Original author: Will Awang
Original source: Web3 Lawyer
Since ancient times, emperors and generals have had endless yearning for immortality, and this is still the case today. This pursuit of life continuation and exploration of the frontiers of science have taken on a new direction with the help of blockchain technology. The rise of decentralized science (DeSci) has provided new hope and possibilities for the exploration of the frontiers of science.
What first caught my attention about DeSci was Pfizers investment in VitaDAO. This is not only Pfizers first investment in the Web3 field, but also a sign of the recognition and support of the traditional pharmaceutical giant for the DeSci field. Combined with my own entrepreneurial background in digital healthcare, I began to think about how to reimagine the business model through DeSci.
The DeSci research report From Challenges to Opportunities: How DeSci Reimagines Science published by Binance Research first proposed the phenomenon of Death Valley in the scientific research process, then introduced DeSci, responded to the Death Valley through DeScis innovative solutions, and finally summarized the current DeSci landscape in the market, indicating that DeSci is mature enough to influence the way scientific research is conducted today. Although there are some gaps and challenges in the current situation, solving the Death Valley in research is already a big step forward.
Following the idea of the research report, in fact, in the process of transforming scientific research into commercialization, DeSci can be more integrated with blockchain technology and Web3. Let’s take medical research and development as an example:
Data acquisition: Data from early basic research and translational research can be obtained through DePIN, and further enhanced with the help of AI. The benefit is that it can cover the world and provide incentives.
Data storage: These data can be stored on the chain through encryption technology to maintain the immutability and security of the data, while building a new form of publication that is open and accessible to everyone, which to a certain extent solves the problem of replicability and reproducibility of scientific discoveries;
Community of interests: Through the rules formulated by the DAO organization, a community of interests between basic research and clinical treatment can be achieved. This rule can be further expanded to cover multiple links such as the entire research, clinical, commercialization, and doctor-patient scenarios to achieve a win-win situation for all parties;
The picture presented by DeSci in the future will be: decentralized organizations (DAOs) composed of multi-party interest communities, with common goals and visions, no longer constrained by capital profits, deeply combining blockchain technology and Web3, promoting scientific discoveries, and accelerating the implementation of substantive products, promoting the development and progress of the entire society.
Although DeSci is still in its very early stages, it is actively influencing the way scientific research is conducted today.
The following is the content of From Challenges to Opportunities: How DeSci Reimagines Science. Enjoy:
01 Core Viewpoint
The scientific research process faces major challenges, especially in the transition from basic research to translational research for practical applications. The “valley of death” phenomenon causes 80%-90% of research projects to fail before human trials, and only 0.1% of candidate drugs become approved treatments.
Misaligned incentives between academia, funding agencies, and industry have led to challenges such as insufficient RD funding, reduced collaboration between scientists and clinicians, and poor replicability and reproducibility of scientific discoveries, ultimately causing most research to stagnate in the valley of death.
Decentralized Science (DeSci) is a movement that leverages the Web3 stack to create innovative scientific research models that can address the challenges outlined above.
By using decentralized autonomous organizations (DAOs), blockchain, and smart contracts, DeSci solves critical coordination problems. This enables different groups of stakeholders to align their capital interests, thereby incentivizing them to advance research to the clinical stage.
The market has identified four key innovation areas in the DeSci space:
Infrastructure, including sub-sectors such as funding platforms and DAO tools, which form the cornerstone of DeSci DAO.
research, including a grassroots DeSci community hosting events around the world and a DAO with an aligned vision from multiple stakeholders.
Data services, including publishing and peer review platforms that support open access scientific publications, and data management tools that provide strong data integrity and collaborative access controls.
Memes can directly fund scientific experiments or serve as an investment vehicle for other DeSci projects.
While the existing stack can already support basic and translational research, it is less suited to clinical research, an area where products have direct benefit to patients.
All in all, decentralized science is mature enough to impact the way science is done today. While there are some gaps and challenges in the current landscape, addressing the “valley of death” in research is already a big step forward.
02 Introduction
2.1 Background of Traditional Scientific Research
The process of generating new knowledge and inventions in the scientific industry can be divided into different stages, mainly basic research and clinical research. These two main stages are connected by translational research. The key function of translational research is to transform the results of basic research into practical applications that can be tested by clinical research. The ultimate goal of this process is to commercialize research findings and create products that benefit society.
