Last summer, Astera’s life science division, Radial, launched a project aiming to reimagine how biologists study protein motion experimentally.

Proteins are in constant motion, and this motion drives their function. But today, much of structural biology only captures static snapshots, representing the most common form a protein adopts. This is why we can predict a protein’s fold but still cannot predict whether a mutation will break a protein or how a drug may affect its target. These answers exist in protein motion. We set out to push the limits of the structural biology data we collect and to carefully evaluate which data types might actually be most useful for understanding motion in the first place.

The pilot project, called DiffUse, focused on one data modality: X-ray crystallography. We proposed that a certain type of X-ray scattering called diffuse scattering, long considered background noise in measurements of three-dimensional protein shapes, might be a key signal for reconstructing how individual proteins move.

Assessing the value of X-ray diffuse scattering wasn’t just a data-collection problem. It was a simultaneous experimental and computational challenge: new data goes to waste without quality computational models to make sense of it, and machine learning models can’t interpret motion from data formats not designed to prioritize it. DiffUse began by asking which parts of the structural biology pipeline must change before diffuse scattering can turn protein motion into usable structural information.

However, X-ray diffuse scattering is only one potential data type that could inform us about how proteins move. Given the progress the project and team have made, we are expanding to more modalities: DiffUse is becoming Prism. Prism is tackling a more ambitious goal: making protein motion measurable, actionable, and predictable. This will establish a new foundation for structural biology, changing what AI can know about proteins and transforming how we discover drugs, engineer biology, and understand disease.

A year ago, we committed $5M to seed DiffUse and have deployed $4.3M of that budget so far. Today we are re-committing $20M over the next three years to Prism.

Why motion matters

We started DiffUse because of a deep conviction that understanding and predicting protein motion is a key unsolved problem in biology, with clear real-world impact.

Consider Gleevec, the first cancer drug designed to hit a specific molecule. Developed in the 1990s for chronic myeloid leukemia, it targets BCR-ABL, a rogue kinase and one of more than 500 structurally similar kinases in the human body. Yet Gleevec binds BCR-ABL thousands of times more selectively than its relatives.

How did Gleevec get so selective? Clever experiments and good fortune: pharma chemists iterated with high-throughput screens, throwing every ligand at the wall to find the best candidate. But it turned out that the motion of BCR-ABL proteins was what actually allowed the selectivity to happen.

Proteins aren’t static. They wiggle and jiggle, alternating between multiple conformations. The most common form may seem the most important, but the transient shapes often hide the most druggable targets. Structural biology at the time couldn’t see this; it was still mapping static structures.

Now we can. Better experimental tools and machine learning are revealing the “grammar” of protein dynamics — which regions flex, which sites shift, where new drug pathways emerge. But to do this systematically, or to deliver the right data for AI to learn these lessons, we can’t tackle this one protein at a time, one technique at a time, or one part of the pipeline at a time. We have to re-establish the foundation of structural biology. This will allow us to design biology instead of stumbling into it.

Prism’s work to rebuild dynamic structural biology could help engineer more Gleevecs. 

The team envisions our work enabling the design of proteins based on dynamics data from the start, and unlocking new — and ultra-selective — drug targets.

A year of experimentation and learning

This has been a year of experimentation at DiffUse, with a few key open questions we set out to de-risk before expanding the project.

1) Does X-ray crystallography data contain useful, repeatable dynamics measurements?

Yes.

DiffUse had a clear hypothesis to test — that crystallography data contains reproducible hidden motion signals that we can collect at scale and now make sense of with modern AI. In the past year, DiffUse has supported that hypothesis with reproducible signals of protein dynamics; we have established a new kind of hybrid centralized-distributed team; and we have begun engaging the broader community to help shift the tide from a static picture of protein forms to a more holistic understanding of how dynamic forms create function.

Earlier this year, the DiffUse team showed that diffuse scattering is reproducible across different detector systems, beam profiles, and facilities. This ability to quantitatively measure the same ensembles consistently across different physical setups gives us confidence that any member of the structural biology community can eventually collect data.

Another key learning from this year is that our existing data is an underused resource. While new data collected with methods optimized for diffuse scattering will be valuable, the data already sitting in the PDB can be mined for this information as well. By reanalyzing 50+ years of existing PDB structures, we uncovered latent information on conformational heterogeneity just sitting in the PDB.

We believe that signals from other structural, functional, and sequence data will also be important for revealing the full spectrum of conformational ensembles and ultimately fully describing protein motion. Prism will now widen the scope to multiple types of structural biology data, beyond crystallography.

We imagine future models that can predict the function and movements of any protein based only on its sequence, or design drug candidates with selectivity we would consider rare today. This will require collecting and analyzing many different types of data. What data, in what combination, and in what quantity, is an open question in itself, and a focus of Prism.

2) Does a hybrid centralized-distributed organizational structure work well to advance this type of science?

Yes, but this is still an ongoing experiment.

Over the past year, we have hired dedicated full-time scientists to the project to stay laser-focused on getting the effort off the ground. They started testing the hypothesis, building professional-grade software, and — just as importantly — managing a unique collaboration with academia.

We have also assembled a group of world-class academic and national-lab team members who are generating protein ensemble measurements across multiple sites and inventing new ways to collect and use that data. This flexibility in structure allows us to experiment in new areas, demonstrate data reproducibility, and ultimately engage the scientific community, whom we will need as true partners to co-develop the large-scale version of dynamic structural biology.

From day one, James Fraser (UCSF), Nozomi Ando (Cornell), Mike Wall (Los Alamos National Lab), James Holton (LBNL, UCSF), and Steve Meisburger (CHESS) have been key team members driving forward our work on X-ray diffuse scattering. Today, we are announcing the addition of cryo-EM as the second structural biology data modality at Prism, led by Joey Davis from MIT.

This may seem obvious, but our key takeaway from this year is to recruit great people who are aligned on mission and strategy from day one. This has been particularly important given our commitment to sharing all our work as soon as it is ready, outside of journals. Luckily, the scientists most willing to experiment in how they share their work are also the ones most creative in their scientific approach. We are still looking for more scientists who want to redesign dynamic structural biology in the open and move fast on tools, methods, infrastructure, and, above all, biology that matters.

