ADMET Prediction & Modeling
AI Toxicity Prediction 
Metabolism & Stability Forecasting 
QSAR & PBPK Modeling 
In Vivo ValidationPoor ADMET accounts for over 30% of clinical failures — yet most programs test it too late. We front-load developability certainty with deep learning ADMET panels, metabolism prediction, and PBPK modeling, validated in vitro and in zebrafish — before the first milligram is synthesized.
Creative Biostructure at a Glance
Over a decade of trusted expertise powering biotech, pharma, and research institutions worldwide to advance therapeutic innovation.
Why Partner With Us
Most ADMET surprises are not surprises — they are predictable. hERG liabilities, CYP450 inhibition, and solubility cliffs follow patterns that deep learning models can flag from SMILES alone. Yet virtual biotechs still discover these liabilities in Phase I. Pharma teams still run expensive in vitro panels on compounds that AI would have eliminated. We built this platform to move ADMET from a late-stage gate to an early-stage filter: every designed compound is scored for absorption, distribution, metabolism, excretion, and toxicity before it reaches the lab, and every AI prediction is calibrated against experimental data.
Your CapEx is in chemistry and disease biology. Ours is in predictive toxicology and pharmacokinetic modeling.
| Stage | What We Deliver | What You Don't Need to Build |
|---|---|---|
| Property Prediction | AI-predicted solubility, permeability, LogP, pKa, polar surface area; BBB and P-gp substrate prediction | Cheminformatics infrastructure |
| Toxicity Screening | Deep learning hERG, Ames, CYP450 inhibition, hepatotoxicity, genotoxicity, carcinogenicity flags | Cardiac safety and genotoxicity assay suites |
| Metabolism & PK Modeling | Microsomal stability prediction; P450 metabolite profiling; PBPK simulation for human dose projection; drug-drug interaction risk | DMPK modeling software and trained staff |
| Experimental Validation | In vitro ADME-Tox panel; zebrafish toxicity; NMR pharmacometabonomics | Vivarium, analytical lab, hepatocyte facility |
Production-Ready Deliverables: Every compound exits with a multi-parameter ADMET risk card, predicted PK profile, and experimental validation data — enabling go/no-go decisions before synthesis commitment.
- ✓ Milestone-based pricing aligned with your fundraising cycles
- ✓ No vivarium or analytical lab overhead — prediction, triage, and validation under one project manager
Phase I failures are expensive. We prevent them by front-loading computational certainty.
AI toxicity firewall
Transformer-architecture models flag hERG, CYP450, Ames, and hepatotoxicity risks before synthesis, cutting in vitro workload by 60%+.
PBPK-guided dose projection
Physiologically based pharmacokinetic models predict human exposure, clearance, and bioavailability from preclinical data, supporting IND dose selection and regulatory pre-submission.
Metabolic soft spot identification
P450 metabolite prediction and QSAR models flag metabolic liabilities before they become bioavailability surprises, enabling deuteration or bioisostere strategies pre-synthesis.
Core Service Modules
Service Module At-a-Glance
| Service | Core Capability | Structural + Computational Integration | Typical Timeline |
|---|---|---|---|
| AI-Based Toxicity Prediction | hERG channel inhibition; Ames mutagenicity; carcinogenicity; hepatotoxicity; genotoxicity | Molecular docking into hERG/CYP450 crystal structures; MD for binding stability; zebrafish validation | 1–2 weeks |
| Metabolism & Stability Prediction | P450 substrate/inhibitor prediction; microsomal stability; P-gp substrate; drug-drug interaction risk | MD simulations of P450 active site access channels; docking for metabolite orientation; in vitro hepatocyte validation | 2–3 weeks |
| QSAR Analysis | Quantitative structure-activity relationship modeling; descriptor selection; model validation; applicability domain definition | AI structure prediction and MD descriptors enrich 2D/3D QSAR feature spaces; FEP validates thermodynamic contributions | 3–4 weeks |
AI-Based Toxicity Prediction
Deep Learning Safety Screening Before Synthesis
Key Features:
- hERG Cardiac Risk — Transformer models trained on hERG electrophysiology data predict IC50 from SMILES and 3D pharmacophore; molecular docking into hERG channel structures identifies off-target binding modes.
- Genotoxicity & Carcinogenicity — Ames mutagenicity and in vivo carcinogenicity prediction via ensemble ML; structural alerts for DNA intercalation and reactive metabolite formation.
- Hepatotoxicity Panel — Deep learning models flag mitochondrial toxicity, bile salt export pump inhibition, and reactive metabolite risk from structure alone.
What We Offer: For seed-stage biotechs, toxicity risk cards for every analog before synthesis prioritization. For pharma, regulatory-ready safety narratives with predicted NOEL margins and mechanistic rationale.
