What Valinor Discovery does

Valinor Discovery is a San Francisco-based, venture-backed AI biotechnology company building virtual patient foundation models to predict therapeutic response and disease progression in drug development. The platform ingests longitudinal, patient-derived multi-omics data — including whole genome sequencing, cfDNA methylation, transcriptomics, proteomics, MRI and amyloid PET imaging, histopathology, and structured clinical records — and embeds these modalities into a unified patient representation space. It then trains disease-specific models initially focused on neurology (Alzheimer's disease, multiple sclerosis, Parkinson's disease, ALS) and hematology (multiple myeloma, DLBCL, AML), where patient biology is least understood and clinical trial failure rates remain high. The intended use cases are patient stratification, responder identification, reverse translation, and prediction of progression events and standard-of-care response to improve clinical trial success rates.

The company generates proprietary datasets through its own data engine and through a strategic collaboration with Renew Biotechnologies, under which Valinor holds exclusive rights to train foundation models for biopharmaceutical customers on jointly-owned neurological datasets. Its go-to-market is enterprise B2B, targeting biopharmaceutical companies through direct field sales, research collaborations, co-development agreements, and licensing of its virtual patient models; pricing is not publicly disclosed. Valinor raised a $13M seed round in December 2025 led by CRV and Harpoon Ventures with Amino Collective, Pelion Venture Partners, and Mythos Ventures, and is backed by founders from Anthropic, Goodfire, and Mercor. The founding team — CEO Josh Pacini and COO/co-founder Zhanel Nugmanova — operates with 1-10 employees and is recruiting across machine learning, computational biology, and data engineering to scale the platform.

Name

Valinor Discovery

Legal name

Valinor Discovery

Website

https://valinordiscovery.com

Company type

Private

Founded year

2024

Operating status

Operating

Headcount range

11–50 employees

Short description

Valinor Discovery builds AI-powered virtual patient foundation models from longitudinal multi-omics and clinical data to predict therapeutic response and improve clinical trial success rates for biopharmaceutical companies in neurology and hematology.

Ownership category

akta.pro rank

Headquarters

HQ city

San Francisco

HQ country

United States

HQ region

North America

Offices1 record

Markets served

GTM type

B2B

Offering type

Software

Cost components

Personnel, Technology or R&D, Operations, Marketing or Sales

Revenue model

1. Virtual Patient Model Licensing: Valinor holds exclusive rights to train foundation models for biopharmaceutical customers using jointly-owned datasets. The company appears to generate revenue through licensing their ML models and data insights to drug development companies for patient selection, reverse translation, and therapeutic response prediction.
2. Research Collaborations & Partnerships: The company pursues research collaborations with biotech partners, generating revenue through co-development agreements and strategic partnerships. Valinor also collaborates with Renew Biotechnologies for joint dataset ownership and model training.

Go-to-market motion1 record

Distribution channels1 record

Marketing channels4 records

Core offering

Valinor Discovery builds AI-powered virtual patient models trained on longitudinal multi-omics and clinical data to predict therapeutic response, patient selection, and disease progression for biopharmaceutical drug development. The company focuses initially on neurological disorders (Alzheimer's, Multiple Sclerosis, Parkinson's, ALS) and hematological malignancies (Multiple Myeloma, DLBCL, AML), licensing its foundation models and datasets to pharma and biotech customers.

Product overview

Valinor Discovery offers a unified AI-powered platform centered on Virtual Patient Models for clinical drug development. The platform consists of three interconnected products: (1) a Proprietary Data Engine that generates patient-derived multi-omics datasets across disease timepoints; (2) Multi-Omics Integration Platform that unifies diverse data types (genomics, epigenomics, proteomics, imaging, clinical) into coherent patient representations; and (3) disease-specific Virtual Patient Models in Neurology (Alzheimer's, Parkinson's, ALS, Multiple Sclerosis) and Hematology (Multiple Myeloma, DLBCL, AML) that predict patient responses, disease progression, and therapeutic outcomes. The company also formed a strategic partnership with Renew Biotechnologies to generate the largest clinical multi-omics dataset for neurological disease modeling.

Differentiator

Problem solved

Functional benefit

Products and services

• Virtual Patient Models (Neurology) AI-powered virtual patient models trained on longitudinal paired cfDNA methylation, proteomics, imaging, and clinical data across neurological disorders including ALS, Alzheimer's, Multiple Sclerosis, and Parkinson's. The model learns patient-level disease biology, outcome trajectories, and features driving disease progression for neurology drug developers.
• Virtual Patient Models (Hematology) Virtual patient models trained on matched longitudinal datasets from patients with hematological malignancies, integrating clinical history, mutations, methylation, gene expression, and clinical endpoints to predict therapeutic response. Supports Multiple Myeloma, Diffuse Large B-Cell Lymphoma (DLBCL), and Acute Myeloid Leukemia (AML) for hematology and oncology drug developers.
• Proprietary Data Engine A data generation platform that produces proprietary, patient-derived datasets collected across relevant disease timepoints using multi-omics and clinical assays including scRNAseq, Bulk RNAseq, Proteomics, cfDNA Methylation, Whole Genome Sequencing, Longitudinal Health Records, MRI, Amyloid PET Imaging, and Histopathology. Provides the longitudinal data foundation for training Valinor's virtual patient models.
• Multi-Omics Integration Platform A platform that embeds DNA, methylation, transcriptomics, proteomics, imaging, and structured clinical data into unified patient representations, capturing the heterogeneity of patient biology and enabling interpretable virtual patient modeling for biopharmaceutical drug development customers.

