Atomwise is a company.
Atomwise has raised $171.1M across 4 funding rounds.
Key people at Atomwise.
Atomwise has raised $171.1M in total across 4 funding rounds.
Atomwise has raised $171.1M in total across 4 funding rounds.
Atomwise's investors include Asset Management Ventures, Baidu Ventures, B Capital Group, Kiersten Stead, Matt Ocko, DCVC (Data Collective), Mexican.vc, Section 32, SV Health Investors, Tencent Holdings, Vickers Venture Partners, Farzad Nazem.
Key people at Atomwise.
Atomwise is an AI-driven biotech company that develops preclinical solutions using machine learning and deep learning to accelerate small molecule drug discovery.[1][2][3] Its core platform, AtomNet, pioneered convolutional neural networks for molecular recognition, enabling rapid virtual screening of billions of compounds to identify, expand, and optimize drug candidates for undruggable targets and complex diseases like oncology, rare diseases, and inflammatory conditions.[1][3][4] Atomwise serves pharmaceutical companies, biotech firms, and academic researchers by reducing time and costs in hit discovery, lead optimization, and toxicity prediction, with partnerships including Eli Lilly, Bayer, and others, plus joint ventures like vAirus for antivirals.[2][3] Recently, it nominated its first internal development candidate—a TYK2 inhibitor for inflammatory diseases—marking a shift toward building its own drug pipeline while maintaining collaborative services.[4]
The company has screened over 1 billion protein-small molecule interactions and supports 100+ academic projects via AIMS Awards, demonstrating strong growth momentum through a 74% success rate in novel compound identification, outperforming traditional high-throughput screening.[2][5]
Atomwise was founded in 2012 by Abraham Heifets (CEO with a computer science background) and Izhar Wallach, aiming to tackle common and orphan diseases too costly and time-intensive for traditional pharma by deploying AI's convolutional neural networks—the first such application in drug discovery for molecular recognition.[3][6] The idea emerged from combining massive chemical libraries with deep learning to predict interactions like hydrogen bonding and aromaticity, even for novel molecules, validated on benchmarks like DUD-E where AtomNet topped structure-based algorithms.[3]
Early traction came from a high-volume, low-touch model partnering with biopharma for hit discovery and optimization, evolving into global collaborations and joint ventures.[2][4] A pivotal moment arrived with the nomination of its first AI-discovered TYK2 inhibitor in 2025, appointing Neely Mozaffarian as CMO to advance internal pipelines into clinical trials, validating the platform's ability to explore uncharted chemical space.[4][5]
Atomwise rides the AI-drug discovery wave, applying image/speech recognition tech (convolutional neural networks) to pharma's core challenge: screening vast chemical spaces for undruggable targets amid rising R&D costs.[3][5][6] Timing aligns with a "generational shift," as AI enables billions-scale evaluations without precise structural data, outperforming 50% success rates of legacy high-throughput screening and addressing failures in 90% of traditional efforts.[2][5]
Market forces like exploding unstructured data (petabytes ingested) and demand for faster oncology/neurodegenerative therapies favor Atomwise, influencing the ecosystem via open academic support and biopharma validations that de-risk AI adoption.[2][7] It democratizes access to novel molecules, pushing competitors toward hybrid AI-human workflows and accelerating therapies for rare/inflammatory diseases.[1][4]
Atomwise's transition to an internal pipeline—led by its validated TYK2 inhibitor—positions it to prove AI's end-to-end value, potentially delivering clinical proofs beyond partnerships.[4] Upcoming trends like expanded chemical exploration and multi-modal AI (integrating protein structures/toxicity data) will shape its path, with clinical trials validating 74% hit rates to attract big pharma buyouts or IPOs.[3][5]
Influence may evolve from service provider to full pharma player, inspiring AI-native drug hunters while tying back to its 2012 mission: making undruggable cures feasible through smarter, faster discovery.[6]
Atomwise has raised $171.1M across 4 funding rounds. Most recently, it raised $120.0M Series B in August 2020.
| Date | Round | Lead Investors | Other Investors |
|---|---|---|---|
| Aug 1, 2020 | $120.0M Series B | Asset Management Ventures, Baidu Ventures, B Capital Group, Kiersten Stead, Matt Ocko, DCVC (Data Collective), Mexican.vc, Section 32, SV Health Investors, Tencent Holdings, Vickers Venture Partners, Farzad Nazem, Karen Bergman | |
| Mar 1, 2018 | $45.0M Series A | Austin Walne, Stuart Peterson, Baidu Ventures, B Capital Group, Kiersten Stead, Matt Ocko, DCVC (Data Collective), Faber, Mexican.vc, Tencent Holdings, Vickers Venture Partners, Farzad Nazem, Karen Bergman | |
| Jun 1, 2015 | $6.0M Seed | ACME Capital, Adeyemi Ajao, DCVC (Data Collective), Matrix, Streamlined Ventures, Think + Ventures, Karen Bergman, Shervin Pishevar, Taz Patel | |
| Mar 1, 2015 | $120K Seed | Mexican.vc, Mike Tsao, Ulrich Gall |
