Just over ten blocks south of Grand Central Station, in the Cure building, a drug discovery startup named after New York State’s motto—Excelsior, meaning “ever upward”—is aiming to transform small‑molecule chemistry and sow the seeds of what its co‑founders boldly call a “molecular industrial revolution.”
Artificial intelligence (AI) can design molecules faster than ever, but it still hasn’t yielded a single FDA‑approved drug—highlighting the widening gap between computational design and real‑world chemistry. Now, Excelsior Sciences, a company that emerged from stealth late last year, is working to address this bottleneck by rethinking how small‑molecule chemistry is executed and integrated with modern technology. Instead of automating traditional synthetic workflows, the company has developed a new form of modular chemistry designed from the ground up for machines—chemistry that can operate in continuous closed loop systems linking synthesis, biological testing, and AI.
At the core of Excelsior’s approach is a system of automated, synthesis-friendly chemical building blocks—called “smart bloccs”—that enable programmable carbon–carbon bond formation and function as tokens in a chemical language. Much like peptides that are assembled in sequence, these molecules are constructed iteratively by snapping carbon–carbon bonds together. By unifying automated synthesis, high-throughput testing, and AI-driven optimization within a single scalable framework, Excelsior hopes to position itself not as an alternative to traditional chemistry but as an infrastructure intended to extend its reach.
Excelsior Sciences’ co-founders, CEO Michael Foley, PhD, and COO Jana Jensen, PhD, spoke with GEN during a recent tour of the company’s lab space about Excelsior’s founding and goals.
A COVID-era inflection point
Foley and Jensen first worked together at Deerfield Discovery & Development (3DC), an in‑house R&D engine that integrates discovery science, translational research, and early development, where they partnered to build the team, infrastructure, and collaborations behind a new model for innovative drug discovery. Both are scientists by training, but they think like builders—Foley with a founder’s instinct for what’s possible and Jensen with a talent for turning ambitious ideas into working systems. And both had seen up close how much modern drug discovery still relied on chemistry that was highly manual and difficult to scale.
During the COVID-19 pandemic, Foley and Jensen saw firsthand how research shut down globally. For Foley, this disruption reinforced a longer-standing realization: chemistry’s effectiveness depends heavily on human presence and physical access to labs. Although chemistry hasn’t changed in a couple hundred years, Foley told GEN that its practice still depends on people being in labs, doing highly skilled work. When that stops, “everything stops—science stops, healthcare stops.”
At the same time, AI and automation technologies continued to advance rapidly. According to Foley, COVID-19 revealed an opportunity to rethink how chemistry could be practiced—more continuously, more reproducibly, and more tightly integrated with computation. For Excelsior’s co-founders, it seemed that at this moment, chemistry, technology, and the scientific needs finally aligned—an inflection point that led to the launch of Excelsior, which has since raised private and public funding, including a $70 million Series A co-led Deerfield Management, Khosla Ventures, and Sofinnova Development and a $25 million grant from New York State’s Empire State Development program.
Integrated discovery, compact Manhattan space
A defining feature of Excelsior’s approach is the tight physical integration of synthesis, purification, and biological testing within a single facility. Rather than distributing chemistry and biology across multiple sites—or outsourcing key steps—the company designed its laboratories so that molecules can be made, evaluated, and iterated on in rapid succession.
“What’s distinctive is the continuous learning cycle,” Foley told GEN. “We’re going to make molecules right there and test them just 40 feet away.”
By minimizing the distance—both physical and operational—between synthesis and testing, Excelsior reduces delays that can otherwise slow feedback and introduce variability. The company currently operates approximately 20 biological assays, each with daily controls to ensure consistency and data quality before results are incorporated into AI-driven optimization.
Automation alone isn’t enough. “It’s really linking that automation and that AI enabling that makes the platform so powerful,” Jensen told GEN.
That level of integration is also shaped by Excelsior’s location. Headquartered in custom-built laboratory space at Cure in the city, the company operates under space constraints that, according to its leadership, encourage efficiency and system-level thinking. Excelsior’s Midtown lab feels less like a traditional chemistry space and more like a compact, robotic micro‑factory. The room is dense and intentional—tiny‑home efficiency meets the automation age.
The smart blocc storage towers rise like miniature skyscrapers, as if a warehouse system scaled down to lab size. Around them, robotic arms shuttle samples through Excelsior’s automated assay platforms. Nearby, acoustic liquid handlers plated compounds with sound‑wave precision, droplets flying without a single pipette tip in sight. The whole space embodies the company’s “molecular industrial revolution” ethos—not as a slogan, but as a physical reality. It’s chemistry reimagined as something scalable, automated, and surprisingly elegant.
Where AI excels—and why human judgment still matters
Excelsior’s leadership is careful to frame AI as a complement to chemical expertise, not a replacement for it. Drug discovery, they noted, remains an inherently human endeavor—driven by scientific judgment, hypothesis generation, and an understanding of biology no model can fully replicate.
Despite all the machinery, the human team is small—just over a dozen people—a mix of scientists, software engineers, and automation specialists working in a tightly interdisciplinary group. Their role feels more like orchestration than bench work. During the tour, one of the few hands‑on moments was Olivia Goethe, PhD, the process chemistry lead, diligently tuning purification conditions—a reminder that even in a highly automated lab, expert judgment still anchors the workflow.
Humans are good at asking the right questions, Jensen told GEN, but drug discovery also requires optimizing dozens of parameters simultaneously—solubility, permeability, stability, toxicity, and more—and that’s where the human brain reaches its limits.
AI, she said, excels at navigating those tradeoffs, identifying patterns across large, multidimensional datasets, and proposing combinations that would be difficult for individual researchers to navigate. But that capability only becomes meaningful when paired with rapid, reliable experimental feedback. Foley emphasized that AI’s impact on drug discovery has often been overstated because the experimental systems feeding those models have not kept pace.
From discovery outputs to scalable impact
Despite its platform orientation, Excelsior is explicit about what it ultimately produces: “The output is a drug molecule, something that is ready to go into animal testing,” Foley told GEN. The company hasn’t disclosed specific therapeutic areas or drug candidates, and for now is focused on building the chemistry platform itself.
At the same time, the company views its chemistry and processes as deliverables in their own right. By using the same underlying chemistry from early discovery through scale-up and manufacturing, Excelsior aims to reduce handoffs, shorten development timelines, and preserve the feedback loops that allow AI to remain useful beyond the earliest stages of optimization.
That continuity underpins Excelsior’s longer-term ambition: to demonstrate that chemistry designed for automation can scale seamlessly from hypothesis testing into production. “From day one, [scalability] was part of the deal,” Foley said.
When asked how throughput and scalability will evolve into the production space, Jensen explained: “Some of the issues that we’ve already talked about [are] the demand on labor for artisanal chemistry. So, we remove that. This is ideal for continuous flow manufacturing, which decreases the footprint required for manufacturing—which is another reason why we’ve outsourced much of the manufacturing.”
She continued: “Finally, because we have simplified chemistry to just a few reactions under a few conditions, it means that from an environmental perspective, you have many fewer things to actually manage—much fewer waste streams, much more efficient and sustainable in that respect.”
For Excelsior, the promise of AI in drug discovery will only be fully realized when chemistry itself can operate as a programmable, data-generating system. The company’s bet is that by giving chemistry the tools to work at machine speed, it can remain the central engine of drug discovery—just with a much larger reach.