(Figure 1: The “Valley of Death” is the stage between basic science and clinical science where most research fails)
However, one of the biggest challenges in this process is the “Valley of Death” phenomenon, where many scientific efforts fail due to a lack of effective translational research.
According to the National Institutes of Health (NIH), 80 to 90 percent of research projects fail before they reach human trials. Furthermore, for every drug that receives FDA approval, more than 1,000 drug candidates were developed that ultimately failed. Even at later stages, challenges remain—almost 50 percent of experimental drugs fail during Phase III clinical trials. To put this into perspective, the probability that a new drug candidate will progress from preclinical research to FDA approval is only 0.1 percent. This staggering statistic highlights the significant challenges of translating knowledge and innovation developed at universities and research institutions into actual products or treatments for human application.
(Figure 2: The number of new molecules approved per $1 billion of global RD spending has been declining)
Exacerbating these challenges is the increasingly inefficient RD process in drug discovery. In the United States, the cost of developing and approving a new drug doubles approximately every nine years—a phenomenon known as Eroom’s Law, the inverse of Moore’s Law. Some reasons may be stricter regulatory standards, a high bar for new medical discoveries to meet needs different from existing drugs, and the high costs of contract research organizations to design and run clinical trials. If the status quo continues, the cost of developing a single drug for the biopharmaceutical industry could be as high as $16 billion by 2043. This financial burden often causes the industry to focus on developing more profitable drugs, which often overshadows the urgency of addressing other critical health needs.
This inefficiency has significant economic and social consequences. High RD costs, coupled with frequent failures, lead to escalating healthcare costs, which are ultimately borne by patients, governments, and insurers. In addition, delays and failures in translating research findings into viable treatments mean that patients often do not receive potentially life-saving opportunities, exacerbating public health challenges. For example, rare diseases and conditions that affect smaller groups are often overlooked because they are considered less profitable, despite the urgent need for treatment.
2.2 Why most research cannot get out of the “valley of death”
The fundamental problem is misaligned incentives, which lead to three major challenges: insufficient funding, reduced collaboration between researchers and clinicians, and poor replicability and reproducibility of scientific discoveries. These challenges ultimately lead research into the valley of death.
We explore these key challenges in more detail below:
2.2.1 Lack of funds
Lack of funding, especially when moving from the basic research phase to clinical research, can be attributed to misaligned incentives between funders and researchers, as well as a lack of transparency in the grant review process.
From a funder’s perspective, they will prioritize research that can be turned into products that generate recurring revenue. The knock-on effect is that given the competitiveness of obtaining funding, researchers are more inclined to work in line with funders’ expectations, which makes research more conservative and effectively stifles innovation.
Furthermore, an opaque review process means that a single proposal presented to different panels may yield different results. When grant review panels are unpaid, other complications may arise, such as bias from competing researchers, poor attention to detail, and significant delays in grant approvals. This means that researchers tend to spend more time producing publications to establish their place in the scientific community, rather than conducting experiments.
2.2.2 Reduced collaboration between researchers and clinicians
Given that most research stagnates in the “valley of death,” coordination between basic researchers and clinicians during translational research is critical.
Effective collaborations facilitate the design of innovative clinical trials that incorporate biomarkers or targeted research approaches from basic research. For example, oncology has made significant advances through collaborations, with genetic and molecular discoveries in the lab directly informing targeted therapies and trial designs for specific cancer subtypes. This synergy reduces the risk of late-stage trial failures and increases the likelihood of delivering effective treatments to patients.
However, there is currently little incentive for basic scientists (focused on discovery) and clinicians (focused on patient care and clinical research) to collaborate. Promotion in basic science research is often tied to the number of grants funded and publications in top journals, rather than contributions to clinical science and medical advances. Conversely, many clinicians’ success is determined by how many patients they treat, and they often do not have the time or motivation to conduct research and pursue funding opportunities.
As a result, the two groups ended up working in silos, which meant that the potential for combining laboratory findings with clinical relevance was reduced.