Quantifiable outcome

• Recovering prognostic subgroups learned entirely from multimodal data while stratifying risk better than clinical standards and genomic features alone in hematological malignancies
• +3 more outcomes

Segments3 records

Ideal customer profiles3 records

Technology focussed Yes

API detail

Has API

No

API docs

API detail

Core technology

AI maturity

App detail

AI capability7 records

Feature5 records

Partnerships

One partnership is on record.

• Renew BiotechnologiescoreStrategic or Co-development Partner · 24 February 2026Strategic collaboration to generate the largest clinical multi-omics dataset for neurological disorders, combining Valinor's machine learning expertise with Renew's clinical laboratory infrastructure. The initiative will generate genomic and epigenomic data from thousands of patients with Alzheimer's, Parkinson's, ALS, and other neurological conditions using native-read sequencing. Renew provides clinical and sequencing infrastructure while Valinor applies machine learning to identify disease patterns and therapeutic response signatures. Datasets are jointly owned by both parties, with Valinor holding exclusive rights to train foundation models for biopharmaceutical customers.

Scale indicators3 records

Recent moves5 records

Expansion highlights5 records

Direct peers

• Recursion Pharmaceuticals: Recursion is an AI-driven drug discovery company combining high-throughput biology, multi-omics, and machine learning to identify therapeutic targets and predict clinical outcomes. It is directly comparable to Valinor in using AI/multi-omics for pharma R&D, though it operates at greater scale with an integrated wet-lab platform.
• Insitro: Insitro uses machine learning on large-scale biological datasets (including multi-omics and patient-derived data) to predict clinical outcomes and accelerate drug discovery. Directly comparable to Valinor's virtual patient modeling and ML-driven pharma partnerships.
• Exscientia: Exscientia applies AI to drug discovery and patient stratification, including precision medicine and clinical trial optimization work. It directly overlaps with Valinor's mission of using ML to improve clinical success rates and therapeutic response prediction.
• Owkin: Owkin builds AI models on multimodal patient data (genomics, imaging, clinical records) for biopharma R&D and clinical trial optimization, including federated learning approaches. Closely comparable to Valinor's multi-omics, longitudinal patient representation strategy.
• Schrödinger: Schrödinger combines physics-based computational chemistry with machine learning for drug discovery and predictive modeling. It is a direct peer in AI-driven pharma R&D, with broader computational chemistry scope but overlapping predictive-modeling philosophy.
• Iambic Therapeutics (formerly Entos): Iambic uses AI foundation models and physics-based simulation to predict molecular properties and clinical behavior. Directly comparable to Valinor's foundation model approach for drug development and patient response prediction.
• Genesis Therapeutics: Genesis Therapeutics applies AI and foundation models to small molecule drug discovery and therapeutic response prediction. It overlaps with Valinor in using ML to predict clinical outcomes and accelerate pharma partnerships.

Broad incumbents

• Tempus Labs: Tempus is a large precision medicine company that combines multi-omic profiling, clinical data, and AI to support pharma R&D and clinical decision-making. Comparable to Valinor in multi-omics + clinical data integration, though it operates as a broad incumbent across many therapeutic areas and services.
• Ginkgo Bioworks: Ginkgo Bioworks is a broad horizontal platform for cell engineering and synthetic biology, increasingly leveraging AI for predictive modeling in pharma and biotech. Comparable to Valinor as a large, well-capitalized AI-biology platform though operating across a broader synthetic biology scope.

Emerging players

• Cradle Bio: Cradle applies generative AI/ML models to protein engineering and drug discovery. Comparable to Valinor as an emerging AI-biotech applying foundation model approaches to pharma R&D, though focused earlier in the discovery pipeline.

Market position

Strengths5 records

Weaknesses5 records

Competitive moat3 records

Key risks6 records

Key highlights6 records

Customer concentration

• Xx.com/valinordiscover
• LinkedInlinkedin.com/company/valinor-discovery

Revenue estimate

Valuation estimate

Number of profiles

Profiles2 records

Funding overview

Funding rounds2 records

Investors5 records

Funding detail is available on the Subscription and Enterprise plan.Contact sales →

M&A

Investments

M&A and investment is available on the Subscription and Enterprise plan.Contact sales →

Frequently asked questions about Valinor Discovery