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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.”
The post DP Technology DevDay 2024 Showcases Large Science Models and Announces Open Science Initiative appeared first on HIPTHER Alerts.

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The EU AI Act Finalized: Implications for Employment Law and Compliance

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The European Union (EU) has finalized the AI Act, a comprehensive regulatory framework designed to address the ethical, legal, and societal implications of artificial intelligence (AI). This landmark legislation has significant implications for employment law and compliance, affecting how organizations develop, deploy, and manage AI technologies. This article explores the key provisions of the EU AI Act and its impact on employment law and compliance.
Overview of the EU AI Act
The EU AI Act aims to ensure that AI technologies are developed and used in a manner that respects fundamental rights, promotes transparency, and mitigates risks. The legislation categorizes AI systems into different risk levels, imposing varying requirements based on the potential impact on individuals and society.
Key Provisions:

Risk-Based Approach: AI systems are categorized into four risk levels: unacceptable risk, high risk, limited risk, and minimal risk. High-risk AI systems are subject to stricter regulatory requirements.
Transparency and Accountability: Organizations must ensure transparency and accountability in the development and deployment of AI systems, including providing clear information about the functioning and decision-making processes.
Human Oversight: High-risk AI systems must incorporate human oversight to ensure that AI decisions can be reviewed and contested.
Data Governance: The Act imposes strict data governance requirements to ensure the quality, accuracy, and fairness of data used in AI systems.

Implications for Employment Law
The EU AI Act has significant implications for employment law, affecting how organizations use AI technologies in hiring, performance evaluation, and workplace monitoring.
Key Implications:

Fair Hiring Practices: AI systems used in hiring and recruitment must ensure fairness and non-discrimination, avoiding biases that could adversely impact candidates.
Performance Evaluation: AI-driven performance evaluation systems must be transparent and provide employees with the opportunity to contest decisions.
Workplace Monitoring: AI technologies used for workplace monitoring must respect employees’ privacy and comply with data protection regulations.

Compliance Requirements
Organizations must comply with the EU AI Act’s requirements to ensure the ethical and legal use of AI technologies. Compliance involves several key steps and considerations.
Compliance Steps:

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Risk Assessment: Conducting a thorough risk assessment to categorize AI systems and determine the applicable regulatory requirements.
Transparency Measures: Implementing measures to ensure transparency in AI decision-making processes, including clear documentation and communication with affected individuals.
Human Oversight: Establishing mechanisms for human oversight and intervention in AI decision-making processes, particularly for high-risk AI systems.
Data Management: Ensuring robust data governance practices to maintain the quality, accuracy, and fairness of data used in AI systems.

Challenges in Compliance
Complying with the EU AI Act presents several challenges for organizations, requiring careful planning and execution.
Key Challenges:

Complexity: The complexity of the regulatory requirements can be challenging to navigate, particularly for organizations with multiple AI systems.
Data Management: Ensuring data quality and fairness requires robust data management practices and continuous monitoring.
Resource Allocation: Implementing compliance measures can be resource-intensive, requiring investment in technology, personnel, and training.

The Role of HR and Compliance Teams
Human resources (HR) and compliance teams play a critical role in ensuring that organizations comply with the EU AI Act and address its implications for employment law.
Key Responsibilities:

Policy Development: Developing and implementing policies that align with the EU AI Act’s requirements and promote ethical AI use.
Training and Education: Providing training and education for employees on the ethical and legal implications of AI technologies.
Monitoring and Auditing: Continuously monitoring and auditing AI systems to ensure compliance and address any issues that arise.

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Future Prospects
The EU AI Act represents a significant step towards regulating AI technologies and ensuring their ethical and legal use. As organizations adapt to the new regulatory landscape, the focus will be on developing AI systems that are transparent, fair, and accountable.
Future Trends:

Innovation in Compliance: The development of innovative compliance solutions, such as AI-driven compliance tools, will help organizations navigate the regulatory landscape.
Global Impact: The EU AI Act is expected to influence AI regulation globally, setting a benchmark for other jurisdictions to follow.
Continuous Improvement: Organizations will continue to improve their AI systems and compliance practices, fostering a culture of ethical AI use.

