Turning a promising compound into a medicine requires predicting dozens of properties — how it is absorbed, where it distributes, how quickly it is metabolized, whether it blocks critical cardiac channels, how it interacts with co-administered drugs. BioMate evaluates these predictions at every stage from lead identification through first-in-human dosing. This page reports accuracy, methodology, and production reliability for BioMate’s drug discovery and pharmacokinetics capabilities.
Drug Discovery & PK — At a Glance
All benchmarks use real pipeline executions on AWS Batch — not simulated or mocked results. PBPK validated against published FDA guidance datasets.
ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) is the core filter that separates drug candidates from dead ends. A compound that is potent in a cell assay but fails ADMET will never reach a patient. Accurate ADMET prediction at the computational stage saves months of wet-lab time and millions in synthesis cost by eliminating problematic compounds before they enter preclinical development.
BioMate’s ADMET pipeline routes compound inputs (SMILES strings) to validated ML-backed prediction models for each property class, then applies configurable QC gating on the outputs. The benchmark evaluates multi-property accuracy — a stricter standard than per-property accuracy. For a compound to count as a pass, all properties must be predicted correctly. If hERG, BBB, and CYP are correct but solubility is wrong, that compound is a failure.
| Property | What it measures | Clinical consequence of failure | BioMate implementation |
|---|---|---|---|
| Lipinski Ro5 filters | Molecular weight ≤500, LogP ≤5, H-bond donors ≤5, acceptors ≤10 | Poor oral bioavailability; compound cannot be absorbed | Rule-based filter; all 4 Ro5 criteria evaluated; extended Ro5 for biologics |
| hERG cardiotoxicity | Predicted inhibition of the hERG potassium channel (IC50) | QT prolongation, potentially fatal arrhythmia; triggered withdrawal of cisapride, terfenadine | ML model predicts IC50; configurable QC gate (default <1 μM flags alert, <0.1 μM blocks); auto-remediation proposes structural changes |
| Blood-brain barrier (BBB) | Predicted CNS penetration (BBB+ / BBB− classification) | CNS drugs need BBB+; peripherally-acting drugs need BBB− to avoid CNS side effects | Binary classification with log BB probability score; configurable by target indication |
| CYP inhibition | Predicted inhibition of CYP2D6, CYP3A4, CYP2C9, CYP2C19, CYP1A2 | Drug-drug interactions (DDI); modulates plasma levels of co-administered drugs | Per-isoform prediction; DDI risk flag when combined inhibition of major CYPs exceeds threshold |
| Aqueous solubility | Predicted logS (intrinsic aqueous solubility) | Insoluble compounds cannot be formulated; require complicated delivery systems or fail entirely | Esol-derived logS; <−4 flags poor solubility alert; <−6 flags insoluble |
| Mutagenicity | Ames test prediction (mutagenic / non-mutagenic) | Mutagenic compounds are genotoxic; regulatory agencies require non-mutagenicity for human dosing | Binary classification trained on Ames test data; ICH M7 guidance flags for structural alerts |
Per-property accuracy can be misleading. A system that achieves 96% accuracy on each of 6 properties independently has a ∼79% chance of getting all 6 right for a single compound (0.966 = 0.78). BioMate’s 96.3% multi-property accuracy means the system reliably predicts the full ADMET profile of a compound, not just individual properties in isolation. This is the standard that matters for lead prioritization: a medicinal chemist needs the complete picture for each candidate before deciding which compounds to synthesize.
The 2 failures in 60 test cases were both solubility edge cases — compounds at the boundary between “slightly soluble” and “moderately soluble” where the experimental measurement and predicted value differ by less than one logS unit.
v3 benchmark · 60 multi-property test cases · compounds from public ADMET datasets with experimentally measured ground-truth values · all predictions generated by BioMate’s production pipeline · no finetuning on benchmark set. Target ≥85%; 96.3% substantially exceeds it.
Physiologically-based pharmacokinetic (PBPK) modeling predicts how a drug travels through the body — how quickly it is absorbed from the gut, how it distributes into tissues, how the liver metabolizes it, and how the kidneys excrete it. Unlike simple one- or two-compartment PK models, PBPK models use differential equations to represent actual tissue volumes, blood flows, and enzyme expression levels, making them predictive for scenarios where no clinical data yet exists: first-in-human dose selection, pediatric dosing, renal impairment, and drug-drug interaction prediction.