In collaborative projects like this, team members need to feel ownership over their individual contributions and see how their work fits into the bigger picture. The mix of a new organizational structure, fantastic people, and aligned scientific goals is making it possible to tackle a scientific problem this large.  

As Prism grows, we will continue to evaluate what the best structure is to advance our mission, with the willingness to evolve. 

3) Are there more effective open science approaches that increase the utility of our work?

Yes, and we are continuing to try new approaches!

The world around us and scientific research are changing far too fast for traditional scientific publishing to keep up. We believe scientists need to share their work in a way that allows others to learn from it, build on it, and use it on a timeline that keeps pace with modern science. That is the “impact factor” of our times.

One major uncertainty when seeding DiffUse was what might result from getting distributed academics to work together as one team, committed to sharing all our science openly, outside of journals.

We have been pleased to see this ongoing experiment pay off. The distributed team is not only making progress on their scientific goals, but also setting a new standard for open science in structural biology. They have continually published blogs, open-sourced code, and posted data and analyses to our open publishing server, The Stacks, as soon as their work is ready for feedback and reuse. 

A key part of our success is having scientists willing to experiment with new ways to communicate and share their science. Doing something new brings about a lot of unknowns. Discussing these openly as a group has led us to think about more creative solutions. We aim to share more details on this publicly in the coming months. 

We have learned that one bottleneck to putting out a wide diversity of outputs at the speed we are generating them is knowing where and when to share work, and having editing support. People want to share their work differently, but often don’t know how, when, or where to. To address this gap, we hired a Data Steward at Radial, and are hiring a science writer to support all our scientists, including those across Prism.

4) Can we better engage with the ecosystem to enable more testing and adoption?

Yes, but this is a work in progress.

From day one, our goal has always been community engagement. Rebuilding the foundations of dynamic structural biology will only be impactful if we build methods, tools, infrastructure, data, and models that the entire scientific community can contribute to and build upon.

Community engagement starts with people. This summer, we brought together many leaders across dynamic structural biology at the Conformational Ensembles Workshop. There, we presented our detailed plans for Prism and asked for community feedback and involvement. This feedback helped us reprioritize efforts, as explained further in our vision for Prism. 

We have also focused on building and quickly releasing useful tools for the community. For example, we released Sampleworks, an open-source, modular software for integrating experimental structural biology data, structure predictors (like AlphaFold), and guidance to improve the modeling of conformational ensembles. We have filled a major gap in the structure prediction space by releasing WaterFlow, a state-of-the-art method for placing water molecules around protein structures, allowing us to better understand how water molecules contribute to structure. We also recently shared PLUG, an open-source, modular framework for constructing leakage-controlled protein function benchmarks, enabling more rigorous evaluation of protein function models. We developed all of these methods openly, allowing the community to build on them before the official release. 

Ultimately, our policy of continually putting all our work out in public as quickly as possible is to enable rapid testing, reuse, and feedback. But we are always looking for ways to better engage with the ecosystem, and we welcome your ideas.

5) Can we find the right leader to expand and lead this?

Yes!

DiffUse began as a truly collaborative effort, with a small core group of founding scientists setting the early vision and strategy together. We always knew that a longer-term, expanded effort such as Prism would require true leadership. This could have been a very hard role to fill, and yet the obvious choice was right in front of us.

Earlier this summer, Stephanie Wankowicz transitioned out of her role as a faculty academic collaborator at Vanderbilt and joined Radial full-time as Prism’s first Scientific Program Director, setting the project’s overall scientific vision and strategy.

Stephanie is a relentless researcher, and her drive is infectious. Most structural biologists know that protein conformational dynamics are important. But Stephanie is a rare scientist who has done the experimental, computational, and leadership work to move the field toward dynamics. She not only has the ambitious vision of a world where we can observe, predict, and act on how proteins move, but she can also execute it. She began her research career studying clinical cancer genomics and computational biology at the Broad and Dana-Farber before moving into structural biology. Both at the Broad and as a graduate student at UCSF, Stephanie committed herself to the open science movement, advocating for industry labs and publishers to lead by example. She understands the big picture in a way that an unconventional multidisciplinary effort like Prism needs.

Help us rebuild dynamic structural biology

Prism is rebuilding the foundation of structural biology to transform how we discover drugs, engineer biology, and understand disease.

We want to hear if this approach to doing science resonates with you. 

The full-time Prism team has expanded from two to ten full-time researchers. We are growing. And we also want our wider network of like-minded researchers — in academic labs or industry — to help this effort grow with us.

If you’re excited by our vision for the future of dynamic structural biology, whether as a biologist, computational scientist, or hardware developer, get in touch about our open roles. Or if you have an idea to collaborate on, reach out!

We would love to hear from you and exchange ideas about the future of biology.

Today, the National Science Foundation (NSF) announced a partnership with the Astera Institute related to the first round of investment in building a national network of automated laboratory infrastructure, known as the Programmable Cloud Laboratories (PCL) Test Bed Initiative. The NSF will spend $380 million spread across 20 awardees to advance AI and automated instrumentation for scientific experimentation and data collection. The Astera Institute will contribute up to $20 million in matched investment and provide additional support and expertise to awardees to advance their open science practices to make their science more rapidly available, machine-readable, and reproducible.

This is the first time Astera is directly partnering with the federal government on a scientific program, but this partnership is not something we fell into by accident. Astera is continually looking for ways to promote and empower our approach to experimenting with how science is conducted and funded, including needed changes to scientific publishing that better and more quickly capture the full range of scientific outputs. Improvements on this front are not only enabled by AI but will also help fundamentally advance AI-enabled scientific discovery. 

Astera’s own Open Science Policy espouses the need to get scientific information out quickly using publishing platforms and practices that focus on scientific utility rather than legacy journal systems that are optimized for and constrained by human limitations. To this end, Astera has built an ecosystem of investments and opportunities around open science, including:

• The Stacks, an open-access publishing platform we are building with the end-goal of experimenting with different ways of making it easier for scientists to generate, structure, and share reproducible data that are geared toward reuse
• Funded Programs such as the diffUSE Project, the Deliverome Project, and OpenADMET, which are all working to collect and openly publish AI-ready scientific information
• The Hidden Science Competition, a recently announced competition to encourage graduate students to publish useful research beyond the constraints of traditional peer-reviewed scientific journals

We view our collaboration with the NSF as an important step in adding to the ecosystem of open and rapidly available scientific information that will help to propel innovation forward. We are excited about this program and mode of partnership because PCLs create an opportunity to advance a more data-centric model of open science. Rather than treating data as a secondary output released only after an embargo and narrative-centric publication, the PCL Test Bed Program can support workflows in which well-structured, reusable data and metadata are shared rapidly and treated as valuable scientific outputs in their own right. 

