# Galen pre-seed partner memo Source: https://preseed.usegalen.com Public Markdown: https://preseed.usegalen.com/llms.txt Structured JSON: https://preseed.usegalen.com/investor-brief.json AI diligence prompt: https://preseed.usegalen.com/ai-diligence-prompt.txt Proof ledger: https://preseed.usegalen.com/proof-ledger.md Diligence questions: https://preseed.usegalen.com/diligence-questions.md Forwardable intro email: https://preseed.usegalen.com/intro-email.txt ## One-sentence thesis Galen is building causal virtual-cell models that predict how living cells respond to intervention, quantify confidence, and choose the next experiment most likely to teach the model. The pre-seed round funds the real human-cell proof loop: predictions committed before results, model-selected experiments, and a first partner-ready intervention-ranking product. ## Round snapshot - A pre-seed round built around prospective biology. The financing advances one investor-underwritable claim: Galen can predict, choose, measure, and learn from real human-cell interventions before results are known. - Pre-seed diligence path: The public workspace gives investors a complete first-pass view before requesting private proof files, round materials, and founder follow-up. - 18-month seed proof package: Capital funds the human-cell learning loop, model-selected experiments, scientific software, and partner-ready intervention-ranking workflow. - AI-for-science platform conviction: Best-fit investors can help with biology leadership, design-partner access, wet-lab judgment, and seed-stage company building. - Terms, budget, and exact proof files: Approved investors can review use-of-proceeds detail, round materials, baselines, controls, validation contracts, and protocol-level notes. ## Why this is interesting now - Virtual cells are becoming inevitable. Large biological datasets, single-cell atlases, perturbation screens, and foundation-model investment have made the category legible to customers and investors. - Intervention prediction is the valuable layer. Discovery teams already have maps of biological state. The scarce decision is what to perturb, what response to expect, and which experiment resolves uncertainty fastest. - The next proof can be scored before results are known. Galen's pre-seed plan converts controlled modeling evidence into a forward-looking human-cell study with predictions, comparison models, and scoring fixed in advance. - Intervention ranking is useful before the platform is broad. A focused model can help teams prioritize expensive follow-up work while each deployment strengthens the same causal virtual-cell machinery. ## Category position - Atlases and foundation models make biology legible. Galen uses that legibility to predict how the state changes under intervention and how much confidence each recommendation deserves. - Wet-lab execution turns predictions into measured learning. Galen chooses the experiments with the highest learning value, records predictions before results, and turns each measurement into model improvement. - Discovery workflow tools organize decisions. Galen returns a ranked intervention plan, expected response, confidence, and the smallest useful validation set. - A focused wedge compounds into programmable biology. Galen starts with causal intervention ranking and expands the same machinery into predict, design, and verify capabilities as evidence accumulates. ## First wedge - Teams with expensive intervention choices. Functional-genomics, immunology, target-discovery, and cell-engineering groups already spend budget deciding which targets, perturbations, donors, states, and readouts deserve reality. - A paid prioritization program with a validation plan. A customer brings a candidate set and a consequential biological decision. Galen returns ranked interventions, expected response, confidence, explicit abstentions, and the smallest useful follow-up experiment set. - Each deployment improves the same virtual-cell asset. The wedge begins as bounded causal triage. As prospective reliability broadens, Galen can expand into recurring workflow access and programmable predict, design, and verify interfaces. ## Investor decision frame - Intervention prediction is the valuable virtual-cell layer. Underwrite Galen if intervention prediction and experiment selection are the valuable layer above biological maps. - Causal structure improves prediction before results are known. Public proof supports a technical call on causal recovery, confidence, and experiment selection for living-cell validation. - Pre-seed capital buys a seed-ready proof package. Capital converts modeling evidence into human-cell proof, model-selected experiments, and a partner-ready workflow. - The right investor can increase the slope of the proof. Best-fit investors bring AI-for-science conviction, biology hiring leverage, design-partner access, and seed storytelling. ## Public evidence - ~90%: Hidden mechanism support recovered in the strongest public summary tests. Galen's current flagship evidence shows the architecture can recover and use cause-and-effect structure when a realistic single-cell-style experiment contains identifiable signal. - 15 / 15: Control settings behaved as expected. The evidence practice matters as much as the benchmark: Galen records when evidence supports a claim, when confidence is earned, and when a control outcome remains a control outcome. - built: The experiment-selection software is already part of the system. In controlled dry-lab tests, Galen selected experiments expected to teach the model more than simple same-budget alternatives. The pre-seed loop tests that claim in living cells. - prospective: The round funds predictions committed before results. The next investor-grade evidence is forward-looking: define the benchmark, lock the baselines, run the experiment, and score the model after biology answers. ## What the pre-seed round funds - Forward-looking cell proof: Run the first real human-cell learning loop with predictions, comparison models, and scoring rules committed before results are known. - Experiment-selection engine: Improve the causal model, confidence estimates, and experiment-selection software around the first validation task. - Partner-ready package: Turn the proof loop into a repeatable intervention-ranking workflow: intake, report, scoring, and next-experiment recommendation. - Reusable scientific system: Build the data provenance, quality standards, and lab-operating practices that make each measured result useful for the next model. ## Seed-readiness target - Months 12-15: Shared intervention data across cell states or donors, partner validation underway, and previously unseen context evaluation if the data supports it. - Months 15-18: Previously unseen result scored after predictions are committed, model-selection result, repeatable partner package, and evidence-based scale plan. ## Best-fit investor - AI-for-science and bio-platform conviction. The best-fit investor already believes virtual-cell companies can be large and wants a precise view of Galen's wedge, proof stack, and next falsifiable milestone. - Capital plus scientific and commercial leverage. The most helpful investors can support senior biology hiring, design-partner introductions, wet-lab execution judgment, and later seed-stage storytelling. - A seed-ready proof package. The pre-seed round is designed to produce prospective prediction evidence, measured learning efficiency, and a repeatable partner package. ## Private packet access path - Request review: We review the request, firm context, and stated diligence need before sharing private proof files or round materials. - Matched packet: Approved investors receive the right package for their intent: proof files, technical diligence, round materials, design-partner detail, or founder follow-up. - Founder diligence: The first conversation focuses on the current proof boundary, the human-cell validation plan, model-selected experiments, and seed-readiness evidence. ## Private packet index - Technical proof packet: request Technical diligence; best for Investors underwriting model quality, comparison plans, controls, and scientific validity. - Round materials packet: request Round materials; best for Leads and serious followers evaluating ownership, budget, hiring sequence, and seed-readiness plan. - Commercial wedge packet: request Design partner; best for Investors helping with customer access, buyer workflow diligence, or first design-partner introductions. - Founder conversation packet: request Founder conversation; best for Investors ready to pressure-test the current proof boundary, next experiment, and founder-market fit. ## Diligence request - Request the private proof packet for exact setup, baselines, confidence intervals, ablations, leakage checks, control outcomes, use-of-proceeds detail, protocol exclusions, and round materials. - First founder call should focus on the current proof boundary, the forward-looking human-cell study, model-selected experiment design, partner workflow, and the evidence required for a seed-stage financing. ## Suggested internal forwarding note Galen is a pre-seed virtual-cell company focused on causal intervention prediction: ranking which biological changes deserve lab budget, predicting expected response, quantifying confidence, and choosing the next experiment. The public proof supports controlled causal recovery and disciplined controls; the pre-seed round funds the prospective human-cell proof loop and first partner-ready intervention-ranking workflow. Worth a founder call if we believe intervention prediction is the highest-value layer of the virtual-cell stack.