MDDI 演讲稿 · 2025-08-26
吴汉雁高级政务次长在 AI4Life 峰会 2025 的开幕致辞
吴汉雁高级政务次长在 AI4Life 峰会 2025 的开幕致辞
要点
- • 传统药物研发往往耗时超过10年、耗资数十亿美元才能让一种药物抵达患者,AI端到端流水线正在大幅压缩这一周期。
- • 南洋生物制药的「药靶相互作用图神经网络」展示了AI如何缩短研发周期、降低成本并拓展未满足医疗需求的治疗可能性。
- • AI驱动的ADMET平台与虚拟分子筛选技术,使研究人员能在早期预测药物吸收、代谢与毒性,将高风险化合物在进入后续阶段前提前过滤。
- • 新加坡以对A*STAR及学术医学中心的持续投入为核心,借助联邦学习方法,允许研究人员在不传输敏感患者数据的前提下跨医院协作开发AI模型。
- • 南洋生物制药、NVIDIA、慧与科技(HPE)及Equinix在峰会上签署谅解备忘录,将在新加坡共建全球最大的AI驱动天然化合物库。
- • 劳动力晋升联盟正推行培训计划,为新加坡人提供数据科学、分子建模与临床转化等多学科技能,以应对AI生物医药领域的新兴岗位需求。
完整译文(中文)
MDDI 英文原文译文 · 翻译日期: 2026-06-21
南洋生物制药公司主席 Roland Ong 博士,
来自 NVIDIA 的各位贵宾,
Hewlett Packard Enterprise、Equinix 及劳动力发展联合会的各位,
女士们、先生们,
下午好,
很荣幸今天出席 AI4Life 计划的启动仪式。这是一项意义重大的举措,旨在借助人工智能为人类健康探索新的解决方案。
人工智能创新,助力生物医学科学发展
药物研发历来耗资巨大且充满不确定性。一种药物从研发到最终到达患者手中,往往需要超过10年时间和数十亿美元的投入。对于急需帮助的人来说,这实在太漫长了。
然而,人工智能正开始改变这一格局并取得初步成效。端到端的人工智能驱动流程已产出药物候选化合物,进入临床试验所需的时间仅为传统方式的一小部分。南洋生物制药公司的药物-靶标相互作用图神经网络(Drug-Target Interaction Graph Neural Network)等平台展示了人工智能如何缩短研发周期、降低成本,并为未被满足的医疗需求拓展治疗可能性。这项技术固然复杂,但真正重要的是它如何影响和改善我们的生活。因此,我非常感激你们正在踏上这段非凡的旅程。
人工智能通过整合多层生物数据——从基因组学到蛋白质组学再到临床洞察——为科学家提供了对疾病通路更深刻的理解。人工智能还帮助科学家克服长期存在的瓶颈。虚拟分子筛选让研究人员无需在实验室筛查数百万种化合物,而是专注于最有前景的候选物,从而节省了宝贵的时间、资金和其他资源。
除筛选之外,人工智能也开始让药物开发变得更加安全。机器学习方法可以预测在研药物在体内的行为,而人工智能驱动的 ADMET 平台则预测药物的吸收与代谢方式及其是否具有毒性。这些平台能够在高风险化合物进入研发流程太深之前就将其筛除。这不仅提高了安全性,还降低了所需的成本和资源。
在人工智能如何革新我们应对人类健康问题的方式上,我们才刚刚起步。仍有许多空白有待填补。例如,人工智能设计的分子在合成上依然颇具挑战,数据匮乏也是一大问题。
尽管如此,方向对我们所有人来说都是清晰的。从药物研发到临床试验,人工智能已经在发挥巨大作用。即便在日常医疗保健成效方面,人工智能也将通过可穿戴设备和移动健康应用程序等工具,实现更个性化的医疗、更早期的疾病检测以及更好的患者参与。
强化新加坡作为全球医疗创新枢纽的地位
AI4Life 也体现了新加坡推动创新的方式——即通过与在场每一位的合作共同实现。这一计划汇聚了众多利益相关方:学术界、研究人员、全球科技企业,共同为大规模加速医疗创新提供一个范式。
我们相信新加坡具备充分条件来担当这一引领角色。多年来,我们在生物医学科学生态系统上进行了大量投资,其核心是对 A*STAR 及各学术医疗中心的持续投入。这些中心将患者护理、教育与研究融为一体,不仅提供高质量的医疗服务,也推动研究前沿向新加坡延伸。与此同时,我们可信赖的数字基础设施为大规模人工智能应用提供了坚实基础,涵盖医疗服务提供和支持转化研究两个层面。例如,利用联邦学习方法,研究人员可以跨多家医院协作开发人工智能模型,而无需转移敏感的患者数据。这既保障了隐私,又加速了疾病预测和个性化医疗领域的创新。
为此,我非常高兴见证南洋生物制药公司、NVIDIA、Hewlett Packard Enterprise 与 Equinix 之间谅解备忘录(MoU)的签署。这是一个四方联盟,旨在新加坡建立全球规模最大的人工智能驱动天然化合物库。此次合作融合了深度技术、先进基础设施与生命科学专业知识,以加速治疗性药物发现和生物医学创新。
