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Insitro

insitro is a data-driven drug discovery and development company that leverages machine learning and high-throughput biology to transform the way medicines are created to help patients. At insitro, we are rethinking the entire drug discovery process, from the perspective of machine learning, human genetics, and high-throughput, quantitative biology. Over the past five decades, we have seen the development of new medicines becoming increasingly more difficult and expensive, leaving many patients with significant unmet need. We’re embarking on a new approach to drug development – one that leverages machine learning and unique in vitro strategies for modeling disease state and designing new therapeutic interventions. We aim to eliminate key bottlenecks in traditional drug discovery, so we can help more people sooner and at a much lower cost to the patient and the healthcare industry. We believe that by harnessing the power of technology to interrogate and measure human biology, we can have a major impact on many diseases. We invest heavily in cutting edge bioengineering technologies to enable the construction of large-scale, high-quality data sets that are designed specifically to drive machine learning methods. Our first application is to use human genetics, functional genomics, and machine learning to build a new generation of in vitro human cell-derived disease models whose response to perturbation is designed to be predictive of human clinical outcomes. This cannot be done without great people. We are bringing together an outstanding team of people whose expertise spans multiple disciplines - life sciences, machine learning, human genetics, engineering, and drug discovery - and building a unique culture where people from diverse backgrounds work as a single team towards a common goal. We offer opportunities to collaborative people with expertise in life science and computational science. Join us to help bring better health to more people, faster and cheaper.

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Founded

2018

Location

San Francisco, CA

Employees

267

Funding

$643M+ total

Insitro — AI-Driven Drug Discovery Company Dossier

Insitro is an AI-native therapeutics company founded in 2018 by Daphne Koller and headquartered in South San Francisco. The team integrates high-throughput biology, human genetics, and machine learning to identify and validate drug targets, design small molecules, and improve clinical success rates. Its platform builds large, purpose-built datasets from human cohorts and in vitro cell models, then trains ML models to discover targets, segment patients, and predict pharmacology. Insitro advances an internal pipeline and engages in strategic pharma collaborations rather than offering a public SaaS product. Notable recent activity includes an expanded ALS research effort with Bristol Myers Squibb and a 2025 collaboration with Eli Lilly to co-develop ML models that predict key small‑molecule properties, with access shared across the two companies and select partners.

  • Core outcome: higher-confidence target discovery from human data and phenotypes, faster hit-to-lead via predictive chemistry (ChemML), and smarter clinical development through patient enrichment and biomarker-driven trial design.
  • Operating model: internal therapeutics pipeline plus structured platform partnerships; no public product, pricing, or free trial.
  • Platform and Technology

    Insitro’s platform centers on multimodal data at scale and a unified data architecture that supports end-to-end discovery, from target identification to preclinical optimization.

  • Multimodal human data: integration of genetics, omics, and longitudinal cohort data to prioritize causal targets and patient subtypes. See platform overview on [Platform](https://www.insitro.com/platform/) and [Purpose](https://www.insitro.com/purpose/).
  • In vitro human cell-derived disease models: high-throughput perturbation and phenotyping to generate large labeled datasets for training ML models.
  • Image-based phenotyping: computer vision applied to cell images for sensitive, quantitative readouts of disease-relevant phenotypes.
  • Statistical genetics: ML-enabled fine-mapping and gene-to-target inference to translate human genetics into actionable hypotheses.
  • AI-enabled chemistry (ChemML): predictive models for ADME, potency, selectivity, and in vivo behavior that accelerate hit identification and lead optimization. See collaboration news on [Bristol Myers Squibb extension](https://www.insitro.com/news/insitro-extends-research-collaboration-with-bristol-myers-squibb-leveraging-insitros-chemml-discovery-platform/) and [Lilly partnership](https://www.insitro.com/news/insitro-partners-with-lilly-to-build-first-in-kind-machine-learning-models-to-advance-small-molecule-drug-discovery/).
  • Unified data architecture: standardized data pipelines and storage across modalities to support reproducibility, model reuse, and scalable experimentation.
  • Pipeline and Focus Areas

    Insitro focuses where data scale and ML provide an edge, with public emphasis on neuroscience and metabolic disease. Recent highlights include:

  • Neuroscience: expanded work in ALS with Bristol Myers Squibb .
  • Predictive chemistry: joint effort with Eli Lilly to build and share ML models for small-molecule property prediction .
  • Explore more on [Pipeline](https://www.insitro.com/pipeline/) and [News & Media](https://www.insitro.com/news-media/).

    Business Model and Partnerships

  • Model: therapeutics company with an internal pipeline and select pharma collaborations (not a public software vendor).
  • Access: platform capabilities and models are made available through research collaborations; there is no public product or self-serve access.
  • Recent partnerships: [BMS collaboration extension](https://www.insitro.com/news/insitro-extends-research-collaboration-with-bristol-myers-squibb-leveraging-insitros-chemml-discovery-platform/); [Eli Lilly collaboration (2025)](https://www.insitro.com/news/insitro-partners-with-lilly-to-build-first-in-kind-machine-learning-models-to-advance-small-molecule-drug-discovery/).
  • Outcomes and Differentiation

  • Human-first discovery: grounding targets and biomarkers in human data to increase translational validity.
  • Faster chemistry cycles: ChemML models improve prioritization for potency, ADME, and developability.
  • Patient enrichment: ML-driven patient segmentation enables smaller, better-powered trials.
  • Repeatable processes: emphasis on standardized datasets, unified infrastructure, and measurable improvements across the discovery funnel.
  • Who It’s For

