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Tahoe Therapeutics

A biotechnology and AI-enabled drug discovery company based in the San Francisco Bay Area, California. The company operates a U.S.-centered R&D model built around high-throughput in vivo data generation and computational modeling.

Founding and History

Tahoe launched in 2022 under the name Vevo Therapeutics and later rebranded as Tahoe. The company was founded to generate large-scale, in vivo single-cell perturbation datasets and use them to train AI models that improve target discovery and drug design decisions. In 2025, Tahoe announced a $30 million financing to expand data generation capacity and scale its single-cell dataset production.

Therapy Areas and Focus

Tahoe is platform-led, with an oncology emphasis. Its stated focus is identifying and validating targets and drug concepts that are difficult to progress using conventional in vitro screening or limited-resolution in vivo studies. The company positions its work around tumor biology, drug response heterogeneity, and biomarker-informed development.

Technology Platforms and Modalities

Tahoe’s core technology centers on Mosaic, an in vivo data-generation platform designed to produce perturbation datasets at single-cell resolution and at scale. The company combines these datasets with AI models intended to support “virtual cell” or predictive cellular response modeling. Tahoe’s development outputs include target hypotheses, biomarker strategies, and therapeutic programs, with an emphasis on antibody-enabled approaches when targets are cell-surface accessible and on other modalities depending on biology and partner capabilities.

Key Personnel

Nima Alidoust, Co-Founder and Chief Executive Officer
Hani Goodarzi, Co-Founder
Johnny Yu, Co-Founder
Kevan Shokat, Co-Founder

Strategic Partnerships

Tahoe’s partnering strategy includes translating platform outputs into development vehicles. In January 2026, Tahoe and Alloy Therapeutics announced a jointly seeded joint venture to develop first-in-class antibody-drug conjugates for hard-to-treat cancers, initially advancing two ADC programs built on tumor targets identified and validated using Tahoe’s platform.

FAQ Section

Tahoe combines scalable in vivo perturbation experiments with single-cell readouts and AI modeling. The goal is to capture drug-response biology in a context that reflects tissue environment and cellular diversity, then use those data to improve target selection and therapeutic design.

Tahoe’s disclosed emphasis is oncology. Rather than building a broad internal indication portfolio, it focuses on generating data and models that can be applied across tumor types where target biology and response heterogeneity are limiting factors.

Tahoe’s primary outputs are platform-derived discovery programs and partnered development efforts rather than a conventional list of clinical-stage assets. The most visible programmatic effort to date is the oncology ADC joint venture with Alloy Therapeutics, which is intended to advance two target-led ADC programs into preclinical development.

In January 2026, Tahoe and Alloy announced formation of a joint venture to develop first-in-class ADCs in oncology. In 2025, Tahoe announced a $30 million financing to expand production of large-scale single-cell datasets and improve the training base for its AI models.

Tahoe’s validation narrative is centered on scale and resolution of its in vivo single-cell datasets, the use of those data to train predictive models, and translation of platform outputs into drug programs. The Alloy joint venture is positioned as a downstream test of the platform’s ability to produce actionable tumor targets suitable for therapeutic development.

Milestones are expected to be platform and partnership driven: expanding dataset scale and model capability, publishing or releasing additional reference datasets, and progressing partnered programs (such as the ADC joint venture) through target-to-candidate work and IND-enabling preparation.

Tahoe is led by a founding team spanning AI, single-cell biology, and drug discovery. Operationally, it is set up to generate large perturbation datasets, train models on those data, and translate outputs through partnerships or dedicated development vehicles when a target and modality path are defined.

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