Every major AI advance was built on massive amounts of data. ChatGPT was built on top of the Internet, new coding agents train on millions of public codebases, and AlphaFold leveraged the Protein DataBank. Learn about the ways that synthetic biologists are leveraging the combination of DNA sequencing and synthesis to fuel new modeling advances.

Blink twice and you’ll miss the latest publication on AI-guided protein design. From structures solved using AlphaFold to de novo proteins generated at the click of a button, scientists are putting AI to work at various stages of the protein design workflow. When looking at how far the field has already come, it’s easy to forget that these are still the early days for lab-in-the-loop protein design. This talk will highlight what it takes to get to real outcomes today, the challenges R&D teams still face when climbing towards their target protein profile, and what lies ahead.

A new era in synthetic biology is unfolding as AI, genome sequencing, and DNA synthesis technologies come together to unlock breakthroughs once considered unattainable. In gene therapy, AI is poised to address one of the most pressing and costly hurdles of the field: efficient and targeted gene delivery. This talk will examine how advanced, AI-enabled approaches to synthetic biology for gene delivery are transcending the limitations of naturally occurring vectors, broadening tissue targeting to organs such as the eye, muscle, and brain, and accelerating the path from concept to clinical success. Attendees will gain actionable insights into harnessing these AI-driven tools to improve patient access, reduce costs, and usher in a new generation of transformative genomic medicines.

New advances in machine learning combined with cell-free protein synthesis are creating synergistic capabilities to expedite enzyme evolution. Machine learning models guide the design of enzyme variants by predicting the impact of amino acid changes on enzyme function. Cell-free protein synthesis enables rapid production and screening of these variants, facilitating high-throughput experimentation. By leveraging these technologies, innovators and researchers can now efficiently evolve enzymes with desired properties in a cost-effective and accelerated manner. This integrated approach represents a powerful tool for advancing biocatalysis and enzyme engineering within the realm of synthetic biology and bioengineering.

AI is revolutionizing drug discovery—not just by accelerating timelines, but by enhancing our ability to predict how drugs behave in the body. This session highlights cutting-edge approaches like dynamic systems simulations that model mechanistic interactions at the cellular level. Learn how platforms are able to generating causal hypotheses to forecast both efficacy and toxicity, unlocking smarter, safer therapeutic development.

What if the next breakthrough biologist isn’t human? In this spotlight talk, Sam Rodriques, founder of Future House, shares his vision for an AI scientist that can generate hypotheses, design experiments, and accelerate discovery in biology. As automation and machine learning reshape the scientific method, Future House is pioneering a new model for research—where algorithms, not just researchers, drive innovation. Join this session for a look inside the lab of the future, where biology is accelerated by intelligence at machine speed.

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.

All of life encodes information with DNA. While tools for sequencing, synthesis, and editing of genomic code have transformed biological research, intelligently composing new biological systems would also require a deep understanding of the immense complexity encoded by genomes. We introduce Evo 2, a biological foundation model trained on 9.3 trillion DNA base pairs from a highly curated genomic atlas spanning all domains of life. We train Evo 2 with 7B and 40B parameters to have an unprecedented 1 million token context window with single-nucleotide resolution. Evo 2 learns from DNA sequence alone to accurately predict the functional impacts of genetic variation—from noncoding pathogenic mutations to clinically significant BRCA1 variants—without task-specific finetuning. Applying mechanistic interpretability analyses, we reveal that Evo 2 autonomously learns a breadth of biological features, including exon–intron boundaries, transcription factor binding sites, protein structural elements, and prophage genomic regions. Beyond its predictive capabilities, Evo 2 generates mitochondrial, prokaryotic, and eukaryotic sequences at genome scale with greater naturalness and coherence than previous methods. Guiding Evo 2 via inference-time search enables controllable generation of epigenomic structure, for which we demonstrate the first inference-time scaling results in biology. We make Evo 2 fully open, including model parameters, training code, inference code, and the OpenGenome2 dataset, to accelerate the exploration and design of biological complexity.

Unleash the power of computational brilliance and biological wonder as we delve into a stimulating session where enzyme engineering meets data-driven decisions in synthetic biology. This confluence aims to spotlight the revolutionary role of enzymes as biological catalysts and how leveraging vast datasets, sophisticated algorithms, and computational ingenuity can optimize and steer these natural wonders towards groundbreaking innovations in chemicals and materials production. Engage with influential thought leaders who will traverse the landscapes of predictive modeling, machine learning, and high-throughput screening, painting a vivid tableau of possibilities in enzyme design and application. Through dynamic discussions and exploratory dialogues, this session seeks to foster a harmonized vision, wherein the interplay of data and biology catalyzes a sustainable and transformative future in the realm of chemicals and materials.

The hype around Artificial Intelligence (AI) in drug discovery and synthetic biology has never been higher. With over $5 billion invested in AI-driven biotech startups in 2024 alone, the industry is racing to redefine biological design. But are we on the cusp of true transformation, or are there fundamental limits to what AI can achieve? In this panel, leading AI/ML pioneers and biotech innovators will explore the evolving landscape of AI in biological design. From accelerating drug discovery pipelines to engineering novel biomolecules, we’ll discuss the breakthroughs, bottlenecks, and the shifting role of high-throughput experimentation in an AI-first world. What will it take to move from promise to impact? And how can material providers and technology enablers help push the boundaries? Join us for a dynamic conversation on the future of AI in biotech—where it’s headed, and what it will take to get there.

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?