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The evolution of AI in biomedical research
From early machine learning efforts to identify patterns in large biological datasets to the emergence of advanced bioinformatics tools and software solutions, AI in biomedicine has evolved significantly over the years. Cloud computing has revolutionized data management by providing scalable resources for handling large datasets, while technologies like Docker enable the creation of reproducible analytical workflows across diverse computational environments. This evolution has established the data and infrastructure foundations that facilitate the next generation of AI use cases in science.
Applications of AI in R&D and the clinic
As innovation continues to accelerate, AI has moved from an experimental technology to a key driver, reshaping research and healthcare in ways previously unimaginable, and we are already witnessing the remarkable impact of AI in drug development. In recent years, the U.S. Food and Drug Administration (FDA) has seen a significant increase in drug and biologic application submissions which use AI/ML components, illustrating the profound impact of these technologies in the drug development lifecycle. Other applications in R&D and the clinic include:
- Knowledge extraction: Through the application of AI and knowledge graphs, Wisecube were able to explore repurposed drugs for Alzheimer’s Disease, offering a promising avenue to accelerate scientific breakthroughs in treatments.
- Compound generation: EXS-21546, an AI-
designed A2A receptor antagonist developed by Exscientia for immuno-oncological applications, is currently undergoing clinical trials. - Target prediction: Gritstone Bio has developed an AI-based platform to predict which mutations will generate tumor-specific neoantigens most likely to be presented on the tumor cell surface by human leukocyte antigen (HLA) molecules.
- Patient identification: Using AI, IQVIA was able to increase the precision of patient identification by 15x and healthcare professional linkage by 10x for oncology patients eligible for new first- or second-line therapies.
Many of these AI-driven breakthroughs would not be possible without robust computational frameworks to process and analyze massive datasets. The same computational rigor that powers large-scale scientific discoveries now supports the integration of AI in biomedicine, ensuring that complex models can be run reliably, reproducibly, and at scale.
Bioinformatics agent purpose-built for the R&D lifecycle
AI has become integral to genomics and drug discovery, and we have been pioneering this vision since the inception of Nextflow over 10 years ago. Nextflow is a powerful parallelization engine that is used by scientists to run high throughput workloads, process data, generate predictions and even train machine learning models. Nextflow is also a language designed to make it easy to write, debug, generate, and execute code in an LLM-first world.
At Seqera, we believe that AI isn’t just a tool – it’s the future of scientific discovery, and we are committed to harnessing its power to unlock scientific breakthroughs that deepen our understanding of diseases and human health. We are continuing to make this a reality with the launch of Seqera AI, the bioinformatics agent designed to support scientists throughout the entire R&D lifecycle.
Seqera AI enhances research productivity by answering complex questions, generating and validating bioinformatics code, and interpreting scientific results with high accuracy. Moreover, Seqera AI simplifies complex bioinformatics analysis, allowing researchers to focus on discovery without the technical barriers, enabling advanced data analysis more accessible and empowering scientists to achieve breakthroughs in genomics, drug discovery, and beyond.
Advancing science for everyone through software
At Seqera, we are committed to accelerating scientific discoveries for everyone through software. From Nextflow, which is optimized for leveraging LLMs, to Seqera AI, which democratizes access to essential bioinformatics tools, we want to enable everyone to benefit from advanced data analysis. By integrating AI, we are making these tools smarter, more intuitive, and more accessible than ever, and you can try it now.
