Artificial Intelligence serving patients

Boehringer Ingelheim is leveraging AI across its entire value chain, from early research through development, production to the distribution of products. The objectives vary depending on the specific application: better, more quickly available or safer products for patients. Find out more in the chat below.

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You

Why are so many people talking about AI these days?

AI has been around for many years already and is built into many applications in the industry and daily life. ChatGPT and other like-minded applications have made AI, and more specifically Generative AI, more widely available to the general public in 2022. The technology was thrust into the media spotlight as a growing object of wonder and fear, as well as a target for investment. So far, this has mainly allowed the average internet user to generate content such as text or images with the help of AI.

You

Isn’t it possible the business applications for AI are being overhyped?

The business applications for AI are certainly not all being overhyped. It’s true that a relatively small number of businesses currently depend on AI to generate practical results, so it is unsurprising that some observers have argued that AI is being overly hyped. But the impact of AI at a growing number of companies is significant. For example, it has been playing an active and expanding role at Boehringer Ingelheim for years, with employees now widely using Generative AI to accelerate their research.

You

What are some of the ways that Boehringer has been incorporating AI into its business?

Boehringer Ingelheim is using AI across the entire value chain, from early research through development, production and the distribution of products. For instance, the company’s sales and marketing department uses AI to optimize the outreach to healthcare professionals who might be interested in learning about certain products. Other examples where AI is already used include solutions to improve the efficiency of the company’s production lines, to ensure regulatory preparedness or to offer innovative solutions for patients. In a more recent development, the company was an early adopter of Generative AI to power iQNow, an in-house platform created to help scientists at Boehringer search through large collections of research papers. In just the first nine months after its introduction in early 2023, the company combined iQNow with the interactive features of large language models. This enhancement propelled the number of unique users at the company from 1,000 to 25,000.

You

How did Boehringer manage to become a forerunner in using Generative AI?

Boehringer Ingelheim did its homework and was well prepared. Several years ago, when multiple departments at the company began reporting they were overwhelmed with information, Boehringer Ingelheim sought out a solution to improve access to scattered documents and multiple databases compiled according to varying standards. The company saw forthcoming advances in AI as a path forward. IQNow was designed to help retrieve specific information using a sophisticated algorithm, and it was combined with AI-driven large language models when they became available.

You

Are there risks associated with connecting the company’s proprietary data to an AI application that lives in the cloud?

To ensure that proprietary data is kept fully secure, iQNow was connected to Microsoft’s Azure Open AI Service as a safe way to bring these complementary technologies together. Full separation is maintained between public and private data sources.

You

How is iQNow changing Boehringer’s research?

Boehringer Ingelheim’s researchers use iQNow to comb through roughly 700 million document databases to quickly identify experts on specific areas of study. Researchers ask iQNow questions as if it is a colleague sitting next to them who can give a reliable answer. By following the logic of human conversation, it helps them break down any research questions they encounter. Researchers can proceed to ask deeper questions or request supporting documents if they want more information.

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Has Generative AI measurably helped with research at Boehringer Ingelheim?

Yes, Generative AI has measurably helped Boehringer Ingelheim’s research. After its initial launch, a select group of staff members used iQNow to supplement their research. But with the release of ChatGPT, generative AI became a more practical, everyday tool, and the company combined iQNow with the interactive features of a large language model. Users reported that, in the first 70 days after this system came online, it saved nearly 150,000 hours of labor. In the first nine months, it helped save approximately 600,000 working hours, equivalent to roughly €60 million in research spending.

You

Besides just saving time, how has this practical application of AI helped the company’s research?

Rather than just steering Boehringer Ingelheim researchers to specific documents or experts, AI now gives them the ability to engage in a moderated dialogue drawing on all the knowledge accessible from across a vast database of research documents. This contributes directly to accelerating Boehringer Ingelheim's process of drug discovery and development.

You

What else has been going on with AI at the company?

The company applies AI in a variety of ways. What started with iQNow, Generative AI for everyone, is meanwhile realized with Microsoft standard applications such as Copilot. Other examples demonstrate the effectiveness of Generative AI for specific business use cases: AI is now being leveraged by Boehringer Ingelheim to summarize insights captured by medical-scientific liaison teams. The company’s first product in this field, KNERD, is now active and being used by researchers. ADAM, the company´s own advanced design assistant for molecules, empowers medical chemists in their hunt for new drug candidates.

These cases demonstrate the value as well as the heterogeneity of AI usage across Boehringer Ingelheim. Meanwhile, Boehringer´s IT has set-up a robust solution framework, which offers standard AI solutions for all users as well as capabilities and platforms to develop and run custom solutions for very specific use cases in the businesses and organizational functions.

You

How is AI expected to affect the pharmaceutical industry in the future?