我们必须持续寻求合作途径,大规模加速医疗创新。通过开展国际合作和跨行业协作,新加坡既可引入宝贵的专业知识、借力全球研究成果,也可通过技能发展和知识产权创造来获取价值。这将确保我们的长期竞争力,并将新加坡定位为人工智能驱动药物研发的枢纽。
本月早些时候,政府启动了经济战略检讨(Economic Strategy Review),以制定经济蓝图。与 AI4Life 等计划相结合,这些举措确保人工智能领域的突破转化为切实影响,惠及患者、新加坡整体经济和社会。我认为,这赋予了一切真正的意义——这绝不仅仅是为了技术而技术。
培育面向未来的生物医学人才队伍
最后,我想说的是,仅靠技术本身并不能带我们到达目标。人工智能与生物技术的融合正在数据科学、分子建模和临床转化领域催生新的职位。为抓住这些机遇,新加坡必须投资培育跨学科人才——能够在人工智能、生物学与药物研发之间架起桥梁的人。这些技能组合对于将强大人工智能算法的预测转化为现实疗法至关重要。
我们已有相关计划付诸实施,并将持续完善。劳动力发展联合会的劳动力发展计划正在为新加坡人赋予所需技能,以承担这些新兴职位。这确保我们的人才培育与行业发展保持同步,使我们拥有一支面向未来的人才队伍,能够支持创新,为新加坡人、新加坡乃至整个世界带来更好的成果。
结语
总而言之,人工智能在医疗健康领域的前景无限广阔,我相信我们所有人都支持这一事业,这也正是我们今天聚集于此的原因。AI4Life 是我们将这一前景转化为现实的有力范例。
现在,让我们乘势而上。让我们携手跨越学科、行业、企业和利益相关方群体的界限,共同合作,确保新加坡不仅紧跟时代步伐,更能引领医疗创新未来的走向。
非常感谢。
英文原文
MDDI 官网原始记录 · 抓取日期: 2026-06-21
Dr. Roland Ong, Chairman of Nanyang Biologics,
Distinguished Guests from NVIDIA,
Hewlett Packard Enterprise, Equinix, and the Workforce Advancement Federation,
Ladies and Gentlemen,
A very good afternoon,
It is my pleasure to join you today at the launch of the AI4Life initiative. This is a significant initiative to harness AI to discover new solutions for human health.
AI Innovation for Better Biomedical Science
Drug discovery has traditionally been costly and uncertain. It often takes more than 10 years and billions of dollars before a single medicine reaches patients. That is really long when someone is in need of help.
However, AI is beginning to change this landscape and yield early success. End-to-end AI-driven pipelines have produced drug candidates that take a fraction of the time to reach clinical trials. Platforms like Nanyang Biologics’ Drug-Target Interaction Graph Neural Network show how AI can cut discovery timelines, reduce costs, and expand treatment possibilities for unmet medical needs. The technology is really complex but what really matters is how it affects and improves our lives. So I am very grateful that you are embarking on such an amazing journey.
