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Uni-MolA Large-Scale Pre-trained Model for 3D Molecular Representation Learning
  1. Pre-training on billions of 3D molecular structures for representation learning, specifically tailored for property prediction and structural optimization design.
  2. Encompassing fields such as pharmaceuticals, materials, and chemical engineering, achieving top-class performance in physicochemical, quantitative, and biochemical property predictions.
  3. Supporting efficient molecular design, empowering practical domains like drug discovery, material optimization, and energy production.
Uni-Mol
Uni-RNAA Large-Scale Pre-trained Model For Nucleic Acids
  1. We have collected and organized the largest available RNA sequence database to train the most extensive pretraining model for nucleic acids.
  2. Our model possesses a powerful ability to represent functional structures, consistently outperforming existing algorithms in all known tasks.
  3. Uni-RNA comprehensively empowers mRNA and nucleic acid drug research and development, accelerating the advancement of new therapeutic approaches.
Uni-RNA
DPA-1A Transferable Interatomic Potential Pre-trained Model
  1. The world's first pre-trained model covers 70 elements from the periodic table.
  2. Possesses strong transferability, enabling the fine-tuning of a reliable potential function model with minimal samples on a new system.
  3. Significantly reduces data dependency for model construction.
DPA-1
Uni-FoldA High-Precision Universal Protein Folding Model
  1. Unveiling the first open-source Fold model in the domestic market with training code and database, boasting accuracy comparable to AlphaFold2.
  2. Efficient and precise inference capabilities, supporting complex real-world scenarios such as large proteins, protein complexes, symmetrical proteins, and more.
Uni-Fold
Uni-DockA High-Performance Docking Engine for Large-Scale Database Virtual Screening With GPU
  1. Maintaining comparable accuracy to traditional molecular docking, Uni-Dock achieves an acceleration rate of over 1600 times on NVIDIA V100 GPU compared to AutoDock Vina's single-core calculation. This performance is more than 10 times faster than other GPU-accelerated molecular docking engines.
  2. Uni-Dock can complete virtual screening of over 38.2 million molecular databases in less than 12 hours, bringing the virtual screening of large-scale databases, with tens of millions of molecules, into a practical, accessible, and reliable era.
Uni-Dock