Inductive Logical Query Answering in Knowledge Graphs

Formulating and answering logical queries is a standard communication interface for knowledge graphs (KGs). Alleviating the notorious incompleteness of real-world KGs, neural methods achieved impressive results in link prediction and complex query answering tasks by learning representations of entities, relations, and queries. Still, most existing query answering methods rely on transductive entity embeddings and cannot generalize to KGs containing new entities without retraining the entity embeddings. In this work, we study the inductive query answering task where inference is performed on a graph containing new entities with queries over both seen and unseen entities. To this end, we devise two mechanisms leveraging inductive node and relational structure representations powered by graph neural networks (GNNs). Experimentally, we show that inductive models are able to perform logical reasoning at inference time over unseen nodes generalizing to graphs up to 500% larger than training ones. Exploring the efficiency--effectiveness trade-off, we find the inductive relational structure representation method generally achieves higher performance, while the inductive node representation method is able to answer complex queries in the inference-only regime without any training on queries and scales to graphs of millions of nodes. Code is available at https://github.com/DeepGraphLearning/InductiveQE.Comment: Accepted at NeurIPS 202

A Group Symmetric Stochastic Differential Equation Model for Molecule Multi-modal Pretraining

Molecule pretraining has quickly become the go-to schema to boost the performance of AI-based drug discovery. Naturally, molecules can be represented as 2D topological graphs or 3D geometric point clouds. Although most existing pertaining methods focus on merely the single modality, recent research has shown that maximizing the mutual information (MI) between such two modalities enhances the molecule representation ability. Meanwhile, existing molecule multi-modal pretraining approaches approximate MI based on the representation space encoded from the topology and geometry, thus resulting in the loss of critical structural information of molecules. To address this issue, we propose MoleculeSDE. MoleculeSDE leverages group symmetric (e.g., SE(3)-equivariant and reflection-antisymmetric) stochastic differential equation models to generate the 3D geometries from 2D topologies, and vice versa, directly in the input space. It not only obtains tighter MI bound but also enables prosperous downstream tasks than the previous work. By comparing with 17 pretraining baselines, we empirically verify that MoleculeSDE can learn an expressive representation with state-of-the-art performance on 26 out of 32 downstream tasks

Tetrabenazine is neuroprotective in Huntington's disease mice

<p>Abstract</p> <p>Background</p> <p>Huntington's disease (HD) is a neurodegenerative disorder caused by a polyglutamine (polyQ) expansion in Huntingtin protein (Htt). PolyQ expansion in Httexp causes selective degeneration of striatal medium spiny neurons (MSN) in HD patients. A number of previous studies suggested that dopamine signaling plays an important role in HD pathogenesis. A specific inhibitor of vesicular monoamine transporter (VMAT2) tetrabenazine (TBZ) has been recently approved by Food and Drug Administration for treatment of HD patients in the USA. TBZ acts by reducing dopaminergic input to the striatum.</p> <p>Results</p> <p>In previous studies we demonstrated that long-term feeding with TBZ (combined with L-Dopa) alleviated the motor deficits and reduced the striatal neuronal loss in the yeast artificial chromosome transgenic mouse model of HD (YAC128 mice). To further investigate a potential beneficial effects of TBZ for HD treatment, we here repeated TBZ evaluation in YAC128 mice starting TBZ treatment at 2 months of age ("early" TBZ group) and at 6 months of age ("late" TBZ group). In agreement with our previous studies, we found that both "early" and "late" TBZ treatments alleviated motor deficits and reduced striatal cell loss in YAC128 mice. In addition, we have been able to recapitulate and quantify depression-like symptoms in TBZ-treated mice, reminiscent of common side effects observed in HD patients taking TBZ.</p> <p>Conclusions</p> <p>Our results further support therapeutic value of TBZ for treatment of HD but also highlight the need to develop more specific dopamine antagonists which are less prone to side-effects.</p