# Chai Discovery Secures $70M for AI Drug Development _AI drug development startup Chai Discovery has raised $70 million in a Series A round led by Menlo Ventures. The new capital will be used to advance its AI platform and pursue previously inaccessible drug targets. As part of its expansion, former Pfizer Chief Scientific Officer Mikael Dolsten is joining the company'..._ **Published:** 2025-08-07 **Source:** https://www.startuphub.ai/ai-news/funding-round/2025/chai-discovery-secures-70m-for-ai-drug-development --- Chai Discovery, a San Francisco-based AI therapeutics company, has raised $70 million in a Series A financing round. The round was led by Menlo Ventures, also including investment from its Anthology Fund, a joint partnership with Anthropic. The fund aims to identify and support promising AI companies. In addition, new investors included Yosemite, DST Global Partners, SV Angel, Avenir, and DCVC. Existing backers Thrive Capital, OpenAI, Dimension, Neo, Lachy Groom, and Fred Ehrsam also participated in the round. https://youtu.be/pHDw4PxmtdE?feature=shared ## Chai-2: A Game-Changing Breakthrough in Antibody Design The timing of this funding round coincides perfectly with Chai Discovery's announcement of **Chai-2**, their latest AI model that has achieved what many considered impossible. The model demonstrates a remarkable **16% binding rate** in de novo antibody design, a 100-fold improvement over previous computational methods. ### Revolutionary Success Rates Transform Industry Standards Traditional antibody discovery methods face significant limitations. Animal immunization and large-scale library screenings are expensive, time-consuming, and often fail against challenging targets. Previous computational approaches, while promising efficiency, still required massive experimental screening due to disappointingly low hit rates. Chai-2 changes this paradigm entirely. The model achieved: - **16% overall binding rate** across diverse protein targets - **50% target success rate** - finding at least one successful binder for half of all tested targets - **68% hit rate** in miniprotein binder design with picomolar affinities - **Two-week discovery timelines** versus months or years with traditional methods ### Impressive Experimental Validation The company challenged Chai-2 to design up to 20 antibodies or nanobodies for 52 diverse protein targets. Importantly, none of these targets had existing antibody binders in SAbDab, making this a true test of zero-shot generative capability. The results exceeded all expectations. In a single experimental round, the model discovered viable hits across a wide variety of challenging targets, routinely finding successful binders when testing just 20 designs. ## Advanced AI Architecture Drives Success At Chai-2's core lies a sophisticated **multimodal generative architecture** that integrates all-atom structure prediction with cutting-edge generative modeling. This enables the creation of novel, epitope-specific binders across diverse modalities. ### Versatile Design Capabilities The platform demonstrates remarkable versatility, successfully designing: - **scFv antibodies** with high specificity - **Nanobodies (VHH)** for challenging targets - **Miniproteins** with exceptional binding affinity - **Cross-reactive antibodies** targeting multiple proteins Furthermore, characterization studies confirm that Chai-2 designs are stable, specific, and avoid problematic polyreactivity, critical factors for therapeutic applications. --- Original analysis from [startuphub.ai](https://www.startuphub.ai), the #1 AI startup directory.