The world needs more inventors Inventions are made where research tools, talent and funding come together. By bringing these three components together, LabDAO is building a generator of tokenised inventions. Let's Build a Home for Inventors Online Inventors need access to scientific tools, dynamic teams, and funding to do their work. At LabDAO we provide these through three building blocks: Lab-Exchange: a peer-to-peer protocol exchange on which computational and wet lab services can be traded. Fees generated on the exchange flow into the lab-fund Lab-Teams: an open toolbox for scientists to onboard into the decentralized science space and launch their own "web-lab" within the community Lab-Fund: a set of funding mechanisms that will support scientists, open-source developers and inventors within the ecosystem

This is a short memo summarizing LabDAO, an ImpactDAO. To learn more head to our extended memo, read the docs and join our discord. Problem Running code has a low cost and a high reproducibility. In contrast, life science laboratory services have a high cost and a low reproducibility. To accelerate human progress, can we abstract laboratory services into code? Running a laboratory or scientific compute infrastructure is expensive, but the marginal cost of running one experiment is cheap. In principle a laboratory could run additional experiments as a service for members of its community, but there are no open standards for communicating instructions or transferring compensations. Centralized cloud labs have tried to abstract laboratory services into code in the past, but haven’t been successful. This is because of the high capital expenditure to build and run a lab, and the low probability of meeting all customer needs with a monolithic infrastructure. Life science software-as-a-service companies offer hosted services, such as sequencing data analysis, to customers. These companies have been benefitting greatly from the open source communities that have developed most of the software tools they are offering. Given their centralized nature, these SaaS companies are publicizing their software development needs while privatizing the income generated by hosting these tools. The developers of useful open source software are not rewarded for their inventions by the providers of their tools.

Let's build open tools to generate protein binders at scale. Problem Therapeutic antibodies, ADCs, BITEs, DARPins, and CAR-based therapeutics are platform technologies that are giving rise to an increasing number of modern (and very expensive) medicines. These platforms are all united by the fact that an engineered protein needs to bind tightly to a target protein associated with a disease. This type of protein engineering is a minimization task: The input is the 3D structure (a .pdb file) of a target protein and the goal is to find the sequence of amino acids that code for a protein, referred to as a binder, that binds tightly to the target. Despite the great overall potential of protein binders, the tools used to design these therapeutics are expensive and inaccessible. For example, most display methods take multiple weeks to generate promising antibody candidates and tools for computational protein design are pubblished by academic scientists without a further incentive to make the tool run at production-grade scale. A particularly interesting use-case for protein binders as therapeutics against aging related diseases, is the clearance of senescent cells from the human body by senolytic cell therapies. Previous work by Amor et al., demonstrated that senescent cells can be effectively cleared from the body using CAR-T cells targeting the surface protein encoded by the PLAUR gene. While most CAR-T cells are based on scFv binders, prior studies have demonstrated that computationally more tractable DARPin binders can also be used for cell therapies. Solution

At the heart of LabDAO is the idea that a community operated open source marketplace protocol for laboratory services could 1) accelerate progress and 2) generate a dynamic knowledge graph for biomedicine, where the receits of past transactions among scientists serve as its building blocks. To create the marketplace protocol and the subsequent knowledge graph, we need to work out a structured way to describe scientific services and the data they generate. We need standards for composable metadata. Scientific data should be FAIR Scientific data is ideally stored according to FAIR principles, developed by Mark Wilkinson. These include: Findability Accessibility Interoperability