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DP Technology DevDay 2024 Showcases Large Science Models and Announces Open Science Initiative
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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.”
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QingSong Health Chairman Speaks at Global Ethics Forum in Geneva: Advancing Ethical Leadership in Healthcare
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At the Global Ethics Forum, a leading platform that convenes stakeholders from various sectors to discuss peace, sustainability, and ethical leadership, the Chairman of QingSong Health, China’s premier healthcare solutions technology platform, delivered a compelling keynote address. The Forum serves as a multisectoral space for dialogue among institutions, policymakers, and experts, aimed at envisioning a future that emphasizes ethical engagement for better global outcomes.
Founded in 2014, QingSong Health is dedicated to providing integrated healthcare solutions in China, serving over 30 million families. The platform combines health education, advanced technologies, and effective health management to offer a comprehensive range of services, including health consultations, disease prevention, and rehabilitation care.
In her address, the Chairman shared three key insights on the role of Artificial Intelligence (AI) in healthcare:
AI as a Bridge Builder: The Chairman underscored AI’s role in enhancing access to healthcare, noting a staggering growth of over 200% in telemedicine consultations globally. This surge connects individuals to specialized medical expertise, addressing disparities in access to care. QingSong Health’s AI tool, Dr. GPT, exemplifies this innovation by efficiently handling inquiries about 3,800 common diseases, ensuring that critical health information is readily accessible to all users.
AI in Prevention: She stressed the importance of AI-driven early intervention, which can reduce healthcare costs by up to 30% while significantly improving patient outcomes. By integrating traditional healthcare wisdom with cutting-edge technology, QingSong Health’s innovative health detectors empower individuals to engage in proactive wellness and preventive care, ultimately aiming to avert health issues before they arise.
Ethical AI:The Chairman highlighted that ethical considerations are paramount in AI development. Guided by Confucian values that emphasize empathy and respect for human dignity, QingSong Health is committed to creating AI solutions that protect privacy and promote ethical standards. The company collaborates with Globethics to advocate for the responsible use of AI in healthcare, ensuring alignment with global best practices for ethical engagement.
In conclusion, the Chairman asserted that “AI offers a pathway to a healthier future, bridging healthcare gaps while reinforcing ethical standards.” She called on global leaders to foster ongoing dialogue inspired by the Global Ethics Forum’s emphasis on inclusive engagement and sustainable development, urging them to work collaboratively towards a brighter future for all.
SOURCE Qingsong Health Corporation
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ATFX Ranks 4th Globally in Q2 2024 Trading Volume with a Remarkable 43.75% Year-Over-Year Growth
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Recently, one of the most respected media firms in the financial services industry, Finance Magnates released its latest data report for the Q2 2024, showing ATFX achieved an impressive global ranking of 4th in trading volume on the MT4/MT5 platforms, with a total volume reaching $765.1 billion. This milestone underscores ATFX’s strong competitive edge in the global financial markets and highlights significant growth in trading activity.
Compared to the previous quarter, ATFX’s trading volume in Q2 2024 saw a significant increase of 22.45%, and an astonishing 43.75% year-on-year growth. These impressive growth rates are not only a solid foundation for the company’s continued success but also a vivid reflection of its expanding business footprint and growing market influence.
Breaking down the product trends, the precious metals category grew by 26.2% compared to Q1 2024 and 79.2% compared to Q2 2023. The indices category saw a 99.38% increase compared to Q1 2024 and a 14.58% increase compared to Q2 2023. The stocks category experienced a staggering 457.82% growth compared to Q1 2024 and a 167.89% increase compared to Q2 2023. The energy category grew by 23.03% compared to Q1 2024, among others.
For a long time, ATFX’s trading volume has consistently ranked among the top ten globally, a testament to the brand’s strength and market competitiveness. This outstanding achievement also reflects our years of deep cultivation in the financial market, through building an integrated model of investment education, services, and tools to serve global clients. Looking ahead, ATFX will continue to prioritize customer needs, providing comprehensive and high-quality trading support services.
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New US Consensus Report supports Nevisense
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SciBase Holding AB (“SciBase”) (STO: SCIB), pioneering prevention and prediction in dermatology announces that key US clinicians and scientists reached consensus on Nevisense and how it can significantly enhance the diagnostic assessment and clinical decision-making for early melanoma at point of care. Nevisense is an AI-driven, non-invasive technology, and the only FDA-approved device for early skin cancer detection.
The consensus report evaluated several technologies for melanoma diagnosis and supports the use of Nevisense for its ability to significantly enhance clinician’s diagnostic assessment of atypical moles by non-invasively providing them with critical information at point of care. The report was published in the Journal of Drugs in Dermatology (JDD) – a peer-reviewed, dermatology journal. The report co-authors included top US clinicians such as Seemal R. Desai, MD, Keyvan Nouri, MD, Aaron S. Farberg, MD, Gary Goldenberg, MD, Mark Lebwohl, MD, and Darrell Rigel, MD, MS, Brian Berman, MD, PhD, Brad Glick, DO, MPH, Mark Nestor, MD, PhD, MBA, and Theodore Rosen, MD.
“I am so excited that this renowned panel of leading dermatologists has endorsed Nevisense as an important tool for early detection of melanoma. This is a significant step forward for SciBase in our US commercialization process,” says Pia Renaudin, CEO of SciBase.
“Melanoma is one of the top 5 most common cancers in the US and is the main cause of skin cancer deaths in the United States. Early detection is critical to survival and one of the few ways to improve clinical outcomes for patients. With melanoma, timing and the earliest detection possible can make a significant positive impact on survival rates and outcomes. For these reasons, we as clinicians can provide our patients with AI-powered Nevisense, which is an advanced technology that enhances early detection while easily integrating at the point-of-care,” said Dr. Darrell Rigel, board-certified dermatologist, Clinical Professor of Dermatology Ronald O. Perelman Department of Dermatology, NYU Grossman School of Medicine, Adjunct Professor, UT Southwestern Medical School, Consultant Dermatologist, Cooper Clinic. Dr. Rigel served as President of the American Academy of Dermatology, the American Academy of Dermatology Association, the American Society for Dermatologic Surgery and the American Dermatological Association, as a Director of the American Board of Dermatology, and is the current Chair of SkinPAC (the American Academy of Dermatology Association’s political action committee).
“Nevisense is a powerful combination of human intelligence and artificial intelligence working together to improve clinical outcomes. At Goldenberg Dermatology, we strive to provide our patients with the highest standard of care with the most advanced technology. Nevisense helps clinicians identify more atypical pigmented skin lesions, which in turn helps increase detection rates. The clinically proven and validated science, and ease of practical integration were all key to reaching consensus,” said Dr. Gary Goldenberg, board certified dermatologist and dermatopathologist, CEO and founder of Goldenberg Dermatology PC, and Assistant Clinical Professor of Dermatology at the Icahn School of Medicine at Mount Sinai Hospital in New York City.
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