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Artificial intelligence and robotics for development and research of new drugs In an era where artificial intelligence (AI) is transforming nearly every industry, the intersection of AI and drug discovery is emerging as one of the most promising frontiers. The recent rise of companies like Lila Sciences and Recursion Pharmaceuticals reflects growing confidence among investors and researchers that AI could unlock previously unattainable scientific insights, accelerating drug discovery and reshaping scientific exploration. Lila Sciences , with its ambitious goal of creating "scientific superintelligence," and Recursion Pharmaceuticals , with its AI-powered platform for mapping human biology, are at the forefront of this movement.
Backed by hundreds of millions in funding and leveraging the latest advancements in AI scaling laws, these companies are positioning themselves to drive breakthroughs in medicine, materials science, and beyond. Most of us are familiar with Moores’s Law of computing power doubling. These companies are examples of how AI has rapidly grown based on distinct scaling laws , discussed below Lila Sciences combines generative AI with a network of autonomous labs, where AI systems design, test, and refine scientific hypotheses in real-time.
The company's goal is to build a self-reinforcing loop in which AI continuously generates and tests new ideas, accelerating the scientific method and leading to discoveries human researchers alone could not achieve. According to Lila's co-founder and CEO Geoffrey von Maltzahn , "Our hypothesis is that by scaling experimentation, we can unlock emergent abilities and enable discoveries that remain hidden at smaller scales." For centuries, scientific progress has followed a methodical but inherently human-limited path: hypothesize, experiment, analyze, repeat.
This approach has yielded remarkable discoveries, yet the vastness of potential chemical, biological, and physical interactions means that even our brightest minds can only explore a fraction of possibilities. Lila Sciences , founded in Flagship Pioneering's innovation labs in 2023, aims to overcome these limitations by developing "scientific superintelligence" – advanced AI systems capable of not just processing existing scientific data but autonomously generating hypotheses, designing experiments, and extracting meaningful insights at scales impossible for human scientists. Lila has already demonstrated applications in materials science, developing catalysts for green hydrogen production and materials for carbon capture – critical technologies for addressing climate change.
Similarly, Recursion has established a processing pipeline and neural network platform that has identified potential treatments across multiple disease categories, creating an impressive pipeline of drug candidates. AI map of biology While Lila Sciences focuses on scientific superintelligence, Recursion Pharmaceuticals , founded in 2013, has been building an AI-enabled map of human biology. Headquartered in Salt Lake City, Utah, Recursion combines experimental biology, bioinformatics, and machine learning to identify potential treatments for diseases at unprecedented scale and speed.
Recursion's platform integrates automated biology, chemistry, and cloud-based computing to test thousands of compounds in parallel. The company aims to overcome "Eroom's Law" of drug discovery—the paradox that despite advances in technology, the cost and time required to bring new drugs to market have continued to rise. Recursion seeks to reverse this trend by using AI to automate and accelerate the early stages of drug discovery.
The company’s AI models analyze cellular-level data to identify patterns and predict compound interactions with biological systems. By creating a comprehensive map of human cellular biology, Recursion hopes to uncover novel drug targets and therapeutic strategies more quickly and cost-effectively than traditional methods. "Recursion is not just trying to find the next drug; we're trying to redefine how drugs are discovered altogether," explains CEO Chris Gibson .
"The combination of AI and large-scale biological data has the potential to unlock entirely new categories of medicine." What makes companies like Lila and Recursion possible today – rather than a decade ago – is our deepening understanding of how AI systems scale and improve. Three critical scaling laws now guide development in the field: The first scaling law demonstrates that larger models, trained on more data with greater computational resources, exhibit predictable improvements in intelligence and accuracy.
This principle has driven the development of billion- and trillion-parameter transformer models that form the backbone of modern AI systems. For scientific applications, this means AI systems can now ingest and process the entirety of scientific literature – millions of papers, experimental results, and theoretical models – creating a knowledge base far beyond what any individual scientist could master. Once foundation models are pretrained, they can be specialized for specific domains through techniques including fine-tuning, pruning, quantization, and distillation.
"The post-training ecosystem of derivative models could require around 30 times more compute than training the original foundation model," notes Andrew Beam, Ph.D., CTO of Lila Sciences .
“This massive computational investment allows us to create models specifically optimized for different scientific domains.” For drug discovery companies, this means creating specialized models that understand protein folding, molecular interactions, cellular biology, and chemical synthesis – each requiring domain-specific training but building on general scientific knowledge. Perhaps most revolutionary for scientific applications is test-time scaling – allowing AI systems to reason through complex problems during inference rather than providing immediate answers.
"On challenging scientific questions, this reasoning process might take minutes or even hours," explains Kenneth Stanley, Ph.D., Senior Vice President at Lila Sciences , "requiring over 100 times the compute of traditional AI inference.
But the result is a much more thorough exploration of potential solutions, similar to how human scientists would approach a complex problem." This capability enables AI to break down complex scientific questions, explore multiple potential solutions, and show its reasoning – a critical feature for scientific applications where transparency in the discovery process is essential. Success in this space requires extraordinary interdisciplinary talent, combining expertise in AI, biology, chemistry, and robotics.
Lila Sciences has assembled an impressive team, including renowned geneticist George Church, Ph.D.; AI expert Andrew Beam, Ph.
D.; and AI research pioneer Kenneth Stanley, Ph.D.
, known for his work on neuroevolution and open-ended algorithms. Recursion similarly boasts an interdisciplinary team combining expertise in experimental biology, machine learning, and drug development, allowing them to bridge the gap between computational predictions and laboratory validation. As AI models continue to grow in complexity and capability, the competitive landscape in drug discovery and scientific research is likely to shift.
Companies that can harness AI scaling laws and build autonomous experimentation platforms will have a distinct advantage in discovering novel treatments, materials, and energy solutions. Lila Sciences and Recursion Pharmaceuticals represent two complementary approaches to this challenge. Lila’s focus on scientific superintelligence positions it to drive breakthroughs across multiple domains, while Recursion's deep expertise in biology and drug discovery gives it a strategic edge in developing new medicines.
The race to build scientific superintelligence is only beginning. But if the early success of Lila and Recursion is any indication, AI-driven platforms could soon unlock discoveries that redefine human health, energy production, and scientific understanding itself..
