Accelerating the discovery of therapies for aging and its related diseases
Over the past few decades, an unignorable amount of evidence has piled up that we are able to slow the biological processes of aging in animals. This evidence has been accumulating along multiple lines of research covering many different therapies¹ led by Stanford, UCSF, and more of the world’s best labs — and in all cases, we’re left with the same conclusion:
By understanding and directly treating the biological damage accumulated while aging, we can find powerful new therapies for fighting disease and living healthier, longer lives.
At Spring Discovery, we’re accelerating the discovery of these therapies with our machine learning-based drug discovery platform. And we’re proud to announce that we’ve raised a $4.25M seed round from a team of bio+tech funders who support our long-term vision: General Catalyst, First Round, Sam Altman, Laura Deming’s Longevity Fund, Ling Wong’s Sea Lane Ventures, Joe Betts-Lacroix, Greg Brockman, and more.
Why do therapies focused on aging present such a profound opportunity? Because aging is the single greatest risk factor for the most detrimental diseases on Earth — cardiovascular disease, neurodegenerative disease, pulmonary disease, cancer, muscle wasting, and more — and drugs that slow the biological damage accumulated while aging have the potential to reduce the incidences of these diseases, possibly simultaneously.
Combined, the above graphs represent A) one of the most important problems facing humanity and B) a problem that looks increasingly possible to tackle. The diseases of old age don’t discriminate, but they can be fought.
We believe that in the not-too-distant future the discovery of therapies for aging will provide some of the most effective tools in history for reducing our burden of disease and extending our healthy lifespan.
Spring Discovery’s mission is to dramatically accelerate the realization of that future. And we’re bringing a new set of machine learning tools to bear on this challenge.
The past five years have also seen a flourishing of new (and old!) machine learning technologies which are uniquely well-suited to accelerate this research. Machine learning is giving us new tools to learn from massive and complex biological datasets in ways that were completely inaccessible up until recently. These newfound powers have already started powering novel forms of scientific discovery, and we’re still at the very beginning.
Machine learning is particularly helpful for aging-related research because it can dramatically accelerate experimentation in a field that otherwise evolves slowly. The aging research mentioned above, while promising, suffers from frustratingly slow iteration cycles due to the unique challenges of measuring the complex and gradual progress of aging-related disease. Spring Discovery’s novel machine learning-based drug discovery platform shortens the timeframe for experimentation and, in doing so, reduces the wait from research breakthrough to promising drugs.
We’ve built a cross-functional team of expert aging scientists working together with machine learning folks. We’re grateful to partner with an advisory board of world-class leaders in aging research, drug development, and tech. And we’re thankful to have funders that both support our long-term vision and bring a range of bio+tech expertise to bear.
We’re growing. We’re looking for data scientists / applied ML / applied research scientists, so reach out: careers@springdisc.com. We value building a diverse, inclusive environment and welcome all applicants regardless of gender, sexual orientation, ethnicity, race, education, age, or other personal characteristics.
To accelerating the discovery of therapies for aging, to dramatically reducing burdens of disease, and to fighting back against the grim reaper,
-The Spring Discovery Team
¹ The lines of evidence supporting these claims come from the fields of senolytics (drugs that remove senescent cells), observations of existing small molecule drugs’ impact on animal lifespan (such as Metformin and Rapamycin), genetic manipulation, heterochronic parabiosis (exchanging blood between young and old animals), and caloric restriction. Laura Deming’s overview of this research is a great place for any newcomer (or expert!) to start.
