BEIJING, April 29,
2024 /PRNewswire/ -- In recent years, the rapid
development of artificial intelligence has introduced new possibilities across
numerous scientific disciplines. As an AI for Science pioneer, DP
Technology is continually collaborating with partners to explore
the transformative impact AI can bring to science. During its
DevDay held in Beijing on
April 12th, DP Technology showcased a
series of large science models, including the DPA large atomic
model[1], Uni-Mol 3D molecular model[2],
Uni-Fold protein folding model[3], Uni-RNA ribonucleic
acid model[4], and Uni-SMART large language model for
multimodal scientific literature[5] among others.
DPA
The rapid development of artificial intelligence (AI) is driving
significant changes in the field of atomic modeling, simulation,
and design. Inspired by recent advancements of large language
models, DP aspires to develop a similar foundational model for the
atomic domain. Developed by DP and collaborators, DPA is a large
pre-trained model for interatomic potential with attention
mechanism. The recently released DPA-2 model addresses the
limitations of single-source DFT data reliance in other pre-trained
atomistic models. DPA-2 covers ~100 elements in the periodic table.
In a perovskite study, Liu Shi's
team at Westlake University utilised the pre-trained DPA increased
the efficiency of force field development by 100x.
DPA-2 is also used in drug discovery. The latest version of
Uni-FEP (free energy perturbation) can now be powered by DPA's
pre-trained inter-atomic potential. Uni-FEP now utilizes the DPA-2
pre-trained model to optimize classical force field parameters
on-the-fly, providing enhanced free energy predictions. This
results in improved R^2 values and reduced RMSE.
Uni-Mol
Uni-Mol, a pre-trained 3D molecular representation learning
model (ICLR '23), now boasts an improved accuracy in predicting
these binding poses with over 77% of ligands achieving an RMSD
value under 2.0 Å and over 75% passing all quality checks. This
marks a substantial leap from the 62% accuracy of the previous
version, also eclipsing other known methods. It effectively tackled
common challenges like chirality inversions and steric clashes,
ensuring that predictions are not just accurate but also chemically
viable.
Based on Uni-Mol, VD-Gen[6], developed by DP and
collaborators, is capable of directly generating molecules with
high binding affinity within the protein pocket. VD-Gen accurately
predicts the elemental types and fine-grained atomic coordinates of
the generated molecules without the need to coarse-grain the atomic
coordinates into a grid, offering higher precision compared to
three-dimensional grid-based methods. Furthermore, VD-Gen can
efficiently generate all types of atoms and their coordinates
simultaneously, outperforming autoregressive generation models in
performance without being affected by the order of generation.
Uni-QSAR[7], built on the Uni-Mol model, is an
innovative tool for automated prediction of molecular properties.
It can rapidly and cost-effectively assess ADMET properties during
the early stages of drug development. This method utilizes the
three-dimensional structural information of molecules, combined
with computational chemistry and bioinformatics tools, to predict
the behavior of drug molecules in the body. DP demonstrated
benchmarks include 22 ADMET public datasets from the TDC Benchmark
and 30 activity datasets from the MoleculeACE Benchmark with
Chemprop, DeepAutoQSAR, and DeepPurpose as baselines. Uni-QSAR
achieved the best performance in 21 out of 22 tasks in the TDC
ADMET Benchmark tests and in 26 out of 30 tasks in the MoleculeACE
benchmark tests.
Uni-RNA
Uni-RNA is pre-trained on approximately one billion high-quality
RNA sequences, covering virtually all RNA space. By fine-tuning the
model across a broad range of downstream tasks, Uni-RNA achieved
leading results in all three RNA domains: RNA structure prediction,
mRNA sequence property prediction, and RNA function prediction.
Through a research conducted by DP, it is found that out of 10
RNA sequences generated by Uni-RNA, each one surpassed the
performance level of the commercially available vaccine sequences
from Moderna, while being generally comparable and sometimes
exceeding the level of BioNTech's commercial mRNA vaccine sequence.