The instrumentation associated with the PCL Test Bed Program emphasizes automated experimentation in controlled environments, enabling researchers to design and test new tools for standardized data-generation processes and capture of high-quality metadata throughout each experimental workflow. This inherently makes experiments more reproducible and data more AI-ready.

These capabilities can uniquely enable rapid deposition into repositories and allow researchers and AI agents to more reliably discover, interpret, and reuse scientific outputs at scale while also supporting alternative publishing workflows organized around utility, reuse, and continuous deposition rather than a single final publication event.

Through this public-private collaboration, NSF, Astera, and the PCL Test Bed awardees can combine federal leadership, private support, and scientific expertise to make research outputs across fields more AI-ready, interoperable, and reusable—accelerating discovery, increasing the value of publicly funded research, and helping scientific advances translate more rapidly into societal benefit.

Astera recently hosted its first essay competition, focused around metascience. This competition was always more about starting a conversation than finding a bunch of perfect solutions. We wanted to hear from working researchers, not policy experts or administrators, about the structural problems they actually run into, described concretely enough that someone could do something about them. We also wanted that conversation to initiate a more public dialogue, which is why we required all submissions to be posted openly before being submitted to us.

We weren’t sure if this requirement would be a major blocker, but nearly 200 scientists openly published their ideas. Many of the essays attracted real engagement through comments sections and social media. We read every piece as well as the threads in which other researchers asked questions, offered suggestions, pushed back, or shared their own versions of the same problem. That was exactly what we were hoping to see. There’s something different about a scientist putting their name on a specific critique of how their field operates versus signing a letter or venting in a survey, and watching that happen across disciplines was genuinely encouraging.

Below we’re sharing some of the high level takeaways and the set of researchers that we are giving cash awards to. We’ve also reached out to a few submissions ourselves to explore potential paths forward that we could help support. And we’ve been sharing the essays with other funders and policymakers as well, in case that can help influence their strategic priorities at this critical moment in time.

By the numbers

We initially organized the essays into different broad thematic categories, which was informative. Whatever scientists are most frustrated about right now, it isn’t only about roadblocks happening at the individual level. “Community tools and resources” was the most common at 30%. Essential infrastructure for sharing, maintaining, and building on existing work and data is also badly underdeveloped.

In terms of the submitters themselves, essays came from academic researchers (53%), independent researchers (28%), industry scientists (8%), and nonprofit and government researchers (4%). Junior and mid-career researchers each contributed 61 essays, compared to 41 from senior researchers. We were delighted to hear from so many people closer to the beginning of their careers who are optimistic about the opportunity for systemic change. Ultimately, while many of the proposed solutions would benefit from funding, their successful execution would need a more bottom-up approach from researchers on the ground.

What scientists said

The top five broad themes below came from reading across all 195 essays.

1. Coordination failure

The most common frustration, cutting across nearly every category, was coordination failure. Not a shortage of ideas or data or even funding, but the absence of any mechanism for doing collectively what no individual lab or team can do alone. Valuable scientific assets like datasets, methods, null results, years of accumulated know-how lack shared infrastructure for sharing, maintaining, and learning from them. This category, which accounted for more than half the submissions, includes issues around community tools, data access, infrastructure. The essays in this bucket also tended to be unusually specific about what was missing and why, which was helpful for brainstorming fundable solutions.

Go here to see the catalog of submissions related to coordination failure →

2. Data issues

The state of scientific data came up enough to deserve its own category, especially given the increasing importance of data for any AI-related work. The main issues revolved around the quality and usability of the data itself (inconsistent formats, missing metadata, lack of standards and useful benchmarks). There’s a big need for more useful crosstalk between different datasets so that scientists can learn more from work across groups or measurement types. It’s clear that scientists have been navigating these problems for a long time, but the cost of not fixing them keeps going up. Before funding more data, what can we do to clean up and utilize what’s already available to us?

Go here to see the catalog of submissions related to data →

3. Scientific training

Training came up in 34 essays, not as a complaint about individual researchers but as an observation about what the PhD and postdoc pipeline systematically fails at, especially as the technological landscape changes. Specific skillsets that were repeatedly mentioned were ones around statistical rigor, computational analyses, cross-disciplinary fluency, and more connections to utility and translatability for greater impact.

Go here to see the catalog of submissions related to training →

4. Incomplete artifacts

The cost of invisibility was a fourth recurring thread. For example, untested ideas aren’t shared, null results often don’t get published, methods aren’t very reusable, and datasets get reused infrequently. A lot of the most actionable essays weren’t proposing new research programs; they were proposing the knowledge infrastructure that would make existing research compoundable.

Go here to see the catalog of submissions related to capturing the full scientific process →

5. Modernization

Finally, AI understandably came up a lot, and the essays we found most useful were the ones that treated it as a forcing function rather than the ultimate solution. The most compelling arguments were around structural gaps that have always existed and are now more costly to ignore and more possible to fix. If your data isn’t useful or interoperable, AI-assisted analysis doesn’t fix that. Without thinking more deeply about what data and data formats are most valuable to particular goals, scientists will still run into the same walls faster and potentially at greater cost.

Go here to see the catalog of submissions related to being more AI-ready →

Our review process

Below are a few notes about how we selected the finalist pool. Feel free to skip to the next section if you’d rather just read more about the winning submissions.

In short, we reviewed all 140 essays that passed initial triage against several dimensions:

• Was the submitter an active scientist? (defined as a researcher currently working on a novel research problem)
• How clearly and specifically the scientific bottleneck was described?
• How deeply the essay understood the structural reasons it persists beyond a single researcher or group?
• Is there a plausible pilot to test the hypothesis?
• And of course basic properties we requested, such as length and the existence of a public posting.

