Understanding AI’s Expanding Role in Drug Discovery and Life Sciences R&D - Liran Belenzon of BenchSci
Feb 26, 2025
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Liran Belenzon, Co-founder and CEO of BenchSci, is a trailblazer in applying AI to drug discovery and R&D. He discusses how AI transformations are tackling the complexities of disease biology, paving the way for more efficient workflows. Liran highlights the significance of knowledge graphs and multimodal AI in enhancing research speed and quality. He emphasizes the need for flexible drug discovery processes and the importance of adapting budgets to harness AI’s full potential, balancing innovation with cost-effectiveness.
AI is revolutionizing drug discovery by addressing complexities in disease biology, improving decision-making and enhancing R&D workflows.
Successful AI adoption in life sciences requires organizations to establish a clear ROI strategy to justify investments and demonstrate tangible benefits.
Deep dives
AI Transforming Drug Discovery
AI is significantly transforming drug discovery by addressing the complexities of disease biology. A major challenge in clinical trials is the misunderstanding of the biological factors influencing disease, which contributes to the failure of 90% of trials. By leveraging AI-driven insights, researchers can enhance their understanding, leading to better decision-making and accelerating the innovation process in pharmaceutical R&D. This intersection of data and technology is paving the way for a new standard in drug development.
Importance of Structured Scientific Data
The effectiveness of AI in enhancing R&D is contingent on the quality of scientific data utilized. A robust framework that harmonizes and structures vast amounts of scientific information allows AI models to derive meaningful insights. BenchSci focuses on creating a high-veracity knowledge base that can facilitate better outcomes by integrating diverse data sources. This structured approach not only speeds up the research process but also ensures that the results are reliable and applicable.
ROI Strategy for AI Adoption
To effectively adopt AI in life sciences, organizations must establish a clear ROI strategy to gain support from stakeholders. Demonstrating tangible benefits, such as reductions in experimentation costs and improved overall outcomes, is crucial for securing investments in AI technologies. Despite the absence of a conventional budget line for AI initiatives, illustrating productivity gains can enable companies to justify their expenditures. This strategic approach reinforces the importance of showing immediate value while aiming for long-term advancements in drug discovery workflows.
Today’s guest is Liran Belenzon, Co-founder and CEO of BenchSci. BenchSci is a global leader in AI solutions for preclinical R&D that helps decipher complex biomedical research using proprietary technology. Liran joins us to discuss how AI is transforming disease biology research, streamlining scientific workflows, and enabling better decision-making in pharmaceutical R&D. Liran shares insights into the biggest challenges facing drug discovery today, including the complexity of disease biology and the critical role AI plays in unraveling it. He breaks down how AI-driven knowledge graphs, ontological models, and multimodal AI systems are improving both the speed and quality of scientific research. This episode is sponsored by BenchSci. Learn how brands work with Emerj and other Emerj Media options at emerj.com/ad1.
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