23 research outputs found
Communicative Message Passing for Inductive Relation Reasoning
Relation prediction for knowledge graphs aims at predicting missing
relationships between entities. Despite the importance of inductive relation
prediction, most previous works are limited to a transductive setting and
cannot process previously unseen entities. The recent proposed subgraph-based
relation reasoning models provided alternatives to predict links from the
subgraph structure surrounding a candidate triplet inductively. However, we
observe that these methods often neglect the directed nature of the extracted
subgraph and weaken the role of relation information in the subgraph modeling.
As a result, they fail to effectively handle the asymmetric/anti-symmetric
triplets and produce insufficient embeddings for the target triplets. To this
end, we introduce a \textbf{C}\textbf{o}mmunicative \textbf{M}essage
\textbf{P}assing neural network for \textbf{I}nductive re\textbf{L}ation
r\textbf{E}asoning, \textbf{CoMPILE}, that reasons over local directed subgraph
structures and has a vigorous inductive bias to process entity-independent
semantic relations. In contrast to existing models, CoMPILE strengthens the
message interactions between edges and entitles through a communicative kernel
and enables a sufficient flow of relation information. Moreover, we demonstrate
that CoMPILE can naturally handle asymmetric/anti-symmetric relations without
the need for explosively increasing the number of model parameters by
extracting the directed enclosing subgraphs. Extensive experiments show
substantial performance gains in comparison to state-of-the-art methods on
commonly used benchmark datasets with variant inductive settings.Comment: Accepted by AAAI-202
Incorporating Retrieval-based Causal Learning with Information Bottlenecks for Interpretable Graph Neural Networks
Graph Neural Networks (GNNs) have gained considerable traction for their
capability to effectively process topological data, yet their interpretability
remains a critical concern. Current interpretation methods are dominated by
post-hoc explanations to provide a transparent and intuitive understanding of
GNNs. However, they have limited performance in interpreting complicated
subgraphs and can't utilize the explanation to advance GNN predictions. On the
other hand, transparent GNN models are proposed to capture critical subgraphs.
While such methods could improve GNN predictions, they usually don't perform
well on explanations. Thus, it is desired for a new strategy to better couple
GNN explanation and prediction. In this study, we have developed a novel
interpretable causal GNN framework that incorporates retrieval-based causal
learning with Graph Information Bottleneck (GIB) theory. The framework could
semi-parametrically retrieve crucial subgraphs detected by GIB and compress the
explanatory subgraphs via a causal module. The framework was demonstrated to
consistently outperform state-of-the-art methods, and to achieve 32.71\% higher
precision on real-world explanation scenarios with diverse explanation types.
More importantly, the learned explanations were shown able to also improve GNN
prediction performance
Mixup-Augmented Meta-Learning for Sample-Efficient Fine-Tuning of Protein Simulators
Molecular dynamics simulations have emerged as a fundamental instrument for
studying biomolecules. At the same time, it is desirable to perform simulations
of a collection of particles under various conditions in which the molecules
can fluctuate. In this paper, we explore and adapt the soft prompt-based
learning method to molecular dynamics tasks. Our model can remarkably
generalize to unseen and out-of-distribution scenarios with limited training
data. While our work focuses on temperature as a test case, the versatility of
our approach allows for efficient simulation through any continuous dynamic
conditions, such as pressure and volumes. Our framework has two stages: 1)
Pre-trains with data mixing technique, augments molecular structure data and
temperature prompts, then applies a curriculum learning method by increasing
the ratio of them smoothly. 2) Meta-learning-based fine-tuning framework
improves sample-efficiency of fine-tuning process and gives the soft
prompt-tuning better initialization points. Comprehensive experiments reveal
that our framework excels in accuracy for in-domain data and demonstrates
strong generalization capabilities for unseen and out-of-distribution samples
Identifying Structure-Property Relationships through SMILES Syntax Analysis With Self-Attention Mechanism
Recognizing
substructures and their relations embedded in a molecular structure
representation is a key process for structure-activity
or structure-property relationship (SAR/SPR) studies. A molecular structure can
be either explicitly represented as a connection table (CT) or linear notation,
such as SMILES, which is a language describing the connectivity of atoms in the
molecular structure. Conventional SAR/SPR approaches rely on partitioning the
CT into a set of predefined substructures as structural descriptors. In this
work, we propose a new method to identifying SAR/SPR through linear notation
(for example, SMILES) syntax analysis with self-attention mechanism, an
interpretable deep learning architecture. The method has been evaluated by
predicting chemical property, toxicology, and bioactivity
from experimental data sets. Our results demonstrate that the method yields superior performance
comparing with state-of-the-art methods. Moreover, the method can produce
chemically interpretable results, which can be used for
a chemist to design, and synthesize the activity/property improved compounds.</p
SyntaLinker: Automatic Fragment Linking with Deep Conditional Transformer Neural Networks
Fragment
based drug design represents a promising drug discovery paradigm complimentary
to the traditional HTS based lead generation strategy. How to link fragment
structures to increase compound affinity is remaining a challenge task in this
paradigm. Hereby a novel deep generative model (SyntaLinker) for linking fragments
is developed with the potential for applying in the fragment-based lead
generation scenario. The state-of-the-art transformer architecture was employed
to learn the linker grammar and generate novel linker. Our results show that,
given starting fragments and user customized linker constraints, our SyntaLinker model can design abundant drug-like molecules fulfilling these constraints and
its performance was superior to other reference models. Moreover, several
examples were showcased that SyntaLinkercan be useful tools for carrying out
drug design tasks such as fragment linking, lead optimization and scaffold
hopping
Publisher Correction: Predicting drug–protein interaction using quasi-visual question answering system (Nature Machine Intelligence, (2020), 2, 2, (134-140), 10.1038/s42256-020-0152-y)
In the version of this Article originally published, the placeholder text ‘Please add some text here’ after the section heading ‘Experiments’ was mistakenly not updated; this text should have read ‘In this section, we will introduce the employed datasets and experiments to indicate the performance of our DrugVQA model’. This has now been corrected
Predicting Retrosynthetic Reaction using Self-Corrected Transformer Neural Networks
Synthesis planning is the process of recursively decomposing target molecules into available precursors. Computer-aided retrosynthesis can potentially assist chemists in designing synthetic routes, but at present it is cumbersome and provides results of dissatisfactory quality. In this study, we develop a template-free self-corrected retrosynthesis predictor (SCROP) to perform a retrosynthesis prediction task trained by using the Transformer neural network architecture. In the method, the retrosynthesis planning is converted as a machine translation problem between molecular linear notations of reactants and the products. Coupled with a neural network-based syntax corrector, our method achieves an accuracy of 59.0% on a standard benchmark dataset, which increases >21% over other deep learning methods, and >6% over template-based methods. More importantly, our method shows an accuracy 1.7 times higher than other state-of-the-art methods for compounds not appearing in the training set.</p