Graphormer is a general-purpose deep learning backbone for molecular modeling.
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Updated
Jun 7, 2024 - Python
Graphormer is a general-purpose deep learning backbone for molecular modeling.
PaddleScience is SDK and library for developing AI-driven scientific computing applications based on PaddlePaddle.
ImageMol is a molecular image-based pre-training deep learning framework for computational drug discovery.
AI4Science: Python/Matlab implementation of online and window dynamic mode decomposition (Online DMD and Window DMD)
Code and datasets for paper "K2: A Foundation Language Model for Geoscience Knowledge Understanding and Utilization" in WSDM-2024
Synthetic lethality (SL) is a promising gold mine for the discovery of anti-cancer drug targets. KG4SL is the first graph neural network (GNN)-based model that uses knowledge graph for SL prediction.
Geom3D: Geometric Modeling on 3D Structures, NeurIPS 2023
MolDiff: Addressing the Atom-Bond Inconsistency Problem in 3D Molecule Diffusion Generation
[NeurIPS 2023] This is the official code for the paper "TPSR: Transformer-based Planning for Symbolic Regression"
ChatCell: Facilitating Single-Cell Analysis with Natural Language
[ICLR 2022] The implementation for the paper "Equivariant Graph Mechanics Networks with Constraints".
[AAAI 2023] The implementation for the paper "Energy-Motivated Equivariant Pretraining for 3D Molecular Graphs"
AI4Science: Efficient data-driven Online Model Learning (OML) / system identification and control
GPT (Generative Pre-trained Transformer) for de novo molecular design by enforcing specified targets
[ICML 2024] LSH-Based Efficient Point Transformer (HEPT)
[ICLR 2024 Spotlight] This is the official code for the paper "SNIP: Bridging Mathematical Symbolic and Numeric Realms with Unified Pre-training"
The official code for "TaxDiff: Taxonomic-Guided Diffusion Model for Protein Sequence Generation"
Uncover meaningful structures of latent spaces learned by generative models with flows!
[NeurIPS 2022] The implementation for the paper "Equivariant Graph Hierarchy-Based Neural Networks".
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