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Found 12 papers, 7 papers with code
A Simple yet Effective DDG Predictor is An Unsupervised Antibody Optimizer and Explainer
For the target antibody, we propose a novel Mutation Explainer to learn mutation preferences, which accounts for the marginal benefit of each mutation per residue.
Intelligent System for Automated Molecular Patent Infringement Assessment
Automated drug discovery offers significant potential for accelerating the development of novel therapeutics by substituting labor-intensive human workflows with machine-driven processes.
Learning to Model Graph Structural Information on MLPs via Graph Structure Self-Contrasting
In this paper, we rethink the roles played by graph structural information in graph data training and identify that message passing is not the only path to modeling structural information.
CBGBench: Fill in the Blank of Protein-Molecule Complex Binding Graph
To broaden the scope, we have adapted these models to a range of tasks essential in drug design, which are considered sub-tasks within the graph fill-in-the-blank tasks.
A Teacher-Free Graph Knowledge Distillation Framework with Dual Self-Distillation
As a result, TGS enjoys the benefits of graph topology awareness in training but is free from data dependency in inference.
$R^3$: "This is My SQL, Are You With Me?" A Consensus-Based Multi-Agent System for Text-to-SQL Tasks
Large Language Models (LLMs) have demonstrated strong performance on various tasks.
Uni-QSAR: an Auto-ML Tool for Molecular Property Prediction
Recently deep learning based quantitative structure-activity relationship (QSAR) models has shown surpassing performance than traditional methods for property prediction tasks in drug discovery.
Using Context-to-Vector with Graph Retrofitting to Improve Word Embeddings
Although contextualized embeddings generated from large-scale pre-trained models perform well in many tasks, traditional static embeddings (e. g., Skip-gram, Word2Vec) still play an important role in low-resource and lightweight settings due to their low computational cost, ease of deployment, and stability.
Ranked #1 on
Word Similarity
on WS353
Teaching Yourself: Graph Self-Distillation on Neighborhood for Node Classification
Despite their great academic success, Multi-Layer Perceptrons (MLPs) remain the primary workhorse for practical industrial applications.
Exploring Generative Neural Temporal Point Process
While lots of previous works have focused on `goodness-of-fit' of TPP models by maximizing the likelihood, their predictive performance is unsatisfactory, which means the timestamps generated by models are far apart from true observations.
STONet: A Neural-Operator-Driven Spatio-temporal Network
Graph-based spatio-temporal neural networks are effective to model the spatial dependency among discrete points sampled irregularly from unstructured grids, thanks to the great expressiveness of graph neural networks.
