In an era where AI is transforming medicine, drug discovery and genetic therapies are entering a new frontier. This session will explore how AI accelerates the development of innovative therapies, from optimizing molecular designs to enabling precise, tissue-specific genetic interventions. Discover how cutting-edge AI technologies are advancing therapeutic modalities, enhancing delivery systems, and unlocking the potential of personalized medicine. Join leading experts as they share groundbreaking insights into AI-driven innovation and its profound impact on the future of drug discovery and genetic medicine.

In AI development, there’s a trade-off between scale and sophistication. This session asks whether biological AI should follow the “bigger is better” mantra or focus on smarter, domain-specific architectures. One camp argues that a simple model fed with colossal datasets will outperform a clever model with limited data – echoing the view that more data beats complex algorithms. Others point out that biology’s complexity (from multi-step pathways to 3D genome organization) demands AI with built-in knowledge or special architectures to learn effectively from smaller, high-quality datasets. Through case studies in drug discovery and genomics, we will discuss if success lies in scaling up simple neural networks on big data or in engineering biologically informed AI models that excel with less data. What are the ROI trade-offs for researchers and investors in each approach?