2.2.3 Low Replicability and Reproducibility of Scientific Discoveries
Reproducibility refers to the ability to obtain consistent results using the same data, methods, and computational steps as the original study. On the other hand, replicability involves conducting a new study to obtain the same scientific findings as before. If scientific findings are not reproducible and replicable, it is difficult to prove the validity and rationality of basic research, and thus difficult to expand to clinical applications.
Challenges in translating animal studies to humans contribute to inefficiencies—only 6% of animal studies are said to translate into human responses. Other issues, such as methodological differences (e.g., the type of coating on the test tubes, the temperature at which the cells are grown, how the cells are agitated in culture) can also lead to a complete failure to replicate results.
While the scale of the problem can largely be attributed to the complexity of science, misaligned incentives between publishers and early-stage researchers also contribute to the lack of reproducibility and replication of scientific discoveries. Publishers play an important role in training early-stage researchers, and published work can increase credibility and thus the chances of receiving funding. As a result, researchers who achieve statistically significant results on their first try are less inclined to repeat their experiments and instead publish directly.
03 Decentralized Science 101
3.1 What is DeSci?
Decentralized Science (“DeSci”) is a movement to create new scientific research models leveraging the Web3 stack.
Blockchain is uniquely positioned to address the challenges outlined above. It provides a trustless way to coordinate funds while ensuring a transparent and immutable way to track and record progress so that the interests of all stakeholders are considered.
DeSci is still in its infancy in the crypto industry. This can be seen from its total market cap of just over $1.75 billion and the fact that there are only 57 projects tracked under the DeSci category on CoinGecko. To put this in perspective, DeFAI (Defi x AI Agent) has a total market cap of $2.7 billion with only 41 projects, while the broader Crypto AI market cap is $47 billion (as of January 15, 2025).
3.2 How DeSci copes with the “Valley of Death”
As mentioned earlier, most research fails in the “Valley of Death” because incentives are misaligned, leading to challenges such as insufficient funding, reduced collaboration, and poor replicability and reproducibility of scientific results. DeSci can solve this coordination problem by using decentralized autonomous organizations (DAOs), blockchain, and smart contracts.
Below, Binance Research summarizes how DeSci provides solutions to existing challenges, first presented in a table format for clarity and ease of understanding, and then explained in detail. As a movement, DeSci addresses these challenges by:
3.2.1 How does DeSci solve the problem of funding shortage?
DAOs can serve as capital formation tools for research funding, and participants can be a mix of patients, researchers, and investor communities. Since stakeholders share the common goal of getting research into the clinical stage and ultimately commercializing it, they have a common incentive to help research cross the “valley of death.”
Decisions are made through decentralized token governance, where voting can be conducted in a transparent and democratic manner. Smart contracts then enforce the parameters decided by the DAO while ensuring transparency. Examples include programmatically released milestone funding, tokenizing intellectual property (IP) generated by funded scientific research, and subdividing IP and distributing it to all DAO participants to align interests.
Overall, DAOs in the DeSci space can provide an integrated end-to-end approach from basic research to clinical research by coordinating various stakeholders to collaborate towards a common goal in a trustless manner.
3.2.2 How DeSci addresses the problem of reduced collaboration between researchers and clinicians
As mentioned above, the main reason for the decrease in collaboration is the different incentives between researchers and clinicians. This can be addressed by participating in a DAO, where research hypotheses, experimental methods and parameters can be agreed upon at the time of the DAO’s creation, thereby coordinating research results. Coupled with IP tokenization, both researchers and clinicians can receive sufficient incentives and rewards to advance research to the clinical stage.
Other tools to foster greater collaboration include platforms that encourage incentivized peer review, where rewards can be programmatically distributed via smart contracts after a successful review. This can bring clinicians closer to researchers by providing early input that, once successful, can guide research toward actual implementation in the clinical phase. On-chain reputation systems could also be built around members of the scientific community based on their contributions to various DeSci DAOs, peer review efforts, clinical implementation, etc., where any work done for the advancement of science is appropriately attributed.
3.2.3 How DeSci solves the problem of low replicability and reproducibility of scientific discoveries
One way to address this problem is to record the research methods, experimental design, and every step on the blockchain. The blockchain is an immutable ledger, which ensures that other researchers have a full view of the experiments conducted and can query every variable if they wish to repeat the experiment.