AI offers scientists a deeper understanding of disease pathways by combining multiple layers of biological data — from genomics to proteomics to clinical insights. AI also helps scientists to overcome long-standing bottlenecks. Instead of screening millions of compounds in the lab, virtual molecular screening has enabled researchers to focus on the most promising ones, therefore saving precious time, money and other types of resources.
Beyond screening, AI is also starting to make drug development safe. Machine learning methods can predict how a drug in development behaves in the body, while AI-powered ADMET platforms – which predict how a drug is absorbed, processed, and whether it might be toxic. These platforms can filter out risky compounds early on, before they move too far down the pipeline. Again, it makes it safer and reduces the cost and resources required.
We are just at the starting block on how we see AI revolutionising the way we address human health. There are still many gaps to be filled. For example, AI-designed molecules can still be really hard to synthesise, and data scarcity is a problem.
That said, the trajectory is clear all of us. AI is already making a huge difference from drug discovery to clinical trials. Even in day-to-day healthcare outcomes, AI will enable more personalised medicine, earlier detection of disease, and better patient engagement through tools such as wearables and mobile health apps.
Strengthening Singapore’s Role as a Global Healthcare Innovation Hub
AI4Life also reflects how Singapore approaches innovation – and that is through partnerships with each and every one of you in the room. This initiative brings together many stakeholders: academics, researchers, global technology players, all here to offer a model for accelerating healthcare innovation at scale.
We believe that Singapore is well positioned to play this leading role. Over the years, we have invested heavily in our biomedical science ecosystem, which is anchored by sustained investments in A*STAR and our Academic Medical Centres. These centres integrate patient care, education, and research, enabling not just high-quality healthcare but also pushing the frontiers of research into Singapore. This is also then coupled with our trusted digital infrastructure, which provides a strong foundation for large-scale AI applications in both the provision of healthcare services and supporting translational research. For example, using federated learning approaches, researchers can then collaborate to develop AI models across multiple hospitals without having to transfer sensitive patient data. This ensures privacy while accelerating innovation in disease prediction and personalised medicine.
To this end, I am very pleased to witness the MoU signing between Nanyang Biologics, NVIDIA, Hewlett Packard Enterprise, and Equinix. This is a four-party alliance to build the world’s largest AI-powered natural compound library, right here in Singapore. The collaboration combines deep tech, advanced infrastructure, and life sciences expertise to accelerate therapeutic discovery and biomedical innovation.
It is crucial that we continue to find ways to collaborate and accelerate healthcare innovation at scale. By collaborating internationally and across industries, Singapore can bring in valuable expertise and leverage global discoveries, while also capturing value through skills development and creation of intellectual property. This will then ensure our long-term competitiveness and position Singapore as a hub for AI-enabled drug discovery.
Earlier this month, the Government launched the Economic Strategy Review to chart an economic blueprint. Together with initiatives like AI4Life, they ensure that breakthroughs in AI translate into real impact, for patients, for Singapore’s wider economy and for our society. I think that makes it all meaningful that it is not just technology for technology’s sake.
Building a Future-Ready Biomedical Workforce
And finally, I want to say that technology alone will not get us there. The convergence of AI and biotechnology is creating new roles in data science, molecular modelling, and clinical translation. To capture these opportunities, Singapore must invest in multidisciplinary talent - people who can bridge AI, biology, and drug development. These skillsets are essential in turning predictions from powerful AI algorithms into real-world therapies.
We have schemes in place to make this happen and will continue to improve. Workforce development programmes by the Workforce Advancement Federation are equipping Singaporeans with these skills to take on these emerging roles. This ensures that our talent development keeps pace with industry evolution, and that we have a future-ready workforce that can support innovation that can lead to better outcomes for Singaporeans, Singapore and the world at large.
Conclusion
In conclusion, the promise of AI in healthcare is immense, and I believe all of us are behind this cause, which is why we are here today. AI4Life is a powerful example of how we translate this promise into reality.
So now, let us build on this momentum. Let us work together across disciplines, sectors, companies, stakeholder groups, to ensure that Singapore not only keeps pace, but leads in shaping the future of healthcare innovation.
Thank you so much.