  • Large pharma and mid-cap biopharma seeking ML-enhanced discovery via structured partnerships.
  • Investors and R&D leaders interested in data-rich target discovery and predictive chemistry.
  • Scientists and engineers who want to work at the intersection of ML, genetics, and cell biology. See leadership profile of [Daphne Koller](https://www.insitro.com/leadership/daphne-koller/).
  • Primary Use Cases

  • Target discovery from human genetics and multimodal cohort data.
  • Patient segmentation and biomarker discovery.
  • High-throughput in vitro phenotyping and perturbation studies.
  • Predictive chemistry for ADME, potency, and in vivo behavior.
  • Hit identification and lead optimization prioritization.
  • Trial design support and patient enrichment.
  • Integrations and Data Flows

  • High-throughput assay platforms and imaging for cell-based models.
  • Omics integration from human cohorts and engineered cell models.
  • Unified data pipelines and storage across modalities.
  • ML workflows spanning statistical genetics, image-based phenotyping, and chemistry property prediction.
  • Collaboration interfaces that connect with pharma partners’ R&D stacks.
  • Note: specific vendors/clouds are not disclosed publicly. See [Platform](https://www.insitro.com/platform/).
  • Quick Facts

  • Founded: 2018
  • Headquarters: South San Francisco, CA (259 E Grand Ave)
  • Founder/CEO: [Daphne Koller](https://www.insitro.com/leadership/daphne-koller/)
  • Employees: ~230–270 (2025), following a ~22% reduction to extend runway into 2027
  • Model: internal pipeline + pharma partnerships
  • Focus areas: neuroscience, metabolic disease; ongoing ALS work with BMS
  • Platform pillars: high-throughput biology, human genetics, ML; in vitro disease models; AI-enabled chemistry; unified data architecture
  • Funding/valuation: private; widely reported to have raised hundreds of millions with multi‑billion private valuation in media (see [Forbes](https://www.forbes.com/companies/insitro/) and [Crunchbase](https://www.crunchbase.com/organization/insitro))
  • No public product, pricing, or free trial. See [Website](https://www.insitro.com/).
  • Sentiment Snapshot (External Commentary)

    Pros

  • Strong science/engineering mix and access to advanced tools in ML and cell biology (reflected across [Platform](https://www.insitro.com/platform/) and leadership materials; employee discussions on [Reddit](https://www.reddit.com/r/biotech/comments/1actsws/what_is_working_at_insitro_like/)).
  • Mission-driven work at the ML–biology interface .
  • Blue-chip partnerships (e.g., [BMS](https://www.insitro.com/news/insitro-extends-research-collaboration-with-bristol-myers-squibb-leveraging-insitros-chemml-discovery-platform/), [Lilly](https://www.insitro.com/news/insitro-partners-with-lilly-to-build-first-in-kind-machine-learning-models-to-advance-small-molecule-drug-discovery/)).
  • Cons

  • Cultural/management critiques and expectations “run by a tech vet,” with perceived overpromising risk .
  • 2025 layoffs impacted morale and created uncertainty .
  • Mixed employee reviews and lower recommend rates than some peers .
  • Limited external visibility into detailed outcomes due to partnership model (anecdotally discussed on [Reddit/Blind](https://www.teamblind.com/post/insitro-interview-fodchvod)).
  • Access and “AI Agent” Notes

  • Insitro does not offer a public “AI agent.” Its AI/ML capabilities are embedded in the discovery platform and accessed via internal programs and research collaborations. The Lilly models are expected to be accessible to Insitro, Lilly, and select partners, not the general public. See [Lilly partnership](https://www.insitro.com/news/insitro-partners-with-lilly-to-build-first-in-kind-machine-learning-models-to-advance-small-molecule-drug-discovery/).
  • Learn More

  • Company: [Home](https://www.insitro.com/) | [Platform](https://www.insitro.com/platform/) | [Purpose](https://www.insitro.com/purpose/) | [Pipeline](https://www.insitro.com/pipeline/) | [News & Media](https://www.insitro.com/news-media/)
  • Leadership: [Daphne Koller](https://www.insitro.com/leadership/daphne-koller/)
  • Partnerships and coverage: [BMS collaboration extension](https://www.insitro.com/news/insitro-extends-research-collaboration-with-bristol-myers-squibb-leveraging-insitros-chemml-discovery-platform/) | [Lilly collaboration (press)](https://www.insitro.com/news/insitro-partners-with-lilly-to-build-first-in-kind-machine-learning-models-to-advance-small-molecule-drug-discovery/) | [SynBioBeta coverage](https://www.synbiobeta.com/read/insitro-partners-with-lilly-to-innovate-small-molecule-drug-discovery-using-machine-learning) | [AP News feature on AI in pharma](https://apnews.com/article/ai-pharma-drug-development-eli-lilly-chatbots-004c0ce0442b72c37bfec6e032796808)
  • Company context: [Forbes](https://www.forbes.com/companies/insitro/) | [Crunchbase](https://www.crunchbase.com/organization/insitro)
  • Workforce updates: [BioSpace](https://www.biospace.com/job-trends/insitro-cuts-22-of-workforce-extending-runway-into-2027) | [FierceBiotech layoff tracker](https://www.fiercebiotech.com/biotech/fierce-biotech-layoff-tracker-2025)
  • Community sentiment: [Reddit discussion 1](https://www.reddit.com/r/biotech/comments/1actsws/what_is_working_at_insitro_like/) | [Reddit discussion 2](https://www.reddit.com/r/biotech/comments/1gu6vqp/working_for_insitro/) | [Glassdoor](https://www.glassdoor.com/Reviews/insitro-Reviews-E2542855.htm) | [Blind anecdote](https://www.teamblind.com/post/insitro-interview-fodchvod)
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