A note on format: the 3-page limit made author participation easier, but it also limited how developed the proposal could be. Articulating a deep problem and solution in three pages is hard, and in a few cases we could see the outlines of a stronger argument that required more space to flesh out. We did have to triage submissions that ran significantly longer, because comparing a 5-page essay with a 3-page one in a competition context isn’t fair. That said, we read all of them and may follow up on some of the longer ones that had compelling ideas.

The top 30 clustered into eight areas, which we presented to our judges for review and discussion: biological data standards and integration, clinical translation and healthcare (the single largest cluster), neuroscience methods and infrastructure, publication systems and knowledge, funding structures and research practice, data access and privacy barriers, interdisciplinary and cross-domain work, and specialized technical bottlenecks. Clinical translation being the largest was somewhat surprising; there were more specific, structurally sophisticated proposals about the gap between research findings and clinical practice than we expected.

The winners

When the panel finished its review, there wasn’t a clear top first-place essay. That wasn’t necessarily a failure of the judging, but perhaps a feature of the shorter length. We also learned that we embrace disagreement across judges, and our award structure should better reflect that.

Unsurprisingly, there was a lot of debate amongst our evaluators on what ideas could hold the most promise. Rather than award a first prize somewhat arbitrarily, we decided to forgo it and instead recognize more essays at the second and third prize level, which meant increasing our total prize budget. The eight essays below were the ones that most consistently combined a precise diagnosis with a credible path to doing something about it.

2nd Prize

3rd Prize

What comes next

This essay competition generated an amazing public catalogue of concrete cases where there could be fundable solutions to metascience challenges. We’re still working through what that means for our own funding priorities at Astera. But the volume and specificity of what came in was enough to move our thinking on a few things.

All 186 essays whose authors consented to sharing are linked on our website. We’re grateful for their willingness to do this, and we have shared their organized entries with other funders and policy advocates. We will continue to pay attention to the dialogue happening through and around the submissions (yes, we’re reading comment sections!). We are also thinking through what the next iteration of this effort might look like in terms of another competition or a funding call, so stay tuned on that front.

When Astera launched its life sciences division, Radial, earlier this year, we set out to reimagine how life science research happens at a systems level. Today we are announcing $5 million in seed funding to The Deliverome Project, a new fit-for-purpose nonprofit research organization building the toolkit to unlock a long-standing bottleneck in precision medicines: targeted delivery.  

The Deliverome Project will create the first comprehensive, open-source, multimodal atlas of proteins on the surface of human cells. Precision medicine has long needed a high-quality, comprehensive dataset that not only catalogs these biomolecules’ specificity and abundance, but also characterizes their ability to traffic cargo into human tissues. But no one group has had the right combination of technology, institutional structure, and incentives to pull it off. The Deliverome, led by cofounders Becca Carlson and Bobby Hollingsworth, are up for the challenge.

Precision medicine’s missing data problem

The payloads of precision medicine have never been more sophisticated. Today’s scientists can engineer CAR-T therapies for wiping out blood cancers; gene-editing to permanently correct heritable disease; RNA medicines that block harmful proteins from forming, and antibody-drug conjugates that destroy cancer cells while sparing healthy tissue. But precision medicine’s promise rests on our ability to get those revolutionary payloads to exactly the right cells in the body. Delivering cutting-edge therapy means inscribing the correct molecular address. And here, the field is flying blind.

The overwhelming majority of drugs in development today cluster around a small set of known, validated surface targets. That clustering isn’t a choice; it’s a data gap. Human tissues carry thousands of individual surface proteins; the vast majority (and their millions of pairwise combinations) remain undiscovered. Even for familiar proteins, biologists have little sense of how abundant they are across different cell types, or how they circulate cargo in and out of the cell. Neither a scientist nor an AI model can reliably predict which surface proteins will enable safe, effective delivery to a given disease.

A purpose-built, open-science solution

The Deliverome team will atlas the presence and nature of cell surface proteins using two increasingly sophisticated techniques. The first is mass spectrometry, which will measure surface protein abundance in different human tissue; the second will determine which proteins support cellular internalization of cargo. Both approaches are high-throughput by design. Deliverome will be able to interrogate thousands of surface proteins simultaneously.

The seed funding we are announcing today will allow the team to build out and validate the necessary technology platforms, hire a team, begin sample acquisition, set up computational infrastructure, and start sharing data. Any surface target the Deliverome maps may be the missing link for the next transformative therapy.

Deliverome’s methods and findings will all be released openly. Expect to find protocols, reagents, raw data, and analytical outputs shared continuously in AI-ready formats without waiting for journal publication cycles and with an eye towards reuse. The goal is to make the data generative for the entire ecosystem — every academic lab, every biotech company, every AI model building the next generation of precision medicines.

Why a dedicated nonprofit org is the right structure

Precision medicine’s glaring data gap on surface proteins is a too-common failure of the market. Pharma companies that build surface protein data keep it proprietary and narrowly focused. Academic labs lack the scale, coordination and incentives to execute at the required level. Venture-backed startups face immediate pressure to narrow to one therapeutic area, protect data as IP, and deprioritize the platform as they advance their first clinical asset. Everyone would benefit from a high-quality, comprehensive cell-surface protein atlas, but no one in the current system will build it. 

The Deliverome Project requires the resources and freedom to pursue the full problem space: industrial-scale execution combined with an open science mandate. Radial’s mission is to experiment with what science gets done, how science is organized, and what science produces. Structuring this project as an FRO allows the Deliverome Project to meet each of these ambitions. 

Ambitious science requires entrepreneurial, technical founders

The Deliverome’s cofounders Becca and Bobby each have a rare combination of deep technical fluency in experimental platforms, genuine conviction to open science, and the drive to build an organization from scratch. They bring with them years of experience in functional genomics and proteomics, cell biology and structural biology, platform building and target identification. 

Becca’s PhD in the Blainey and Hacohen labs at the Broad Institute led her to develop single-cell genetic analyses called optical pooled screens. She then moved into industry to scale science into useful technologies, first at a spatial biology startup and then in venture creation at Flagship Pioneering. Time after time, she noticed the same obstacle: platform biotechs had powerful delivery technologies, but their target data was too weak to actually deploy a therapy. 