This demonstrates that models like Uni-RNA not only hold immense
value for academic research but also possess significant potential
for industrial research and development applications.
Uni-Fold
Protein structure modeling is a prerequisite for structure-based
drug development. Once a preliminary structure is obtained, further
refining and optimizing key structural regions is crucial for
ensuring the accuracy of subsequent research.
Uni-Fold is the first protein structure prediction tool to fully
open-source its training and inference code. It supports the
structural prediction of polymeric protein systems and achieves top
industry accuracy in prediction results under the same training
datasets.
Uni-SMART
Uni-SMART (Science Multimodal Analysis and Research Transformer)
tackles the urgent need for new solutions that can fully understand
and analyze multimodal content in scientific literature. Indeed,
many LLMs can already ingest PDF, but they often struggle to digest
and interpret the rich information encapsulated within charts,
graphs, and molecular structures embedded within those
documents.
Through rigorous quantitative evaluation, Uni-SMART demonstrates
significant performance gain in interpreting and analyzing
multimodal contents in scientific documents, such as tables,
charts, molecular structures, and chemical reactions, compared with
other leading tools, such as GPT-4 and Gemini.
Industrial Software for Drug Discovery, Battery Development
and beyond
Advancing AI for Science, DP Technology has developed a suite of
industry applications based on its large science models and advanced
algorithms. This suite includes the innovative Bohrium® Scientific
Research Space, Hermite® Computational Drug Design Platform,
RiDYMO® Dynamics Platform, and Piloteye® Battery Design Automation
Platform. Together, these platforms support a robust foundation for
industrial innovation within an open ecosystem for AI in science,
fostering advancements in key areas such as drug discovery, energy,
materials science, and information technology.
Open Science initiative
At DevDay, DP Technology joined forces with industry leaders
such as CATL, Yunnan Baiyao, Alibaba
Cloud, Tencent Cloud, Volcano
Engine, China Unicom etc, to initiate an AI for Science open
science ecosystem. This cross-industry collaboration aims to
integrate the strengths of each party in artificial intelligence,
cloud computing, and industry applications to propel innovation.
The initiative aims to accelerate the open-source development of
datasets, algorithms, code and pre-trained models.
Sun Weijie, founder and CEO of DP Technology, stated, "The
launch of large science models is our firm commitment to advancing
scientific and industrial innovation. With this series of
scientific large models, we are not only able to accelerate the
process of scientific research and product development but also
increase the success rate of R&D, bringing disruptive impacts
to drug discovery, battery development and beyond."
About DP Technology
DP Technology is a global leader in the "AI for Science"
research paradigm, where AI learns scientific principles and
data, then tackles key challenges in scientific research and
industrial R&D.
DP's commitment to interdisciplinary research has led to the
creation of the "DP Particle
Universe," an array of pre-trained large science
models designed to bridge foundational research with
practical industrial applications. DP's software suite
includes the Bohrium® Scientific Research Space, Hermite®
Computational Drug Design Platform, RiDYMO® Dynamics Platform,
and Piloteye® Battery Design Automation Platform. Together,
these platforms form a robust foundation for industrial innovation
and an open ecosystem for AI in science, fostering
advancements in key areas such as drug discovery, energy,
material science, and information technology.
More:https://www.dp.tech/en
Business:bd@dp.tech
Media:pr@dp.tech
Reference:
[1] https://arxiv.org/abs/2312.15492
[2] https://openreview.net/forum?id=6K2RM6wVqKu
[3]
https://www.biorxiv.org/content/10.1101/2022.08.04.502811v3.full
[4]
https://www.biorxiv.org/content/10.1101/2023.07.11.548588v1
[5] https://arxiv.org/pdf/2403.10301.pdf
[6] https://arxiv.org/abs/2302.05847
[7] https://arxiv.org/abs/2304.12239
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SOURCE DP Technology