The rapid advance of generative AI is reshaping the strategic vision for R&D
across industries. The unique challenges of pharmaceutical R&D will see
applications of generative AI deliver value along the entire value chain from
early discovery to regulatory approval. This perspective reviews these
challenges and takes a three-horizon approach to explore the generative AI
applications already delivering impact, the disruptive opportunities which are
just around the corner, and the longer-term transformation which will shape the
future of the industry. Selected applications are reviewed for their potential
to drive increase productivity, accelerate timelines, improve the quality of
research, data and decision making, and support a sustainable future for the
industry. Recommendations are given for Pharma R&D leaders developing a
generative AI strategy today which will lay the groundwork for getting real
value from the technology and safeguarding future growth. Generative AI is
today providing new, efficient routes to accessing and combining organisational
data to drive productivity. Next, this impact will reach clinical development,
enhancing the patient experience, driving operational efficiency, and unlocking
digital innovation to better tackle the future burden of disease. Looking to
the furthest horizon, rapid acquisition of rich multi-omics data, which capture
the 'language of life', in combination with next generation AI technologies
will allow organisations to close the loop around phases of the pipeline
through rapid, automated generation and testing of hypotheses from bench to
bedside. This provides a vision for the future of R&D with sustainability at
the core, with reduced timescales and reduced dependency on resources, while
offering new hope to patients to treat the untreatable and ultimately cure
diseases.

生成人工智能在制药研发领域应用的价值链上，从早期发现到监管批准，以三个层次的方法进行探讨，评估已产生影响、即将到来的颠覆性机会和将塑造行业未来的长期转型。这为制药研发领导者制定生成人工智能战略提供了建议，为获取技术的真正价值和确保未来增长奠定基础。

新天地：从实验室到临床的创新性药物研发 —— 行业视角

New Horizons: Pioneering Pharmaceutical R&D with Generative AI from lab  to the clinic -- an industry perspective

The fiercely competitive business environment and increasingly personalized
customization needs are driving the digital transformation and upgrading of the
manufacturing industry. IIoT intelligence, which can provide innovative and
efficient solutions for various aspects of the manufacturing value chain,
illuminates the path of transformation for the manufacturing industry. It is
time to provide a systematic vision of IIoT intelligence. However, existing
surveys often focus on specific areas of IIoT intelligence, leading researchers
and readers to have biases in their understanding of IIoT intelligence, that
is, believing that research in one direction is the most important for the
development of IIoT intelligence, while ignoring contributions from other
directions. Therefore, this paper provides a comprehensive overview of IIoT
intelligence. We first conduct an in-depth analysis of the inevitability of
manufacturing transformation and study the successful experiences from the
practices of Chinese enterprises. Then we give our definition of IIoT
intelligence and demonstrate the value of IIoT intelligence for industries in
fucntions, operations, deployments, and application. Afterwards, we propose a
hierarchical development architecture for IIoT intelligence, which consists of
five layers. The practical values of technical upgrades at each layer are
illustrated by a close look on lighthouse factories. Following that, we
identify seven kinds of technologies that accelerate the transformation of
manufacturing, and clarify their contributions. Finally, we explore the open
challenges and development trends from four aspects to inspire future
researches.

这篇论文提供了一个关于 IIoT 智能化的综合概述，研究了制造业转型的必然性和成功经验，并分析了 IIoT 智能化在各个方面的价值，提出了一个包括五层的分层发展架构，并讨论了加速制造业转型的七种技术以及未来研究的挑战和发展趋势。