Learning Joint 2D & 3D Diffusion Models for Complete Molecule Generation

Designing new molecules is essential for drug discovery and material science. Recently, deep generative models that aim to model molecule distribution have made promising progress in narrowing down the chemical research space and generating high-fidelity molecules. However, current generative models only focus on modeling either 2D bonding graphs or 3D geometries, which are two complementary descriptors for molecules. The lack of ability to jointly model both limits the improvement of generation quality and further downstream applications. In this paper, we propose a new joint 2D and 3D diffusion model (JODO) that generates complete molecules with atom types, formal charges, bond information, and 3D coordinates. To capture the correlation between molecular graphs and geometries in the diffusion process, we develop a Diffusion Graph Transformer to parameterize the data prediction model that recovers the original data from noisy data. The Diffusion Graph Transformer interacts node and edge representations based on our relational attention mechanism, while simultaneously propagating and updating scalar features and geometric vectors. Our model can also be extended for inverse molecular design targeting single or multiple quantum properties. In our comprehensive evaluation pipeline for unconditional joint generation, the results of the experiment show that JODO remarkably outperforms the baselines on the QM9 and GEOM-Drugs datasets. Furthermore, our model excels in few-step fast sampling, as well as in inverse molecule design and molecular graph generation. Our code is provided in https://github.com/GRAPH-0/JODO

Realization of Bad Message Filtering System Based on kNN

Abstract: The transformation is implemented from short message to feature vector in this paper based on the ICTCLAS system. Then we use the kNN method to come to carry on the classified recognition to the message content, thus realizing to filter bad message effectually. The method has been testified effective in experiment

Realization of Bad Message Filtering System Based on kNN

Abstract: The transformation is implemented from short message to feature vector in this paper based on the ICTCLAS system. Then we use the kNN method to come to carry on the classified recognition to the message content, thus realizing to filter bad message effectually. The method has been testified effective in experiment

Towards Better Dynamic Graph Learning: New Architecture and Unified Library

We propose DyGFormer, a new Transformer-based architecture for dynamic graph learning. DyGFormer is conceptually simple and only needs to learn from nodes' historical first-hop interactions by: (1) a neighbor co-occurrence encoding scheme that explores the correlations of the source node and destination node based on their historical sequences; (2) a patching technique that divides each sequence into multiple patches and feeds them to Transformer, allowing the model to effectively and efficiently benefit from longer histories. We also introduce DyGLib, a unified library with standard training pipelines, extensible coding interfaces, and comprehensive evaluating protocols to promote reproducible, scalable, and credible dynamic graph learning research. By performing exhaustive experiments on thirteen datasets for dynamic link prediction and dynamic node classification tasks, we find that DyGFormer achieves state-of-the-art performance on most of the datasets, demonstrating its effectiveness in capturing nodes' correlations and long-term temporal dependencies. Moreover, some results of baselines are inconsistent with previous reports, which may be caused by their diverse but less rigorous implementations, showing the importance of DyGLib. All the used resources are publicly available at https://github.com/yule-BUAA/DyGLib.Comment: Accepted at NeurIPS 202

Predicting Temporal Sets with Deep Neural Networks

Given a sequence of sets, where each set contains an arbitrary number of elements, the problem of temporal sets prediction aims to predict the elements in the subsequent set. In practice, temporal sets prediction is much more complex than predictive modelling of temporal events and time series, and is still an open problem. Many possible existing methods, if adapted for the problem of temporal sets prediction, usually follow a two-step strategy by first projecting temporal sets into latent representations and then learning a predictive model with the latent representations. The two-step approach often leads to information loss and unsatisfactory prediction performance. In this paper, we propose an integrated solution based on the deep neural networks for temporal sets prediction. A unique perspective of our approach is to learn element relationship by constructing set-level co-occurrence graph and then perform graph convolutions on the dynamic relationship graphs. Moreover, we design an attention-based module to adaptively learn the temporal dependency of elements and sets. Finally, we provide a gated updating mechanism to find the hidden shared patterns in different sequences and fuse both static and dynamic information to improve the prediction performance. Experiments on real-world data sets demonstrate that our approach can achieve competitive performances even with a portion of the training data and can outperform existing methods with a significant margin.Comment: 9 pages, 6 figures, Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '2020

A review of high‐velocity impact on fiber‐reinforced textile composites: potential for aero engine applications

Considerable research has indicated that fiber-reinforced textile composites are significantly beneficial to the aerospace industry, especially aero engines, due to their high specific strength, specific stiffness, corrosion resistance, and fatigue resistance. However, damage caused by high-velocity impacts is a critical limitation factor in a wide range of applications. This paper presents an overview of the development, material characterizations, and applications of fiber-reinforced textile composites for aero engines. These textile composites are classified into four categories including two-dimensional (2D) woven composites, 2D braided composites, 3D woven composites, and 3D braided composites. The complex damage mechanisms of these composite materials due to high-velocity impacts are discussed in detail as well

A two-step lineage reprogramming strategy to generate functionally competent human hepatocytes from fibroblasts

Terminally differentiated cells can be generated by lineage reprogramming, which is, however, hindered by incomplete conversion with residual initial cell identity and partial functionality. Here, we demonstrate a new reprogramming strategy by mimicking the natural regeneration route, which permits generating expandable hepatic progenitor cells and functionally competent human hepatocytes. Fibroblasts were first induced into human hepatic progenitor-like cells (hHPLCs), which could robustly expand in vitro and efficiently engraft in vivo. Moreover, hHPLCs could be efficiently induced into mature human hepatocytes (hiHeps) in vitro, whose molecular identity highly resembles primary human hepatocytes (PHHs). Most importantly, hiHeps could be generated in large quantity and were functionally competent to replace PHHs for drug-metabolism estimation, toxicity prediction and hepatitis B virus infection modeling. Our results highlight the advantages of the progenitor stage for successful lineage reprogramming. This strategy is promising for generating other mature human cell types by lineage reprogramming.</p