The Discovery Engine is a general purpose automated system for scientific discovery, which combines machine learning with state-of-the-art ML interpretability to enable rapid and robust scientific insight across diverse datasets. In this paper, we benchmark the Discovery Engine against five recent peer-reviewed scientific publications applying machine learning across medicine, materials science, social science, and environmental science. In each case, the Discovery Engine matches or exceeds prior predictive performance while also generating deeper, more actionable insights through rich interpretability artefacts. These results demonstrate its potential as a new standard for automated, interpretable scientific modelling that enables complex knowledge discovery from data.

The prevailing model for disseminating scientific knowledge relies on individual publications dispersed across numerous journals and archives. This legacy system is ill suited to the recent exponential proliferation of publications, contributing to insurmountable information overload, issues surrounding reproducibility and retractions. We introduce the Discovery Engine, a framework to address these challenges by transforming an array of disconnected literature into a unified, computationally tractable representation of a scientific domain. Central to our approach is the LLM-driven distillation of publications into structured "knowledge artifacts," instances of a universal conceptual schema, complete with verifiable links to source evidence. These artifacts are then encoded into a high-dimensional Conceptual Tensor. This tensor serves as the primary, compressed representation of the synthesized field, where its labeled modes index scientific components (concepts, methods, parameters, relations) and its entries quantify their interdependencies. The Discovery Engine allows dynamic "unrolling" of this tensor into human-interpretable views, such as explicit knowledge graphs (the CNM graph) or semantic vector spaces, for targeted exploration. Crucially, AI agents operate directly on the graph using abstract mathematical and learned operations to navigate the knowledge landscape, identify non-obvious connections, pinpoint gaps, and assist researchers in generating novel knowledge artifacts (hypotheses, designs). By converting literature into a structured tensor and enabling agent-based interaction with this compact representation, the Discovery Engine offers a new paradigm for AI-augmented scientific inquiry and accelerated discovery.

BRYAN, Texas, Jan. 05, 2023 (GLOBE NEWSWIRE) -- iBio, Inc. (NYSEA:IBIO) ("iBio" or the "Company"), an AI-driven innovator of precision antibody immunotherapies, today announced that the United States Patent and Trademark Office has issued U.S. Patent No. 11,545,238, entitled "Machine Learning Method For Protein Modelling To Design Engineered Peptides," which covers a machine learning model developed to design engineered epitopes which allow precise steering of therapeutic antibodies towards specific regions of a target protein. "We are thrilled to be issued this U.S. patent, which solidifies our position as a leader in AI-driven drug discovery, and whose claims guarantee broad coverage of our proprietary, epitope-steering antibody discovery engine," said Martin Brenner, DVM. "In addition to marking an important milestone as we transform iBio into an AI-powered biotech company, this patent provides us with a competitive advantage as we continue to build our differentiated pipeline, with benefits that extend to our potential future partners." It uses a combination of a proprietary epitope steering technology, a specialized antibody library, and AI-powered antibody optimization to quickly identify and optimize molecules that can effectively address challenging drug targets. This allows for faster discovery compared to traditional antibody discovery methods.

VANCOUVER, British Columbia--(BUSINESS WIRE)--AbCellera announced today that it has closed its Series A financing led by DCVC Bio, a Silicon Valley venture capital fund focused on deep technology ventures that lie at the nexus of artificial intelligence and biotechnology. AbCellera will use the USD $10 million financing to accelerate the growth of their therapeutic antibody discovery business, including investments to build capacity and integration of advanced technological capabilities spanning computation, protein engineering, and immune repertoire profiling. "With this financing, we will double-down on our partnership business that has enjoyed profitable, triple-digit growth over the past 3 years. In DCVC Bio, we have found the ideal funding partner to help us accelerate the success of our full-stack antibody discovery engine, one that integrates artificial intelligence with industry-leading microfluidic screening technology," said Carl Hansen, CEO of AbCellera. AbCellera's high-throughput single B cell screening platform and repertoire sequencing technologies enable discovery of large multidimensional data sets of valuable antibody sequences.

Brief Recognition: Elena Grewal leads a team of data scientists responsible for the user's online and offline travel experience at Airbnb. Her team partners with the product team to understand and optimize all parts of the product, using experimentation and machine learning in a wide variety of contexts. Prior to Airbnb, Elena was a doctoral candidate in the Economics of Education program at the Stanford University School of Education. She received a B.A. in Ethics, Politics, and Economics, with distinction, from Yale University, and a Masters degree in Economics at Stanford University. She was also the recipient of the Stanford Interdisciplinary Graduate Fellowship.

The home feed, a collection of Pins from the people, boards and interests followed, as well as recommendations including Picked for You, is the most heavily user-engaged part of the service, and contributes a large fraction of total repins. The more people Pin, the better Pinterest can get for each person, which puts us in a unique position to serve up inspiration as a discovery engine on an ongoing basis. The home feed is a key way to discover new content, which is valuable to the Pinner, but poses a challenging question. Given the ever increasing number of Pins from various sources, how can we surface the most personalized and relevant Pins? Pinnability is the collective name of the machine learning models we developed to help Pinners find the best content in their home feed.

As Devin Coldeway of Tech Crunch puts it, "Just as you could supply the probable details of a pixelated face because you are familiar with how faces look, the AI can extrapolate as well, having examined on a pixel by pixel basis what certain features look like at various levels of detail."