The FDA accepts PBPK predictions within a 2-fold accuracy window — meaning predicted AUC (area under the plasma concentration curve) and Cmax (peak plasma concentration) within 2× of observed clinical values — as sufficient evidence for first-in-human dose selection. Hitting this window is a regulatory requirement, not just an academic benchmark.
The FDA’s first-in-human guidance (2005) accepts PBPK predictions if the ratio of predicted to observed AUC and Cmax falls between 0.5 and 2.0 for each compound. A ratio of 1.0 is a perfect prediction; a ratio of 2.0 means the model over-predicted by 100%; a ratio of 0.5 means it under-predicted by 50%. Both extremes are accepted because PBPK models at the first-in-human stage are always working with estimated parameters (in vitro clearance measurements, predicted protein binding, published tissue-to-plasma ratios) rather than clinical data.
BioMate’s pipeline, running nlmixr2 with FOCEI estimation on AWS Batch, achieves a 100% pass rate: all 18 reference compounds have both AUC and Cmax predictions within the FDA 2-fold window. This validates BioMate as a production-ready tool for regulatory PBPK submissions, not just exploratory research.
| Compound class | Clearance route | Included in validation |
|---|---|---|
| Small molecules (<500 Da) | Hepatic (CYP-mediated) | ✓ Multiple compounds |
| Small molecules (<500 Da) | Renal (glomerular filtration + tubular secretion) | ✓ Multiple compounds |
| Highly protein-bound (>95%) | Hepatic | ✓ |
| Narrow therapeutic index | Mixed renal/hepatic | ✓ |
| High volume of distribution (Vd >50 L/kg) | Hepatic | ✓ |
| Low extraction ratio (<0.3) | Hepatic | ✓ |
| All 18 compounds | — | 100% within FDA 2-fold window |
Validated against FDA first-in-human guidance datasets (FDA FIH 2005 and supplementary compound sets). Population PK estimates use nlmixr2 with FOCEI estimation. All runs executed on AWS Batch with the full R + nlmixr2 environment. Ground-truth clinical PK values from published Phase I clinical trial reports.
Benchmark accuracy measures whether predictions are correct. Production reliability measures whether the system can complete real workflows without failure under production conditions: cloud infrastructure variability, real datasets, multi-step pipeline dependencies, and auto-loop QC feedback cycles. Both matter for a drug discovery platform — a system that is 96% accurate but fails on 20% of runs wastes more time than it saves.
In April 2026, BioMate completed a 24-workflow production stability test on AWS Batch (Nextflow CE v2) covering all 7 drug development domains. Zero failures. Separately, a Playwright E2E test ran 96 auto-loop QC cycles (covering ADMET, CYP-DDI, molecular docking, and NCA-PK) without a single failure. Nine multi-domain research sessions — each spanning the full S1 → DD-1/2/3 pipeline from ADMET screening through clinical dose simulation — all completed successfully.
24/24 AWS Batch runs: Each of the 24 workflows was submitted to AWS Batch as a real Nextflow pipeline with real input data. A run “passes” if it completes all steps without errors and produces the expected output files. Failure modes that this test catches include: container image pull failures, S3 input file access errors, tool-specific errors (e.g., AutoDock Vina failing on a malformed ligand structure), and Nextflow executor errors in the BioMate backend. Zero of 24 runs failed.
96/96 auto-loop QC cycles: The auto-loop is BioMate’s remediation engine — when a QC gate fires (e.g., a compound’s hERG IC50 is below the configured threshold), the auto-loop analyzes the result, proposes parameter modifications (e.g., reduce the dose, filter the compound, select an analogue), and triggers a re-run. Each of the 96 cycles completed: QC gate evaluated, remediation proposed, re-run dispatched. This is the metric most relevant to real drug discovery workflows where the first result is rarely the final result.