Bobby’s path is complementary. His doctoral work with Hao Wu at Harvard investigated innate immunity with cryo-EM; and his postdoc with Wade Harper added spatial proteomics to his repertoire. Before Arena BioWorks’ closure last year, Bobby worked on their team identifying novel surface targets across disease areas. But beyond his technical background, Bobby’s long-standing commitment to open science inspired us. As a postdoc, he shared work publicly outside even traditional preprint infrastructure; at Arena BioWorks, he pushed the company toward a more open posture on their science; and recently when receiving the Experiment Foundation’s Beyond the Journal award, he responded to the news simply with “I don’t need the money, this is how I’d operate anyway.” That orientation is central to the Deliverome Project’s mission.

Both Becca and Bobby’s careers have been building toward this project. We’ve been impressed by their conviction, their speed, their ability to absorb and incorporate feedback, and their fundamental orientation toward building useful science that works for everyone. Additionally, Becca and Bobby’s detours through the applied world of platform biotech and drug discovery are a feature, not a bug. That experience sharpens their sense of exactly what’s missing and why it matters. There are very few places designed to support researchers who want to go back to the sandbox and address fundamental problems with this kind of clarity. We think that’s the profile this work demands, and it won’t be the last time we look for founders like them.

What comes next

Becca, Bobby, and their team will not be able to solve this problem alone. The Deliverome Project will require partners across areas such as human tissue sample sourcing, methods development, computational biology, and the broader biopharma and academic ecosystem. Inherent to the open science commitment and desire to build a truly useful resource is an appetite for feedback from the research community. 

If you’re a researcher working on precision delivery, a clinician with access to annotated tissue samples, a company with relevant platform capabilities, a scientist building off of our open work, or a funder who believes public-good data infrastructure in biomedicine deserves more serious investment, we want to hear from you. 

To learn more about The Deliverome Project, visit deliverome.org

Get in touch at contact@deliverome.org

Today, alongside the launch of Radial, we are opening an essay competition that I’ve been ruminating on for some time. Namely, inviting active scientists from any sector to share concrete research challenges that can inform our future work at Astera. We’re interested in your hypotheses about what broad structural or systemic issues contribute to the bottlenecks you experience in your own science. It’s important to me that we hear more from active scientists on the ground.

Many of our scientific systems and institutions are no longer fit for purpose. How we fund work, share results, build teams, and connect science to other disciplines or sectors has long been in need of experimentation. This is no longer a controversial statement.

We are living through a historical inflection point that demands change. One force is technological, happening at unprecedented scale and speed. AI is making it harder to ignore systemic and infrastructural gaps, while also changing what solutions are possible. This is an incredible forcing function we should leverage to update our scientific practices.

At the same time, it’s become harder to talk constructively about change in light of political differences and more recent budgetary contractions. But it’s more important than ever to openly debate long-term reform now. And many disagreements are unlikely to be resolved through debate in the absence of real life testing.

We’re looking to you, scientists

The field of metascience, i.e. the science of science, is often driven today by non-scientists: policy experts, economists, sociologists, psychologists, historians, politicians. Their work can be very useful, but practicing scientists should be more deeply involved in shaping the systems they depend on.

Scientists know first-hand what is broken. When scientists themselves have led metascience experiments, the outcomes have often been distinctive and more durable: new institutes structured around questions; focused research organizations built to unlock specific field-level bottlenecks; community infrastructure launched because there was simply no other way to make it happen; critical resources that can’t wait for permission.

We want to help get more scientists in the driver’s seat of this conversation and source more hypotheses that can be tested for systemic improvements. We want all of it to happen in the open to stimulate more useful public debate about science. And we hope that will help the most compelling ideas get real world implementation through support from us or others.

Examples of what we’re looking for

Perhaps an easier way to explain what we’re looking for is to highlight a few historical examples that we would have loved to fund early iteration for. Here are a few:

1. The Protein Data Bank

A few crystallographers were frustrated that hard-won structural data was disappearing into individual labs with no way to share it. They bootstrapped a community archive in 1971 with just seven structures and no formal institutional mandate. We would have loved to award an essay describing this gap and fund the early bootstrapping required to prototype the foundational data infrastructure for structural biology and drug discovery worldwide.

2. arXiv

The scientist Paul Ginsparg noticed that his colleagues were emailing preprints to each other and built a centralized server in 1991 to do it better. We would have loved to award an essay describing this gap and fund the initial server required to test the utility of what became today’s default open publishing infrastructure for physics, math, and computer science. It has since become a general model for the broader open-access movement.

3. Focused Research Organizations

Two scientists, Adam Marblestone and Sam Rodriques, were dead set on trying to generate more connectomics data as a critical public resource for the neuroscience community. This was a defined roadmap that required a start-up-like team, which lacked any dedicated funding mechanism. So they created one by inventing FROs, and it has become an enabling structure for many other projects with similar properties. We would have loved to fund early iterations of FRO projects (and we did through the first FRO: the longevity-focused Rejuvenome!).

4. Arcadia Science

This one’s an experiment I’m directly involved in that’s still in a work-in-progress. Arcadia is a for-profit research company co-founded in 2020 by myself and another scientist, Prachee Avasthi. It was motivated by trying to reimagine how we could more effectively traverse a wider swath of biology for useful discovery than was possible in our academic labs. We asked how we could use data to develop organism-agnostic tools, compound broader lessons by sharing more of our work in real time, and open up new funding and sustainability strategies. It would be exciting to fund smaller scale pilots that could inform experiments that lead to new institutes, which can and should be less monolithic than what dominates today.

I hope more scientists will join us in this dialogue, which is why I’ve asked that all submissions are public. I know it can sometimes be uncomfortable to put your neck out in this way, but positive change is more likely if we normalize open debate. We should approach all disagreements according to the scientific principles we were trained on. Data, not drama: let’s do the experiment.

See more details and apply here by May 1st.

Dileep George is joining Astera as Head of AI, leading our AGI research division. Working alongside our Chief Scientist Doris Tsao, he and the team will explore novel, brain-inspired computational architectures to accelerate the development of safe, efficient and aligned AGI. Astera will continue to support this effort with over $1 billion in committed resources over the coming decade.

Dileep joins from Google DeepMind, where he worked on frontier AGI research on agents with memory, planning and structure learning. Throughout his career, Dileep has shown that drawing on the computational principles of biological intelligence opens up novel, high-impact pathways for AGI research. At Vicarious, he scaled algorithms for visual processing and reasoning, gaining worldwide attention for breaking text-based CAPTCHAs with human-like data efficiency. He also pioneered AI-powered robotics as a service for industrial applications. At Numenta, he co-developed Hierarchical Temporal Memory, the theoretical framework modeling how the neocortex learns and reasons.

