BeatSoC Virtual Clinical Trial (BVCT) models Hazard Ratio (HR), Odds Ratio (OR) or Relative Ratio (RR) against standard of care at 0.1 precision (effect sizes reported to one decimal place), at every step from target ID through label expansion. Company-reported prospective accuracy: 95.6% (n=705, cohort 24 Sep 2026). Research use only.
HIPAA-compliant architecture · AES-256 encryption · Proprietary patient records not required · Research use only
Each pipeline gate answers one question with a modeled BeatSoC estimate: HR, OR or RR vs standard of care, at 0.1 precision.
Define desired enhancements: choose a reference drug and your target enhancements (e.g. stronger effect size, longer half-life, better oral bioavailability, higher CNS penetrability, fewer adverse effects). Every submission is timestamped.
Translate differential drug designs into modeled effect-size differences in disease-matched virtual patients. Compare variants against the reference drug at 0.1 precision, with mechanistic rationales.
Estimate HR/OR/RR against a named comparator. Company-reported, timestamp-audited prospective track record: PPV 234/261 (89.7%) and NPV 440/444 (99.1%), cohort 24 Sep 2026. See the evidence page for the cohort and how to check each prediction.
Select disease and target or drug in the 3D knowledge graph. Regulate nodes to encode your mechanism of action.
BVCT auto-fetches eligibility criteria from ClinicalTrials.gov, generates a protocol PDF, and simulates drug-body interactions.
A modeled GO or NO-GO with hazard ratio, odds ratio or relative risk vs standard of care, at 0.1 precision, backed by a company-reported prospective accuracy of 95.6%.
Every BeatSoC Virtual Clinical Trial delivers six properties no other platform combines.
Drug-design-based · Effect-size-quantified · Causal-biology-driven · Independent-of-patient-data · Decision-ready · Exact-SoC-controlled
No patient data required. Predictions start from the drug's design and mechanism of action, applied to the disease, comparator and trial protocol with public scientific knowledge.
HR, ORR, PFS and OS estimates at 0.1 precision: quantitative outputs for decision committees, with their assumptions stated.
Root-cause mechanistic modeling of drug-body interactions. Explainable rationale, not black-box probability.
Proprietary patient records are not required. De novo virtual patients contain no personal data.
Pre-GO/NO-GO verdicts with commercial framing. Effect sizes your committee can act on at every pipeline gate.
Head-to-head comparison vs. any Standard of Care. Not standalone prediction — always relative to the benchmark that matters.
Prospective track record. 705 scored readouts from 692 predictions of then-ongoing trials, each prediction timestamp-audited to predate its readout (PandaDoc e-signature audit trail).
0.1 precision on HR/OR/RR. Effect sizes reported to one decimal place (e.g. an illustrative HR 0.6), rather than qualitative labels.
HIPAA-compliant architecture. AES-256-GCM encryption. Hash-chained audit log. Argon2id hashing. Re-auth on reload.
Your data stays yours. No model fine-tuning on your pipeline data. On-prem available.
BVCT outputs are modelled decision-support estimates for drug R&D / asset decisions, validated against a prospective ex-ante track record (data.bioinvestgpt.com) — Research Use Only; not clinical advice, not for individual-patient diagnosis or treatment, and not a treatment recommendation.