Leash Biosciences Raises $9.3 Million Seed Financing

Pictured above (from left to right): Brayden Halverson (scientist), Andrew Blevins (CTO), Ian Quigley (CEO, holding Belka), Nate Wilkinson (software engineer), Becca Levin (head of BD/strategy), Ben Miller (head of operations).

Leash Biosciences, a Salt Lake City-based artificial intelligence and machine learning (AI/ML)-native biotechnology company, has announced it has raised a $9.3 million seed financing round to advance its mission of revolutionizing medicinal chemistry through modern computational methods and massive biological data collection.

The oversubscribed round was led by Springtide Ventures with participation from MetaPlanet, Top Harvest Capital, Mitsui Global Investment, MFV Partners, Recursion CEO and co-founder Chris Gibson, and Recursion co-founder Blake Borgeson.

Leash aims to develop a foundational and generalizable machine learning model of medicinal chemistry that can accurately predict small molecule drug candidates for any protein in silico, and more broadly, interactions between any protein and any chemical. To achieve this, Leash is producing bespoke, expansive datasets of protein targets binding to chemicals.

To date, Leash has physically generated over 17 billion high-quality protein-chemical interaction measurements. The company plans to screen 500+ protein targets against many millions of machine learning-designed, proprietary chemicals by 2025.

“ML improvements in chess, Go, image recognition, language translation, text generation, and protein folding all were driven by the collection and curation of massive datasets. We believe a similar strategy will revolutionize how we approach medicinal chemistry," said Quigley. "We are thrilled to have the support of this group of top-tier investors who share our vision for transforming drug discovery through an ML-first approach."

To advance its machine learning engine, Leash will use the funding to scale its data collection and computational capabilities. The Company’s ML engine will also support advancing multiple internal therapeutics programs toward in vivo studies.

"Leash's platform stands apart with its combined excellence in machine learning, experimental biology, and medicinal chemistry," said Claire Smith, Lead Investor at Springtide Ventures. "We are excited to back this exceptional team as they leverage cutting-edge tech to tackle the toughest drug discovery challenges."

Alexey Morgunov of MetaPlanet added, "Leash sits at the forefront of innovating the next paradigm of AI-driven, scalable, and rapid drug design. We are honored to partner with them as thought leaders in this space."

The Leash team is comprised of TechBio veterans with expertise spanning AI/ML, biology, and chemistry. Five of the company's six employees are former Recursion employees with experience building and scaling transformational drug discovery platforms. The team also brings experience from Eikon Therapeutics, Myriad Genetics, insitro Biosciences, LinkedIn, Stripe, and other leading technology and biotechnology players.

In parallel, Leash announced the launch of its inaugural machine learning Kaggle competition, the Big Encoded Library for Chemical Assessment (BELKA). Leveraging a dataset of unprecedented scale, BELKA sets out to address one of the most critical challenges in drug discovery: predicting the likelihood of chemical materials binding to pharmaceutically-relevant targets. The competition will be hosted on the Kaggle platform, the world’s largest data science community.

"By providing participants with access to such a comprehensive dataset, we are empowering the global scientific community to develop innovative solutions that could revolutionize the way we identify potential drug candidates,” said Ian Quigley, Leash Bio CEO.

BELKA aims to contribute to groundbreaking advancements in predictive modeling for pharmaceutical research by harnessing the capabilities of artificial intelligence and machine learning. Participants will be tasked with analyzing a vast dataset comprised of 133 million physically-measured activities for each of three key protein targets.

Leash rigorously produced a dataset that exceeds all existing small molecule binding datasets combined. With 133 million molecules screened against each protein and evaluated with deep sequencing coverage and many replicates, participants will have access to an unparalleled wealth of data in scale and depth. Importantly, this competition dataset is larger than the world’s largest existing drug-target dataset (PubChem), providing a unique opportunity for groundbreaking insights and discoveries. It represents a small fraction of Leash’s screening data.

Committed to transparency and collaboration in scientific research, Leash plans to publicly release the full dataset of all conditions and replicates aggregated for the contest dataset, some 3.6 billion physically-measured interactions, at the conclusion of the competition. This resulting collection, expected to be released in the summer of 2024, will be approximately 10 times larger than the largest publicly available dataset to date and 1,000 times larger than higher-quality, curated public datasets, providing researchers worldwide with an invaluable resource for future drug discovery efforts.

The BELKA competition is open for registration and concludes on July 8, 2024. For more information, including participation criteria and registration, visit the competition page on Kaggle.

For a good summary of Leash, see Ian Quigley's detailed Linkedin description from 2023.