Dileep joins Astera alongside Miguel Lázaro-Gredilla, previously a Research Scientist at Google DeepMind. As Research Lead, Miguel will spearhead the development of world models that utilize hierarchical latent variables for long-horizon planning and robust reasoning.

Neuro-inspired AGI research is underexplored relative to its potential

The overwhelming majority of AI research today pursues a dominant paradigm: scaling transformer architectures trained on massive datasets. This approach has produced remarkable results and will likely continue to do so, but concentration around any single research direction leaves promising alternatives underexplored.

The principles of biological intelligence likely offer novel approaches to AI engineering at scale that aren’t captured in existing research paradigms. This could help address two sets of challenges that remain on the path to AGI:

1. Current AI systems lack fundamental capabilities that biological intelligence demonstrates. They can’t handle long-range planning that requires maintaining coherent goals across complex action sequences, or learn continuously from experience the way humans do. Massive datasets are still required for tasks where humans need only a handful of examples, and they continue to fail to generalize robustly to situations that differ meaningfully from their training data.

2. The safety and alignment challenges posed by current architectures remain unsolved, even as we continue to scale them. We don’t yet know how to build systems whose goals stay aligned with human values as circumstances change in ways they weren’t trained for. We can’t reliably interpret why models make the decisions they do, which makes it difficult to predict or prevent failures.

Commercial investments currently concentrate on scaling transformers, which risks trapping the field in local minima: optimizing a single approach while leaving vast parts of the solution space unexplored. Biological intelligence offers computational principles that current architectures don’t capture, opening pathways to systems that are more efficient and more naturally aligned with how humans think and perceive.

Bridging neuroscience and AI engineering

Efforts to map biological intelligence — how the brain constructs perception, cognition, and intelligence itself — remain disconnected from the engineering of AI systems. Neuroscience and AI research proceed largely in parallel with limited integration.

Providing decade-scale commitment and computational resources, Astera is running two research programs in tight integration:

• Decoding the brain’s computational architecture: Led by Doris Tsao, Chief Scientist for Astera Neuro, our Neuro division is working to decode the fundamental mechanisms through which the brain constructs intelligence. These capabilities represent some of the hardest unsolved problems in AI, and the brain solves them with remarkable efficiency.
• Building AI systems that learn like humans do: Now led by Dileep, our AGI division tackles the research and engineering challenges of building systems that exhibit these capabilities: how intelligent agents adapt continuously to changing environments, correctly attribute rewards to actions in scenarios with sparse feedback, and build hierarchical memory systems that enable efficient retrieval and generalization.

Dileep and his team will work closely with Doris, whose work has revealed some of the most detailed accounts of how neural activity produces perception to date. Together, they hope to create an iterative research program where neuroscience discoveries inform engineering approaches, and engineering challenges surface new neuroscience questions. Going forward, we hope to see others more tightly link basic neuroscience and applied AI work.

This work will be conducted in line with Astera’s broader commitment to open science. We believe progress on AGI is better served by distributed work across the field than by locking insights away.

The team this requires

We’re now building a team whose capabilities span deep theoretical investigation of biological intelligence, large-scale ML systems engineering, and experimental validation of novel architectures.

We’re actively looking for researchers and engineers with strong machine learning backgrounds and deep curiosity about neuroscience: people who want to investigate what’s missing from current approaches and build something better.

If this vision excites you — whether you’re a researcher, engineer, or someone who wants to work on foundational questions about intelligence — we want to hear from you.

There’s always a need for more ideas and talent in this area. If you have an interesting, underexplored angle you’d like to chase down, we’ve also recently opened a call for applications to the Neuroscience and Artificial Intelligence tracks of Astera’s residency program. Our residency is meant to support talented innovators seeding early-stage projects, especially those that might sit outside of what’s conventionally pursued. We’re building a community here that could be a great hub for this type of exploration. We hope you will consider applying.

Important science and technology development often falls through the cracks of public funding and private markets, i.e. work that may be high impact but risky, requires long timelines, or involves unpopular ideas. These areas are ripe for philanthropy. And as AI ushers civilization toward an event horizon, we need more people working on the hardest problems with many shots on goal.

Over the last five years at Astera, we’ve tested different approaches to funding and building ambitious technical work. We’ve explored a lot of directions to figure out where we think we can have the most impact. We’re now sharpening our focus on two areas: intelligence—both biological and artificial—and AI-enabled life sciences. Progress in either could help positively shape humanity’s future in critical ways.

Both areas benefit from more philanthropic support, as they involve open questions, unexplored territory, and long timelines. The right experiments aren’t always obvious, and success might look nothing like expected. We’ve also chosen them because we personally know them well. Jed is an engineer focused on neuroscience and intelligence foundations; Seemay is a biologist experimenting with how research gets done. We engage directly with technical details, which allows us to embrace more uncertainty.

Structure and flexibility for technical work

Creating an organizational structure that sustains this work over decades requires more than knowing where to focus. We’ve found the most effective technical efforts function like startups: flexible, nimble, guided by leaders with real authority to make technical calls. 

Like start-ups, they also need to be able deploy resources in a much more flexible way than is typical of most philanthropy. In addition to giving out grants, we find that work—especially of the more opinionated type—benefits from a wide range of tactical strategies, including hiring, contracts, competitions, and for-profit investments. 

Moving forward, we’re intentionally separating Astera’s foundation from the technical divisions it supports. The foundation handles shared operational and administrative infrastructure to enable technical teams that run semi-independently like start-ups. Each has a leader with deep expertise and CEO-like authority, supported by flexible, long-term capital and operational scaffolding through the foundation. These include:

Neuro & AGI divisions that explore how biological systems compute, how that relates to artificial systems, and what approaches might lead toward general intelligence. We think there’s a wider space of possible architectures than currently explored, and neuroscience offers crucial insights. This builds on work by our current researchers and fellows.

