Atomic AI operates at the convergence of artificial intelligence and RNA drug discovery — two revolutionary fields with significant potential, yet not without challenges. In developing an RNA foundation model for rational drug design, Atomic pioneered novel approaches to generate, process, and model RNA data (structure, function, and interactions) from first principles. This talk will reveal their methodological journey, critical insights gained, and identify persistent gaps in the broader field that currently constrain AI's capacity to transform drug discovery.

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?