For computational biology and target-discovery teams. Rosenbound's D-MPNN molecular property prediction stack is validated on the standard MoleculeNet scaffold splits (BACE, BBBP, HIV) and is available today as a benchmark-locked capability. Active causal-MoA productization — treating drug-target effects as causal estimands with explicit sensitivity bounds — is deliberately deferred to 2027 while we focus the platform on pharmacovigilance and real-world evidence.
The Rosenbound platform's underlying causal-inference engine works as well on drug-target effects as on adverse-event signals — structurally, the math is the same. But the buyer is different (comp-bio team vs. drug-safety director), the data is different (assay panels and SMILES strings vs. ICSRs), the workflow is different (target validation and lead optimization vs. signal triage), and the regulatory framing is different (no 21 CFR Part 11 in early-stage discovery).
Building all four product surfaces in parallel is what kills founder-led companies. We've chosen to ship pharmacovigilance and real-world evidence to 9.5/10 quality first, get them into Founding Partner pilots, then turn full attention to drug discovery in 2027.
What that means for you today: the D-MPNN benchmark capability is real and citable, and the platform technology is portable. The active product is on the 2027 roadmap.
In-house PyTorch implementation of the Directed Message-Passing Neural Network architecture (Chemprop v2 parity). Validated on the three standard MoleculeNet scaffold-split benchmarks. Ready to extend to your in-house assay data as a Founding-Partner co-authorship.
β-secretase inhibition (Alzheimer's target). Rosenbound AUROC 0.8861 ± 0.001 vs Chemprop v2 reference 0.859 ± 0.024. Within published σ-overlap. Scaffold split, no ensembling.
Blood-brain barrier penetration. AUROC 0.9144 ± 0.0113 vs Chemprop v2 reference 0.897 ± 0.012. Within published σ-overlap.
HIV replication inhibition. AUROC 0.7937 ± 0.0149 vs Chemprop v2 reference 0.776 ± 0.020. Within published σ-overlap.
No Chemprop runtime dependency. 5-test gradient-check suite green. Bemis-Murcko scaffold splits enforced. Published-baseline parity under stricter conditions than the reference paper.
The same causal-inference engine that powers PV and RWE consumes molecular-property scores as treatment-effect inputs. The technical substrate is ready; the discovery-specific UX and the causal-MoA estimator are the 2027 work.
If your team has an in-house assay panel you'd like benchmarked, Founding Partner status includes Rosenbound running our D-MPNN against your data and co-authoring the resulting benchmark publication (under your IP terms).
The drug-discovery productization track activates when (a) the pharmacovigilance vertical has 2+ paid Founding Partners through year-1 renewal, and (b) the RWE vertical has 2+ paid Founding Partners. Estimated activation: Q3 2027.
Planned scope: causal mechanism-of-action estimator integrating the D-MPNN molecular feature space with the platform's neural counterfactual estimator; assay-panel ingestion UX; target-validation workflow with sensitivity-bounded effect estimates; explicit per-target Γ-bound on every MoA claim.
Early-access program will open Q1 2027 to comp-bio teams interested in shaping the discovery-specific UX. Email harsh@rosenbound.com to be on the early-access list.
Watch the product walkthrough at rosenbound.ai — three moments that define the platform: the Cognitive Validation Report refusing incoherent data, the live Γ-bound sensitivity visualization, and the reproducibility certificate generated on every study. The full platform stays gated for Founding Partners.
Watch the preview →
pip install rosenbound
— Official Python SDK for programmatic access: cohort upload, sensitivity-bounded study runs, and reproducibility certificate retrieval. Apache 2.0; Pydantic v2 typed; py.typed for IDE autocomplete + mypy. Platform access gated by Bearer token + RBAC + tenant scoping — the SDK is open, the audit substrate is not.
Comp-bio teams who want input on the causal-MoA UX, the assay ingestion path, or the target-validation workflow are welcome to apply for early-access status. No commitment, no cost — just a structured feedback channel with the founder during the design phase.