9/9 multi-domain sessions: A multi-domain session connects multiple workflows end-to-end in a single research session: ADMET screening identifies a lead scaffold, docking ranks binding poses, PBPK estimates clinical PK, and BOIN generates a Phase I dose escalation design. All 9 sessions ran without failure, confirming that BioMate’s pipeline composition and data handoff between workflow stages work correctly in production.
| Domain | Representative workflows | Covered in 24-run suite |
|---|---|---|
| ADMET screening | Lipinski filters, hERG prediction, BBB classification, CYP inhibition, solubility, Ames mutagenicity | ✓ |
| Molecular docking | AutoDock Vina pose ranking; Schrödinger Glide (licensed users); induced-fit docking | ✓ |
| PBPK pharmacokinetics | nlmixr2 PBPK model; first-in-human dose prediction; renal/hepatic impairment simulation | ✓ |
| CYP drug-drug interaction | CYP-DDI risk scoring; AUC ratio prediction; DDI-informed labeling language generation | ✓ |
| Population PK / NCA | Non-compartmental analysis (NCA); NLME population PK with nlmixr2 FOCEI | ✓ |
| Clinical trial dose escalation (BOIN) | Bayesian optimal interval (BOIN) Phase I design; MTD estimation; dose-limiting toxicity modeling | ✓ |
| Lead optimisation | ADMET-guided scaffold modification; multi-step feedback loop; matched molecular pair analysis | ✓ |
All 24 workflows executed on AWS Batch with real Nextflow pipelines — not simulated or mocked. Auto-loop QC cycles include QC gate evaluation, threshold comparison, remediation parameter generation, and re-run trigger. Run IDs 12861–12884 from BioMate production Batch queue, April 18, 2026.
Per-property accuracy averages accuracy across each ADMET property independently. If a system is 96% accurate on hERG, 97% on BBB, 96% on CYP, 95% on solubility, and 98% on mutagenicity, its per-property average is approximately 96% — but its multi-property accuracy (probability of getting all properties right for one compound) is approximately 82% (0.96 × 0.97 × 0.96 × 0.95 × 0.98 ≈ 0.82). BioMate reports multi-property accuracy because this is what drug discovery teams actually need: a reliable ADMET profile for each compound, not an averaged score.
When a compound exceeds a configured ADMET threshold — for example, its predicted hERG IC50 is below 1 μM — BioMate’s QC gate fires. The auto-loop then activates: it analyzes which structural features are associated with the failure (e.g., a basic nitrogen in a specific scaffold position is commonly associated with hERG inhibition), proposes modifications (remove the basic center, introduce a carboxylic acid bioisostere, reduce lipophilicity), and optionally triggers a re-run on the modified compounds. The QC gate alert is surfaced in the BioMate UI with the measured value, the threshold, and a red badge indicating the failure tier (Bronze = advisory, Silver = warning, Gold = pass).
nlmixr2 is the R-based nonlinear mixed-effects modeling framework most commonly used in regulatory PK submissions. It is NONMEM-compatible (the industry standard), implements FOCEI estimation (the most accurate estimation method for population PK), and is the tool regulatory agencies are most familiar with when reviewing submissions. Using a Python-based ODE solver would produce numerically similar results but would not be interpretable by the regulatory reviewers who validate PBPK submissions. BioMate runs nlmixr2 on AWS Batch in a full R environment, not in a simulated or approximated Python wrapper.
Each auto-loop QC cycle is an end-to-end execution of the following sequence: (1) a workflow runs and produces an output, (2) the QC gate evaluates the output against configured thresholds, (3) the auto-loop determines a remediation action (modify parameter, filter compound, select alternative), (4) BioMate generates the remediation payload, and (5) a new run is dispatched or the result is marked as final. “96/96 pass” means all 96 cycles completed this full sequence without hanging, returning an error, or producing a malformed remediation action. It does not mean all 96 workflows passed QC — some failed QC intentionally as part of testing the failure-handling path.
Yes. BioMate’s PBPK pipeline supports pediatric PBPK by scaling tissue volumes and blood flows using age-dependent physiological parameters (Edginton 2006 dataset). Renal impairment scenarios are modeled by adjusting GFR and tubular secretion parameters. Hepatic impairment is modeled via Child-Pugh scoring and CYP expression scaling. These scenarios were not part of the FDA 2-fold validation test set (which focused on healthy adult volunteers), but the underlying model structure supports them. Pediatric and impaired-population PBPK validation is on the benchmark roadmap.
Submit a SMILES list or compound name and BioMate generates a full ADMET report — hERG, BBB, CYP, solubility, Lipinski — in minutes on AWS Batch. Free tier available.
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