Furthermore, a new form of publishing that is open and accessible to all can be built using Web3 primitives, where all research (even failed research) can be shared. This would eliminate publication bias, where only successful experiments are published, as data from failed experiments is still valuable.
Another area where DeSci can help is in data integrity and compliance. While traditional archival storage can meet this need, they often rely on tapes, which makes data retrieval slow. Given the dynamic nature of scientific research, which involves working on the same data across multiple parties while keeping it immutable and secure, decentralized storage and data warehouses can be a solution. They can provide the necessary data access controls, offer greater redundancy by eliminating single points of failure, and provide fast data retrieval for collaborative work. This promotes greater rigor in scientific research and increases the likelihood of replicable and reproducible results.
04 DeSci Landscape Overview
4.1 Key innovation areas
Binance Research has identified 4 key areas of innovation in the DeSci landscape: Infrastructure, Research, Data Services, and Memes.
Infrastructure includes sub-sectors such as funding platforms and DAO tools (e.g. IP tokenization, DAO formation, and legal agreements). These form the cornerstone of DeSci DAO, which is at the forefront of scientific discovery.
Research includes grassroots communities such as DeSci Global, DeSci Collective, which host events around the world to connect DeSci enthusiasts, and DAOs that bring together common interests from multiple stakeholders. These DAOs often focus on different areas of science, such as longevity, hair loss, women’s health, and more.
Data Services include publishing and peer review platforms that enable open access to scientific publications, thereby promoting more collaboration, and data management tools to provide strong data integrity and appropriate access controls.
Memes represent the interest of retail investors in the market and can bring more awareness and education to the DeSci field, which is usually limited to academia. Some Memecoins directly fund scientific experiments, while others serve as investment vehicles for other DeSci projects.
4.2 Sub-sectors worth noting
A. Infrastructure: Intellectual Property (IP) Tokenization/Fragmentation
Intellectual Property Tokenization plays a transformative role in advancing translational science by addressing a fundamental barrier in research and innovation: the monetization and liquidity of intellectual property (IP).
Traditional IP management and trading systems are cumbersome, centralized, and often inaccessible to smaller stakeholders, limiting the speed at which discoveries can be commercialized and translated into real-world applications. By leveraging blockchain technology, IP tokenization creates a decentralized and transparent framework that enables researchers, investors, and other stakeholders to more efficiently participate in and fund innovation projects.
IP tokenization involves converting intellectual property into digital assets, making it tradable and liquid. Projects like Molecule embody this process by introducing the concepts of IP-NFT (Intellectual Property Non-Fungible Tokens) and Intellectual Property Tokens (IPTs). IP-NFTs bring intellectual property onto the chain, while fragmentation allows multiple stakeholders to jointly manage intellectual property. The desired outcome is the coordination of stakeholders to ensure that there is sufficient funding to advance research to the clinical stage and ultimately achieve commercialization.
B. Infrastructure: DAO formation
DAO infrastructure represents a key innovation in the decentralization of science, enabling communities of patients, scientists, and biotech professionals to collectively fund, manage, and own scientific projects. Traditional science funding is often limited by centralized institutions, strict gatekeeping, and opaque processes. DAO infrastructure breaks this mold by providing a transparent, decentralized framework for the planning, funding, and governance of scientific initiatives.
Through DAOs, stakeholders can pool resources, make collective decisions, and directly influence the trajectory of scientific research. The BIO Protocol is an example of this, which supports the creation, funding, and governance of BioDAOs. Each BioDAO has its own expertise and focuses on a different scientific field, such as longevity (VitaDAO), cryopreservation (CryoDAO), hair loss (HairDAO), womens health (AthenaDAO), etc.
C. Infrastructure: Funding Platform
Web3 funding platforms are changing the way scientific research is funded by decentralizing the process and enabling broader participation. Traditional research funding often relies on grants and institutional support, which can be slow, bureaucratic, and limited in scope. Through crowdfunding, it provides researchers with the opportunity to connect directly with funders, communities, and collaborators, promoting a more transparent and inclusive funding ecosystem.