A life sciences division, where we’re rethinking how science gets done in the age of AI. Today’s scientific approaches were largely designed for a different era. AI has given new urgency to the need to reimagine our practices, motivating us to expand efforts around funding, structuring, and publishing approaches. We believe that the best way to innovate on this front is by iterating alongside active, ambitious research efforts. For instance, by embedding initiatives like The Diffuse Project with open science experimentation.

The right leaders

Our new start-up-like approach only works if there are the right leaders in place. We look for people comfortable with uncertainty, technical enough to engage directly, with a builder mentality to create what’s needed. Critically, we’ve sought out people whose primary experience is outside philanthropy from industry, startups, or research environments.

We’ve already been fortunate to attract such people. In the coming weeks, we’re excited to share about several exciting new folks who will be joining Astera to help lead new divisions in intelligence and life sciences. In parallel, we’re also refocusing the residency program to better prioritize people and ideas where we have long-term commitment and in-house expertise. 

We’re still learning

Astera has always been an experiment in doing philanthropy differently. This structure is another iteration. We have strong convictions but expect to keep adapting.

We’re eager to connect with people and organizations thinking about new approaches to funding or doing science. Or if you’re working on similar problems in intelligence or life sciences, we’d like to hear from you.

More at astera.org, or reach out at info@astera.org.

The Astera Institute is excited to launch a major new neuroscience research effort led by Dr. Doris Tsao, who will be joining as Chief Scientist for Astera Neuro. We seek to understand one of the deepest mysteries of science: how the brain produces conscious experience, cognition, and intelligent behavior. Astera will support this effort with $600M+ over the next decade.

Doris has spent her career developing one of the most detailed accounts of how neural activity gives rise to perception through work on the neural code and circuitry underlying face and object recognition. This work shows how a complex visual percept, object identity, is represented by a principled geometric code. Her recent work explores a new computational framework for how symbols first arise in the brain through specialized circuits for object tracking.

What are we doing?
Across every moment of our lives, the brain transforms raw sensory input into a coherent world filled with objects, relationships, meanings, and a sense of self. Yet we still do not understand the fundamental computational principles the brain uses to construct this internal world. Uncovering these principles would transform both neuroscience and technology–revealing the mechanism responsible for generating conscious experience, and at the same time, providing a new framework for AGI.

At the heart of our new effort is the conviction that true understanding of the brain’s internal model means being able to manipulate it in a controlled way. Towards this goal, we are betting that the brain’s representational architecture is compositional, built from elemental units and a neural syntax for combining them. By identifying these fundamental units and the rules that create and link them, we can uncover the brain’s infinitely generative internal code. This, in turn, would provide a principled way to construct or modify internal representations, much as knowing the words and grammar of a language allows the creation of an unlimited range of sentences and meanings. Such capability would mark a profound advance in understanding. 

The compositional framework remains a hypothesis, but pursuing it opens a path for fundamentally new kinds of experiments. The first step will be to measure neural activity through large-scale recordings across a rich variety of stimuli and behaviors, allowing us to characterize the underlying neural code. We will then attempt to write in hypothesized neural codes and thereby construct or alter internal representations according to proposed compositional rules. In this way, we can move neuroscience beyond passive observation and towards active, engineering-style tests of a model. Whether or not our hypothesis proves fully correct, this approach will accelerate our understanding of how the brain’s internal model is built.

A field ready for a paradigm shift 
The ability to precisely map and modify the brain’s internal model may sound like a lofty goal and indeed, for decades, progress in neuroscience was limited by technology. But that barrier has largely fallen, and we believe now is the right time for our moonshot. We now have the tools to interrogate the brain at unprecedented resolution and scale. 

What is needed next is a coordinated engineering effort to fully harness these tools. Advances in large-scale neural recording, targeted stimulation, chronic high-density interfaces, and computational modeling have created a unique moment where a focused, non-clinical, scientifically driven program can push far beyond what academic labs or clinically oriented companies alone can achieve. We intend to fill this essential gap between traditional basic research and clinically driven neurotechnology.

Progress towards our goals opens major branches of independent inquiry:

1. Inspiring new approaches to building and steering AI systems: Understanding the brain’s computational strategies—the architectural principles and representations—could reveal fundamentally different approaches to building AI systems that are orders of magnitude more efficient and naturally aligned with human cognition. Industry pursues only a narrow slice of what’s possible. We believe reverse-engineering the only generalized intelligence in existence could open up new pathways to general artificial intelligence. 
2. Deepening our fundamental understanding of biological intelligence and conscious experience: The brain is one of the universe’s wonders. What is the structure of neural activity required for a specific experience? What are the primitives of perception and thought? How does the brain represent itself? How do disruptions in the brain manifest as psychiatric and neurological conditions? We seek to develop a theory of conscious experience that successfully predicts the experiences that emerge when we write specific patterns to the brain. 
3. Opening pathways to revolutionary neural interventions: Today’s brain-machine interfaces work at the periphery, translating motor commands or delivering basic sensory inputs. But understanding deeper computational structures could enable interfaces that engage with the brain’s core representational system. This could have major therapeutic applications, for example, a visual prosthesis for the blind that restores vivid, naturalistic visual experience, not just pixelated sight.

Why Astera is pursuing this work
Since the founding of the Astera Institute in 2020, Obelisk, Astera’s AGI research program, has pursued the hypothesis that a better understanding of how intelligence arises in natural systems could reveal computational principles missing from current AI paradigms. The brain achieves flexible, general intelligence with roughly 20 watts of power. It constructs everything we experience—every object we see, every thought, every feeling—from patterns of electrical activity across ~100 billion neurons. It learns continuously from sparse data. It plans, imagines, and constructs a coherent model of the world. We don’t yet understand how.

Astera Neuro brings deep experimental neuroscience into direct dialogue with this work. We hope to create a tight iterative loop across teams where experimental findings shape AI architecture research, and computational questions drive new lines of neuroscientific inquiry. 

We believe Doris has developed what may be the most detailed empirical account of how neural activity produces perception so far. The potential of her work requires long-term investment. We are excited to work with Doris to test her model and systematically explore how the brain constructs reality in direct collaboration with Obelisk engineers and researchers exploring alternative approaches to AGI. The iteration between these basic and applied research efforts will surface things neither could find separately.

Research will be shared exclusively outside traditional journals as a forcing function for developing faster, more open, and more useful outputs that represent the full scientific process. As we’ve seen with other efforts, we believe such an approach will enable greater alignment of scientific goals and values across the team. We will also be iterating on ways to make these outputs more compatible with AI-driven discovery.

