Search Results for author:
Found 11 papers, 6 papers with code
ProtFAD: Introducing function-aware domains as implicit modality towards protein function perception
Protein function prediction is currently achieved by encoding its sequence or structure, where the sequence-to-function transcendence and high-quality structural data scarcity lead to obvious performance bottlenecks.
A Self-feedback Knowledge Elicitation Approach for Chemical Reaction Predictions
The task of chemical reaction predictions (CRPs) plays a pivotal role in advancing drug discovery and material science.
Ranked #1 on
Chemical Reaction Prediction
on Mol-Instruction
Impact of Domain Knowledge and Multi-Modality on Intelligent Molecular Property Prediction: A Systematic Survey
We discover that integrating molecular information significantly improves molecular property prediction (MPP) for both regression and classification tasks.
Scientific Language Modeling: A Quantitative Review of Large Language Models in Molecular Science
Efficient molecular modeling and design are crucial for the discovery and exploration of novel molecules, and the incorporation of deep learning methods has revolutionized this field.
Compact Binary Systems Waveform Generation with Generative Pre-trained Transformer
Space-based gravitational wave (GW) detection is one of the most anticipated GW detection projects in the next decade, which promises to detect abundant compact binary systems.
3D-Mol: A Novel Contrastive Learning Framework for Molecular Property Prediction with 3D Information
Molecular property prediction, crucial for early drug candidate screening and optimization, has seen advancements with deep learning-based methods.
Dilated convolutional neural network for detecting extreme-mass-ratio inspirals
The detection of Extreme Mass Ratio Inspirals (EMRIs) is intricate due to their complex waveforms, extended duration, and low signal-to-noise ratio (SNR), making them more challenging to be identified compared to compact binary coalescences.
GIT-Mol: A Multi-modal Large Language Model for Molecular Science with Graph, Image, and Text
Large language models have made significant strides in natural language processing, enabling innovative applications in molecular science by processing textual representations of molecules.
Ranked #1 on
Image Captioning
on ChEBI-20
MLGWSC-1: The first Machine Learning Gravitational-Wave Search Mock Data Challenge
The final of the 4 provided datasets contained real noise from the O3a observing run and signals up to a duration of 20 seconds with the inclusion of precession effects and higher order modes.
Space-based gravitational wave signal detection and extraction with deep neural network
Space-based gravitational wave (GW) detectors will be able to observe signals from sources that are otherwise nearly impossible from current ground-based detection.
AIPerf: Automated machine learning as an AI-HPC benchmark
The de facto HPC benchmark LINPACK can not reflect AI computing power and I/O performance without representative workload.