Conclusion
The finalization of the EU AI Act has significant implications for employment law and compliance, requiring organizations to ensure the ethical and legal use of AI technologies. By adhering to the Act’s provisions and implementing robust compliance measures, organizations can navigate the complex regulatory landscape and promote transparency, fairness, and accountability in their AI systems. As the AI regulatory landscape continues to evolve, organizations must remain vigilant and proactive in addressing the ethical and legal challenges associated with AI.
Source of the news: SHRM
The post The EU AI Act Finalized: Implications for Employment Law and Compliance appeared first on HIPTHER Alerts.

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Medtech Compliance: Not Regulation, but Innovation

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The medical technology (Medtech) industry is at the forefront of healthcare innovation, developing cutting-edge solutions that improve patient outcomes and streamline healthcare delivery. However, compliance remains a critical concern, with regulatory requirements often viewed as a barrier to innovation. This article explores how compliance, rather than stifling innovation, can drive it by fostering a culture of quality, safety, and continuous improvement in the Medtech industry.
The Compliance Challenge in Medtech
The Medtech industry is subject to stringent regulatory requirements to ensure the safety and efficacy of medical devices. Compliance with these regulations is essential but can be resource-intensive and complex.
Key Compliance Requirements:

FDA Regulations: In the United States, the Food and Drug Administration (FDA) regulates medical devices, requiring rigorous testing and documentation to ensure safety and effectiveness.
EU MDR: The European Union’s Medical Device Regulation (EU MDR) sets stringent requirements for the marketing and distribution of medical devices in the EU.
ISO Standards: International standards, such as ISO 13485, provide a framework for quality management systems in the Medtech industry.

Compliance as a Driver of Innovation
While compliance is often seen as a barrier to innovation, it can also drive innovation by promoting a culture of quality and continuous improvement.
How Compliance Drives Innovation:

Quality Assurance: Compliance with regulatory requirements ensures that medical devices meet high standards of quality and safety, fostering trust and confidence among healthcare providers and patients.
Risk Management: Effective compliance programs help identify and mitigate risks, reducing the likelihood of product recalls and adverse events.
Continuous Improvement: Regulatory requirements encourage Medtech companies to continuously improve their processes and products, leading to innovative solutions that address unmet medical needs.
Market Access: Compliance with global regulatory standards enables Medtech companies to access international markets, expanding their reach and driving growth.

Balancing Compliance and Innovation
Medtech companies must strike a balance between compliance and innovation, ensuring that regulatory requirements do not stifle creativity and progress.
Strategies for Balancing Compliance and Innovation:

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Integrated Compliance Programs: Developing integrated compliance programs that align with the company’s innovation goals and support a culture of quality and safety.
Collaborative Approach: Collaborating with regulatory agencies, industry partners, and stakeholders to develop innovative solutions that meet regulatory requirements.
Leveraging Technology: Utilizing advanced technologies, such as artificial intelligence (AI) and machine learning, to streamline compliance processes and enhance product development.
Continuous Training: Providing continuous training and education for employees to ensure they understand and adhere to regulatory requirements while fostering a culture of innovation.

Case Studies of Compliance-Driven Innovation
Several Medtech companies have successfully leveraged compliance as a driver of innovation, demonstrating that regulatory requirements can enhance, rather than hinder, progress.
Case Study Examples:

Innovative Product Development: A Medtech company developed a new medical device that met stringent regulatory requirements, resulting in a product that was safer and more effective than existing solutions.
Streamlined Approval Process: By developing a robust compliance program, a Medtech company streamlined the regulatory approval process, bringing their innovative product to market faster.
Global Market Access: Compliance with international standards enabled a Medtech company to expand into global markets, driving growth and innovation.

The Role of Regulatory Agencies
Regulatory agencies play a crucial role in fostering a compliance-driven culture of innovation in the Medtech industry.
Regulatory Agency Initiatives:

Innovation Pathways: Developing innovation pathways and expedited approval processes for breakthrough medical devices that address unmet medical needs.
Collaborative Partnerships: Forming collaborative partnerships with industry stakeholders to develop regulatory frameworks that support innovation.
Guidance and Support: Providing guidance and support to Medtech companies to help them navigate the regulatory landscape and develop compliant, innovative solutions.

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Conclusion
Compliance in the Medtech industry is not a barrier to innovation but a driver of quality, safety, and continuous improvement. By fostering a culture of compliance and leveraging regulatory requirements as a catalyst for innovation, Medtech companies can develop cutting-edge solutions that improve patient outcomes and advance healthcare delivery. As the industry continues to evolve, balancing compliance and innovation will be essential for driving progress and ensuring the safety and efficacy of medical devices.
Source of the news: STAT News
The post Medtech Compliance: Not Regulation, but Innovation appeared first on HIPTHER Alerts.