Building the team
We are excited for the opportunity to build this moonshot. We have a chance to experiment with how science can be done by designing our team and approaches in a purposeful way. This work requires capabilities that don’t typically collaborate as part of a cohesive iterative circuit at an institutional scale: neuroscientists who can design experiments on complex natural behaviors, ML engineers who can build models from massive neural datasets, optical engineers working on holographic optogenetics and advanced imaging, systems builders who can create scalable experimental infrastructure, and metascience innovators dedicated to accelerating all aspects of this work. 

Doris brings decades of foundational work on neural coding. For the next chapter we are building an exceptional founding team whose contributions will span large-scale reading and writing to neural circuits, clarifying the neural basis for cognition, and understanding brain function during naturalistic behavior. 

What do standardized, low-cost space telescopes, ultra-high-performance bio-inspired materials, and fusion energy that costs under 1¢/kWh all have in common? For one, each of these domains holds incredible potential to further human flourishing. And secondly, each represents a new idea that will be pursued over the next year at our Emeryville, CA campus.

We’re delighted to introduce three new residents to our Residency Program — a program in which residents receive a salary, a budget of up to $2M for team and expenses, compute access, lab space, and an exceptional community of talented like-minded peers, mentors, and investors.

Read on to learn more about our three new ambitious entrepreneurs, along with a brief overview of their work. In the coming months, we’ll be sharing more detailed profiles of the residents and their projects.

If you’re interested in applying to be a future resident, you can reach us at residency@astera.org, or subscribe here to receive our next call for applications, coming in early 2026.

Aaron Tohuvavohu – Cosmic Frontier Labs

Dr. Aaron Tohuvavohu is a physicist, astronomer, and explorer designing the next generation of space telescopes. He has designed missions and experiments across the electromagnetic and multi-messenger spectrum, with expertise spanning black holes, relativistic explosions, UV and X-ray instrumentation, and space systems engineering. Most recently, he led an 11-month sprint from clean sheet to launch of the highest-performance UV detector in orbit, and drove major upgrades to NASA’s Swift Observatory, significantly expanding its scientific reach, impact, and efficiency.

Project description

Cosmic Frontier Labs is building a new class of scientific tools to accelerate discovery and exploration of the Universe. We are expanding humanity’s cosmic horizons by scaling up the number and capability of orbital observatories, bringing Hubble-quality to fleets of telescopes rather than single flagships. By redesigning precision instruments for manufacturability and iteration, the team is moving space astronomy from an era of scarcity to one of abundance, continuous innovation, and exponential discovery.

These telescopes will form a platform for science that evolves as quickly as the questions we ask. We will build the platform iteratively, to continuously integrate new detectors, optics, and algorithms on successive units. In this near future, exploring the cosmos won’t depend on waiting decades for the next great observatory, but on a living, growing constellation of instruments; each a window into the expanding frontier of human understanding.

Open roles: Contact info@cosmicfrontier.org if you’re interested in the mission and want to explore ways to contribute!

Damien Scott – 1cFE

Damien Scott is a technologist and founder. Homeschooled in Botswana and shaped by science fiction, his north star is to build energy systems that move humanity up the Kardashev scale toward post-scarcity. His first entrepreneurial venture was founding Marain, an electric and autonomous-vehicle simulation and optimization company that was acquired by General Motors in 2022. His career has spanned energy and mobility systems across startups and large companies, including the extreme engineering environment of Formula 1 at Williams F1. Beyond racing, he worked on a wide variety of initiatives, from adapting uranium-enrichment centrifuge concepts, to electromechanical flywheel energy storage, to hybrid hypercars and automated mining systems. He has a BSc in physics from the University of Sydney and an MS and MBA from Stanford University.

Project description

Everything humanity values depends on abundant, inexpensive energy. Most usable energy across the universe is fusion…with extra steps. The last decade has brought major public and private progress towards cutting out those steps, to directly generate electricity from fusion, and bring us closer to abundant, low-cost energy. The 1cFE initiative builds on this progress to set our ambitions higher: could the cost of fusion reach below-1¢/kWh LCOE within the next ten years? We map cost-first corridors to sub-cent power, integrating physics, engineering, and manufacturing. We will also publish open analyses, and test how emerging AI capabilities can radically improve and compress cycles across science, first-of-a-kind engineering, and deployment. Our outputs are intended to steer R&D, capital allocation, and policy toward the fastest corridors to sub-cent fusion energy, thereby pushing humanity up the Kardashev scale and upgrading our civilization.

Open roles: Theoretical Physicist and Systems Engineer

Tim McGee – Impossible Fibers

Tim McGee is a biologist and materials innovator developing new ways for proteins and composites to self-assemble into high-performance materials. Trained in Biomolecular Science and Engineering at UCSB, his mission is to translate biology into design and manufacturing. As an early pioneer of bio-inspired design at Biomimicry 3.8, IDEO, and later his own firm, LikoLab, he has worked with global teams on challenges ranging from advanced coatings for food, to novel textile manufacturing, to the biophilic design of urban environments. Most recently, McGee founded Impossible Fibers at Speculative Technologies, leading a DARPA-funded collaboration to predict fiber properties directly from amino acid sequences. His work integrates biology, design, and engineering to create new manufacturing capabilities where materials are assembled from the nanoscale to the macroscale.

Project description

The Impossible Fibers Lab is building a new manufacturing environment that enables proteins to self-assemble into exceptional materials; fibers and composites with electrical, optical, and mechanical properties beyond what’s achievable today. Existing fiber production systems were designed a century ago, and were made for cellulose and plastics, not for the complexity of proteins. McGee’s team combines microfluidics engineering, encapsulation chemistry, automated liquid handling and robotics, and novel spinning techniques to explore how protein composites form, align, and transform during fiber fabrication. The resulting structured dataset will map the relationships between molecular sequence, process conditions, and material outcomes, creating the foundation for predictive, bio-inspired materials design.

In the long term, Impossible Fibers seeks to make matter programmable, from quantum interactions to custom product-scale performance, laying the foundations for a new era of materials manufacturing.

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Extending a warm welcome to our new residents, and stay tuned for a deeper dive into their work!