Learn how AI can help you manage complex biological data in biopharma. Explore strategies for better software, databases, and data standardization to stay compliant, enable innovation, and build a strong foundation for AI-driven drug discovery.
Explore how AI is transforming drug discovery. Learn how generative and agentic AI, data integration, and workflow optimization can accelerate research, overcome challenges, and drive collaboration in pharmaceutical R&D.
Discover the future lab, an ecosystem where digital transformations shape the landscape of biopharma research, driving innovation and unlocking the potential of tomorrow's therapies.
Data silos and challenges around data fragmentation and interoperability hinder holistic insights and innovation in R&D. Employing strategy around the patient at the center of data interoperability can provide accelerated insights and innovation through a unified approach to data management.
Historically, cancer drug development has a high failure rate. Why is that, and how can we facilitate success? Listen in as we chat with the co-founder of a company that is trying to shift the paradigm using AI and machine learning.
From antibiotics to cancer drugs, fungi and other microbes have long been a rich source of medicinal compounds. In this episode, we discuss how one company sequences and mines novel fungal genomes to discover and engineer new therapeutics.
Early detection of cancer and other diseases can significantly improve patient prognosis. Join us for a practical discussion about the challenges of developing noninvasive early screening tools, from biomarker identification to clinical performance.
Precision medicine is a multifaceted approach to designing and delivering individualized patient treatments. Using multi-omic, environmental, lifestyle, and other data, researchers aim to more accurately develop treatment and prevention strategies tailored to each unique patient. The promise of precision medicine is huge, but it also creates new challenges around data capture, storage, computation, and creation of algorithms for prediction and analysis. In this episode, we discuss the evolution of precision medicine and some ways these challenges are and can be addressed, including blockchain, remote patient monitoring, and strategic data integration. Guest: Kumar Bala, Former Head of Digitalomics Strategy, Oracle
The relatively high failure rate of new candidate drugs is a key issue in pharma and biopharma R&D. Listen in as we discuss how artificial intelligence, machine learning, and the ever-increasing availability of biological datasets can improve prospects.
Digital therapeutics are software-driven, evidence-based healthcare products. They can improve patient access to and involvement in their care, improving outcomes. Join us as we discuss the recent rise in digital therapeutics and what the future may hold.
Personalized medicine is critical to the future of healthcare. However, implementation faces unique challenges. What changes are needed for personalized medicine to truly become a reality? What are some of the biggest obstacles? Listen in as we discuss.
High-throughput technologies have triggered a surge in scientific data generation and consumption, but managing these volumes of data is challenging. Listen as we discuss data fabric, an emerging solution that connects data sources in one environment.
Synthetic biology is the design, testing, and construction of new, standardized biological parts and devices, and programming them to do something productive. Significant medical breakthroughs are already happening as a result of advances in synthetic biology. From antiviral treatment and immune cell engineering to biomonitoring, the applications of synthetic biology are endless, creating new opportunities for innovative informatics. In this episode, we discuss revolutionary applications of synthetic biology in biopharma. Guest: Mark Charbonneau, PhD, Director and Head of Quantitative Biology, Synlogic
Technologies for therapeutic antibody R&D have evolved rapidly, but the informatics needed to support these processes have struggled to keep up. In this episode, we discuss challenges in antibody discovery and benefits of improved informatics.
Data liquidity, the ability of data to flow easily and securely, provides tremendous value for biopharma researchers. Open access is paving the way for increased collaboration and discovery. However, additional initiatives are needed to further promote the management and sharing of scientific data and ensure that information is quickly available to researchers. This episode highlights the needs, solutions, and challenges of ensuring interoperability of research data throughout the data lifecycle.
Increasingly, pharma is looking at partnerships and collaborations to increase the velocity of drug discovery and development. Common data sharing platforms are not built to meet lab-specific requirements. In addition to discussing the necessity of simple and secure collaboration, listeners will learn about the dangers of not using proper tools.
As biopharma research and development continues to transform digitally, the industry as a whole struggles with guidance on how data and relevant metadata are captured, managed, and shared. Typically experimental data are siloed,
Sequencing is used to identify novel drug targets and genetically stratifying clinical trial patients, improving the power of precision medicine. However, the global disparity of genomic technologies is increasing. How will this affect drug development, particularly for non-European ethnicities? Why is it important to have a global context for targeted therapies?
Life science companies are looking to modernize their research environments and build the Lab of the Future. From early discovery to preclinical research, how can technology help automate the lab and accelerate discoveries?
What are the theories, algorithmic principles, and limitations of active learning? What are some current applications of active learning within BioPharma drug screening and optimization? How can active learning accelerate multi-dimensional drug discove...
What is the vision of interactive data exploration beyond enriching visualizations and facilitating the integration of data? Within the context of research, what are the challenges and opportunities in building next-generation visual analytics?
Artificial Intelligence (AI) and Machine Learning (ML) are big topics getting lots of attention within BioPharma. What is the role of AI/ML in genomics? How can we separate the hype of AI/ML from reality? Listen in to find out this and more.