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Can AI Help Banks Navigate Regulatory Compliance?

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Artificial intelligence (AI) is rapidly transforming the banking industry, providing innovative solutions to enhance operational efficiency, customer experience, and regulatory compliance. This article examines how AI can help banks navigate the complex regulatory compliance landscape, highlighting the benefits, challenges, and potential applications of AI in compliance.
The Regulatory Compliance Challenge
Banks operate in a highly regulated environment, with stringent requirements to ensure the integrity and stability of the financial system. Compliance with regulations such as anti-money laundering (AML), counter-terrorist financing (CTF), and data protection is crucial but can be resource-intensive and complex.
Key Compliance Requirements:

AML and CTF: Banks must monitor transactions to detect and prevent money laundering and terrorist financing activities.
Data Protection: Ensuring the privacy and security of customer data is paramount, particularly in light of regulations such as the General Data Protection Regulation (GDPR).
Reporting: Banks are required to submit detailed reports to regulators, demonstrating compliance with various regulatory requirements.

AI Applications in Regulatory Compliance
AI technologies offer a range of applications that can help banks streamline compliance processes, improve accuracy, and reduce costs.
Key AI Applications:

Transaction Monitoring: AI algorithms can analyze transaction data in real-time to identify suspicious activities and flag potential AML and CTF violations.
Customer Due Diligence: AI can automate the process of customer due diligence (CDD), verifying customer identities, and assessing risk profiles.
Regulatory Reporting: AI-powered tools can automate the collection, analysis, and submission of regulatory reports, ensuring timely and accurate compliance.
Risk Management: AI can analyze large datasets to identify and assess risks, enabling proactive risk management and mitigation.

Benefits of AI in Compliance
The integration of AI in regulatory compliance offers several benefits for banks, helping them navigate the complex regulatory landscape more effectively.
Key Benefits:

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Efficiency: AI-driven automation reduces the time and effort required for compliance tasks, allowing banks to allocate resources more efficiently.
Accuracy: AI algorithms can process and analyze data with high precision, minimizing the risk of human error and ensuring accurate compliance.
Cost Savings: By automating compliance processes, AI can significantly reduce operational costs associated with regulatory compliance.
Scalability: AI solutions can scale to handle large volumes of data and transactions, making them suitable for banks of all sizes.

Challenges in Implementing AI for Compliance
Despite the benefits, implementing AI-driven compliance solutions also presents several challenges that banks must address.
Key Challenges:

Data Quality: The effectiveness of AI in compliance depends on the quality and completeness of the data. Banks must ensure that their data is accurate and up-to-date.
Regulatory Uncertainty: The regulatory landscape for AI is still evolving, and banks must stay abreast of new regulations and guidelines to ensure compliance.
Integration: Integrating AI solutions with existing systems and processes can be complex and requires careful planning and execution.
Ethical Considerations: Banks must consider the ethical implications of using AI, including issues related to transparency, fairness, and accountability.

Future Prospects of AI in Compliance
The future of AI-driven compliance in banking looks promising, with ongoing advancements in technology and increasing regulatory acceptance. As AI continues to evolve, it is expected to play an even more significant role in enhancing regulatory compliance and risk management.
Future Trends:

Advanced Analytics: The use of advanced analytics and machine learning algorithms will enable more sophisticated risk detection and management.
RegTech Solutions: Regulatory technology (RegTech) solutions that leverage AI will become more prevalent, providing banks with innovative tools to streamline compliance.
Collaboration with Regulators: Increased collaboration between banks and regulators will drive the development of AI standards and best practices for compliance.
Personalized Compliance: AI will enable personalized compliance solutions tailored to the specific needs and risk profiles of individual banks.

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Conclusion
AI has the potential to revolutionize regulatory compliance in banking, offering significant benefits in terms of efficiency, accuracy, and scalability. While there are challenges to overcome, the future prospects of AI-driven compliance are bright, with ongoing advancements and increased regulatory acceptance paving the way for more innovative and effective solutions. As banks continue to embrace AI, they will be better equipped to navigate the complex regulatory landscape and ensure compliance in the digital age.
Source of the news: BizTech Magazine
The post Can AI Help Banks Navigate Regulatory Compliance? appeared first on HIPTHER Alerts.

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