BioRad.io

In this episode, discover how artificial intelligence (AI) can help you transform biopharma research and tackle the critical challenge of managing complex biological data. The discussion dives into strategies for designing software and databases that enable innovative science while keeping you compliant with regulatory demands. We examine how data management practices are evolving, why data standardization matters for your work, and how to balance fast-moving experimental designs with the need for AI-ready data. The episode highlights collaborative efforts to help you build a strong foundation for AI-driven insights in drug discovery.  Guest: Alekhya Akkunuri, Senior Bioinformatics Software Engineer, Metagenomi 

In this episode, learn how artificial intelligence (AI) is transforming pharmaceutical research and development. The discussion highlights how advancements in target identification, data integration, and workflow optimization can accelerate your research. We share insights on overcoming challenges in drug discovery, the role of generative AI, and what the future of agentic AI means for you. The conversation underscores the importance of collaboration and explores how AI can help revolutionize drug discovery.  Guest: Chris Waller, PhD, Vice President and Chief Scientist, Epam Systems 

Episode 21: The Future Lab — Innovations in Digital TransformationDiscover how the future biopharma lab will enable seamless collaborations and data-driven insights, catalyzing research advancements. Equipped to integrate data streams, automate repetitive tasks, and accelerate experimentation, the future lab promises transformative impact. Technologies — from AI-driven analytics to high-throughput screening platforms — will revolutionize how scientists explore drug candidates and unravel disease mechanisms faster and more efficiently. Listen to this episode to learn how the future lab will facilitate digital innovations and transform biopharma R&D.  Guest: Asha D'Souza, PhD, CEO, Ashtrix, Inc.

Data silos and challenges around data fragmentation and interoperability hinder holistic insights and innovation in research and development. Specifically, these challenges include retrieving, parsing, and cleansing data from various sources and formats, ensuring data security and privacy. However, at the center of data interoperability is the role of the patient. The patient brings data together and provides potential for transformative insights and accelerated innovation through a unified approach to data management. With a comprehensive strategy that integrates genomic, phenotypic, clinical, and device data, enhancing drug discovery and fostering innovation can be expedited, effectively bridging the existing data divide.Guest: Ardy Arianpour, CEO & Cofounder, SEQSTER

Episode 19: Reimagining Cancer Drug DevelopmentIn this episode, we discuss the way cancer drug development can be reimagined using AI and machine learning. Traditional drug development faces a high failure rate, highlighting the importance of understanding a drug's mechanism of action to improve success rates. AI and computational models that analyze data from various modalities, such as genomics and clinical information, can identify novel targets and enhance precision medicine. Advancements in data availability, especially in the clinic, and a deeper biological understanding are fundamental to revolutionize future drug discovery.Guest: David Li, Co-Founder and CEO, Meliora Therapeutics, Inc.