These funding platforms may also differ in who they fund, such as Catalyst (which funds DeSci IPs), Bio.xyz Launchpad (which funds DeSci DAOs), and pump.science (which funds compound testing).
The composability of Web3 enables different crowdfunding platforms to coordinate stakeholders at all stages of research, facilitating a seamless funding ecosystem. For example, a DeSci DAO funded through Bio.xyz can organize funding for specific IP research through Catalyst, or test and validate compounds in a transparent manner through pump.science.
D. Data Services: Publishing/Peer Review Platform
The traditional model for publishing scientific research is often slow, expensive, and inaccessible, with high article processing charges (APCs) and limited transparency in peer review. Additionally, researchers rarely receive credit or compensation for their contributions to the peer review process. This slows down the pace of review and increases the potential for bias due to conflicts of interest. Overall, this hampers the pace of scientific progress and limits access to knowledge for a wider audience.
Incentivized peer review and publishing platforms aim to address these issues by creating open and transparent systems where researchers are rewarded for their contributions, including publishing, reviewing, and collaborating. By integrating blockchain technology and community governance, these platforms democratize access to scientific knowledge, accelerate the dissemination of research, and foster collaboration among researchers around the world. One example is ResearchHub, where researchers can earn token rewards for peer reviewing articles or collaborating with like-minded people in their scientific fields of interest. Positive contributions to the scientific community can be recorded on-chain, building a reputation for the scientist and unlocking features like auditing and access control.
This is also where the intersection with AI becomes interesting. Projects like yesnoerror, an AI agent that finds math errors using OpenAI, are already live. It is able to find math errors, identify falsified data, and detect numerical inconsistencies that could compromise scientific integrity at scale, with little to no downtime.
E. Data Services: Data Interoperability and Integrity
The healthcare and biomedical research industries are plagued by fragmented data systems, lack of transparency, and a lack of patient-centric practices. Patients often donate valuable data and biospecimens for research, but have no visibility and control over how their contributions are used, and rarely benefit from the scientific or commercial value generated. These gaps lead to mistrust, privacy breaches, and reduced participation, especially among marginalized and underrepresented communities.
Data interoperability and integrity aims to address these issues by creating systems that empower patients with transparency, control, and shared benefits while enabling seamless collaboration between researchers, institutions, and businesses. Interoperability systems allow for the coordination of disparate data sources, making them available across the network while protecting data privacy and integrity. This ultimately accelerates scientific discovery, streamlines clinical development, and builds trust in biomedical research.
One example is AminoChain, a decentralized platform designed to connect medical institutions and support user-owned healthcare applications. It gives patients control over their own data and samples, ensures transparency into how their data is used, and lets them share in the value generated by research. Other decentralized data solutions include Filecoin, Arweave, and Space and Time, where data is securely stored with no single point of failure while providing flexible access controls to ensure that data is adequately processed.
05 Conclusion
We are in the early stages of DeSci, a decentralized approach to science that will become increasingly prominent in the way science is conducted today. DeSci has the potential to coordinate stakeholders from the earliest stages of research to ensure there is enough interest to advance research to the clinic.
The infrastructure to coordinate research in a decentralized manner already exists. Aligned stakeholders can formalize their shared interest in scientific research in the form of a DAO, provide funding and conduct research, own the resulting intellectual property, and share data securely within data protection guidelines to strengthen collaboration between different scientific communities.
However, the existing stack is better suited for basic and translational research, and less suitable for clinical research. The former research stage requires more trustless coordination, while the latter requires coordination with centralized parties such as regulators, pharmaceutical companies, physical laboratories, etc.
Additionally, the legality of DAOs remains an area of ongoing debate and regulatory development. In the Ooki DAO case, the U.S. District Court for the Northern District of California ruled that Ooki DAO was a person under the Commodity Exchange Act, setting a precedent that DAOs can be held legally liable. This decision has significant implications for DAO members because it suggests that token holders who participate in governance may be personally liable for the actions of a DAO. Given the lack of clarity regarding the treatment of DAOs, this may dissuade potential funders.
All in all, DeSci has matured enough to impact the way science is done today. While there are some gaps and challenges in the current landscape, addressing the “valley of death” in research is already a big step forward.
