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Found 8 papers, 6 papers with code
MutaPLM: Protein Language Modeling for Mutation Explanation and Engineering
To address these issues, we present MutaPLM, a unified framework for interpreting and navigating protein mutations with protein language models.
Learning Multi-view Molecular Representations with Structured and Unstructured Knowledge
To address these issues, we present MV-Mol, a molecular representation learning model that harvests multi-view molecular expertise from chemical structures, unstructured knowledge from biomedical texts, and structured knowledge from knowledge graphs.
LangCell: Language-Cell Pre-training for Cell Identity Understanding
Cell identity encompasses various semantic aspects of a cell, including cell type, pathway information, disease information, and more, which are essential for biologists to gain insights into its biological characteristics.
DeepCRE: Transforming Drug R&D via AI-Driven Cross-drug Response Evaluation
The fields of therapeutic application and drug research and development (R&D) both face substantial challenges, i. e., the therapeutic domain calls for more treatment alternatives, while numerous promising pre-clinical drugs have failed in clinical trials.
MolFM: A Multimodal Molecular Foundation Model
In this study, we introduce MolFM, a multimodal molecular foundation model designed to facilitate joint representation learning from molecular structures, biomedical texts, and knowledge graphs.
Ranked #10 on
Text-based de novo Molecule Generation
on ChEBI-20
Towards Unified AI Drug Discovery with Multiple Knowledge Modalities
In recent years, AI models that mine intrinsic patterns from molecular structures and protein sequences have shown promise in accelerating drug discovery.
DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection
Autonomous driving faces great safety challenges for a lack of global perspective and the limitation of long-range perception capabilities.
CogDL: A Comprehensive Library for Graph Deep Learning
In CogDL, we propose a unified design for the training and evaluation of GNN models for various graph tasks, making it unique among existing graph learning libraries.
