider two popular approaches to Knowledge Graph Completion (KGC):
textual models that rely on textual entity descriptions, and structure-based
models that exploit the connectivity structure of the Knowledge Graph (KG).
Preliminary experiments show that these approaches have complementary
strengths: structure-based models perform well when the gold answer is easily
reachable from the query head in the KG, while textual models exploit
descriptions to give good performance even when the gold answer is not
reachable. In response, we explore ensembling as a way of combining the best of
both approaches. We propose a novel method for learning query-dependent
ensemble weights by using the distributions of scores assigned by individual
models to all candidate entities. Our ensemble baseline achieves
state-of-the-art results on three standard KGC datasets, with up to 6.8 pt MRR
and 8.3 pt Hits@1 gains over best individual models.

我们研究了两种知识图谱补全方法：依赖文本实体描述的文本模型和利用知识图谱的连接结构的基于结构的模型。我们通过集成的方式结合了这两种方法，提出了一种新颖的方法来学习查询相关的集成权重，并在三个标准知识图谱补全数据集上取得了最先进的结果。

文本和基于结构的模型的集成用于知识图谱补全

Ensembling Textual and Structure-Based Models for Knowledge Graph  Completion

Pre-trained models have been successful in many protein engineering tasks.
Most notably, sequence-based models have achieved state-of-the-art performance
on protein fitness prediction while structure-based models have been used
experimentally to develop proteins with enhanced functions. However, there is a
research gap in comparing structure- and sequence-based methods for predicting
protein variants that are better than the wildtype protein. This paper aims to
address this gap by conducting a comparative study between the abilities of
equivariant graph neural networks (EGNNs) and sequence-based approaches to
identify promising amino-acid mutations. The results show that our proposed
structural approach achieves a competitive performance to sequence-based
methods while being trained on significantly fewer molecules. Additionally, we
find that combining assay labelled data with structure pre-trained models
yields similar trends as with sequence pre-trained models.

通过使用改良版图神经网络和序列为基础的方法进行比较，本文旨在填补预测最优蛋白变体的结构和基序方法之间的研究缺口，结果显示出使用这种新方法在训练更少的分子数量时实现了接近基序方法的竞争性性能，此外，将结构预训练模型与测定的标记数据相结合，可以产生与序列预训练模型类似的趋势。

利用等变图神经网络预测蛋白质变异体

Predicting protein variants with equivariant graph neural networks

Structure-based models in the molecular sciences can be highly sensitive to
input geometries and give predictions with large variance under subtle
coordinate perturbations. We present an approach to mitigate this failure mode
by generating conformations that explicitly minimize uncertainty in a
predictive model. To achieve this, we compute differentiable estimates of
aleatoric \textit{and} epistemic uncertainties directly from learned
embeddings. We then train an optimizer that iteratively samples embeddings to
reduce these uncertainties according to their gradients. As our predictive
model is constructed as a variational autoencoder, the new embeddings can be
decoded to their corresponding inputs, which we call \textit{MoleCLUEs}, or
(molecular) counterfactual latent uncertainty explanations
\citep{antoran2020getting}. We provide results of our algorithm for the task of
predicting drug properties with maximum confidence as well as analysis of the
differentiable structure simulations.

本文提出了一种减少预测模型中不确定性的方法，即通过计算学习嵌入的 Aleatoric 和 Epistemic 不确定性的可微估计值，并训练一个优化器，迭代地从中减少这些不确定性，以生成一种名为 MoleCLUEs 的新嵌入，同时提供了在预测药物属性方面的结果和不同 iable 结构模拟的分析。