AI is expected to transform the pharmaceutical industry in several ways. It will accelerate drug discovery, speeding up the process of identifying compounds and targets for possible treatments. This will lead to new drugs that might never have been developed without this remarkable tool, for example with molecule structure prediction or de-novo protein design.

It will soon be capable of supporting human work in low-risk clinical trial settings, leading to quicker regulatory approvals and faster availability of new treatments for patients who need them.

Furthermore, AI will accelerate the development of personalized medicines tailored to individual patients – something that will ultimately improve treatment outcomes.

Choose your prompt to reveal the story

Why are so many people talking about AI these days?

Isn’t it possible the business applications for AI are being overhyped?

What are some of the ways that Boehringer has been incorporating AI into its business?

How did Boehringer manage to become a forerunner in using Generative AI?

Are there risks associated with connecting the company’s proprietary data to an AI application that lives in the cloud?

How is iQNow changing Boehringer’s research?

Has Generative AI measurably helped with research at Boehringer Ingelheim?

Besides just saving time, how has this practical application of AI helped the company’s research?

What else has been going on with AI at the company?

How is AI expected to affect the pharmaceutical industry in the future?

To explore the potential of quantum computers for pharmaceutical research and development, Boehringer Ingelheim launched a collaboration with Google Quantum AI in 2021. What is the current state of advancement in this field?

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Three questions for

Clemens Utschig-Utschig
CTO & Chief Architect IT, Boehringer Ingelheim

1. Where do we currently stand in terms of using quantum computing for pharmaceutical research?

Quantum computing may be on the brink of enabling highly accelerated computing power. And chemistry may prove to be the first major use case. Computational drug discovery relies on making accurate predictions of how candidate drugs will interact with their targets in living cells. This requires the simulation of thousands of atoms at specific temperatures, and it can generate insights into these systems that may otherwise be inaccessible through traditional experimentation.

Quantum computers promise to perform highly efficient chemical calculations that can simulate the quantum nature of the system. These tasks, involving complex molecules, are beyond the capabilities of conventional computers. Today’s quantum computers are still at an early stage of development and can only be used for very small systems. To simulate pharma-relevant molecules we’ll need larger and more reliable quantum computers, as well as quantum algorithms tailored to our needs. A central goal is to make computer modeling equivalent or more convenient than current lab experiments. Quantum hardware is advancing very quickly, and novel quantum algorithms are being developed every day. So practical applications are getting closer.

“Further development will be needed to exploit the economic potential of quantum computing. We are continuing to push this forward and expect to be able to point to examples of industry-relevant applications by the end of this decade.”
Clemens Utschig-Utschig, CTO & Chief Architect IT, Boehringer Ingelheim

2. What has Boehringer Ingelheim learned so far? And when will using quantum computers become a standard tool that can start to benefit patients?

To explore quantum computing’s potential, Boehringer Ingelheim launched a collaboration with Google Quantum AI at the beginning of 2021.

Within this collaboration, we have explored several paths forward to practical applications. For example, one of our use cases was to identify quantum algorithms to study the P450 enzyme. P450 plays an important role in the human metabolism and has never been analyzed this way before. The outcome of the analysis has shown that quantum computers can offer a clear advantage over the best classical methods at very high level of accuracy.

However, even with the best available quantum algorithms, these calculations would require three days of runtime, which is way beyond what is practical in an industrial setting. We are currently working on developing new algorithms that could reduce computer runtimes from hours or days to a few minutes.

Another example of our current research, together with the University of Toronto, involves developing quantum algorithms to study molecular dynamics, a field that seeks to predict how molecules move over time.

Our key goal is to predict how well drug candidate molecules will bind to their target. Therefore, we have developed a novel quantum algorithm for molecular dynamics and have presented those results at various international conferences.

Nonetheless, while we are making steady progress in terms of software, hardware and use cases, we are still at the stage of applied basic research.

Further development will be needed to exploit the economic potential of quantum computing. We are continuing to push this forward and expect to be able to point to examples of industry-relevant applications by the end of this decade.

2021

Launch of cooperation with Google Quantum AI

3. What are the next steps that need to be taken?

It’s still too early to predict when the pharmaceutical industry will be able to harness the full potential of quantum computers.

We need to see further improvements in hardware and the development of novel algorithms. We also need to come up with new methods that allow us to make compromises between accuracy and the amount of time needed for calculations.

The main focus for the time being will be to keep reducing the runtimes of quantum algorithms – to the point where these calculations will be more attractive than either experiments or low-accuracy calculations from conventional computers – all while exploring new use cases.

In other words, there are many challenges that we, together with our partners, can actively contribute our expertise to solving. I am certain that the next years will lead up to the advancements we need.

The perspective of our quantum lab and partners has been published here in Nature Physics.

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