Explore Toxicity Prediction →Metabolism & Stability Prediction
P450, Permeability, and PK Forecasting
Key Features:
- P450 Enzyme Modeling — CYP3A4, CYP2D6, CYP1A2 substrate and inhibitor prediction; docking and MD map active site access channels and metabolite orientation.
- Microsomal Stability & Clearance — AI models predict intrinsic clearance from structure; PBPK integration translates to human dose and dosing interval.
- P-gp & BBB Permeability — Transporter substrate prediction guides CNS program go/no-go and peripheral selectivity strategies.
What We Offer: For lead optimization, metabolic soft spot maps that direct medicinal chemistry away from liability-laden scaffolds. For IND packages, PBPK-simulated human PK profiles that support dose selection and regulatory dialogue.
Explore Metabolism Prediction →QSAR Analysis
Quantitative Models for Property Optimization
Key Features:
- Descriptor Engineering — 2D topological, 3D pharmacophore, and MD-derived dynamic descriptors enrich model feature spaces beyond traditional fingerprints.
- Model Validation & Applicability Domain — Rigorous cross-validation, external test set validation, and applicability domain definition ensure predictions are trustworthy for novel chemotypes.
- Multi-Property Optimization — Simultaneous QSAR modeling for potency, selectivity, solubility, and metabolic stability to identify optimal compromise candidates.
What We Offer: For biotechs with limited SAR data, small-dataset QSAR leveraging transfer learning from public databases. For pharma, project-specific models retrained with proprietary data and locked for regulatory reuse.
Explore QSAR Modeling →Technology Platform
Integrated ADMET Infrastructure: AI Prediction + In Vitro Triage + In Vivo Validation
Computational Platform — Dry Lab
Powered by our MagHelix™ CADD Platform and MagHelix™ AIDD Platform
| Capability | Details |
|---|---|
| AI/ML Toxicity Engine | Transformer and GNN architectures for hERG, Ames, hepatotoxicity, and carcinogenicity prediction; proprietary models retrained with each closed-loop project |
| Metabolism Prediction | P450 substrate/inhibitor classification; microsomal clearance regression; reactive metabolite alerts; drug-drug interaction scoring |
| PBPK Simulation | GastroPlus, Simcyp, and in-house PBPK models for human PK prediction, dose projection, and interspecies scaling |
| QSAR Modeling | Random forest, SVM, and deep learning QSAR; 2D/3D descriptor generation; applicability domain and ADMET profiler |
| Structure-Based Toxicity | Molecular docking into hERG, CYP450, and transporter structures; MD for binding stability and induced-fit |
| Cheminformatics Pipeline | RDKit, Schrodinger, Pipeline Pilot; SMILES/SDF processing; batch ADMET scoring at 10⁶ compound/day throughput |
Platform Edge: The integration of AI pre-filtering → in vitro triage → zebrafish confirmation creates a three-tier risk firewall that catches liabilities at 1/100th the cost of late-stage clinical failure.
Experimental Validation Platform — Wet Lab
Powered by our MagHelix™ Zebrafish Screening Platform and analytical suite
| Capability | Details |
|---|---|
| In Vitro ADME-Tox | Automated solubility, permeability, metabolic stability, CYP450 inhibition, and hERG screening |
| NMR Pharmacometabonomics | Bruker 600/800 MHz; non-targeted metabolite monitoring for organ injury biomarker discovery |
| Zebrafish Toxicity | Developmental toxicity, organ-specific safety, and preliminary PK at 1/10th rodent cost |
| Hepatocyte & Microsome | Human and animal liver microsomes for clearance and metabolite identification |
Molecular Devices SpectraMax i3x
Waters Xevo TQ-S micro
Molecular Devices ImageXpress Micro Confocal
Platform specifications are subject to continuous upgrade. Contact our team for instrument availability and project-specific capability assessment.
Closed-Loop Discovery Engine
When Prediction Meets Biological Truth
Traditional ADMET vendors deliver scores and walk away. Our platform feeds every experimental result back into the AI models — so each program improves the next.
AI Pre-Screening
Deep learning models flag solubility, hERG, CYP450, and metabolic liabilities before synthesis, cutting wet-lab workload by 60%+.
→ Feeds into In Vitro
In Vitro Triage
Automated ADME-Tox panels validate AI predictions and catch false negatives; data refines model confidence intervals.
→ Feeds into In Vivo
Zebrafish Validation
Whole-organism toxicity and preliminary PK confirm cell-based predictions; phenotypic outcomes retrain multi-parameter AI models.
→ Feeds into Models
PBPK Calibration
Experimental PK parameters (clearance, Vd, bioavailability) calibrate human PBPK models for the next program's dose projection.
→ Feeds back into AI
Industrial Value:
For Biotechs
Your preclinical data trains our models for your next candidate. Every program makes the platform smarter — a compounding risk-reduction partnership that protects your runway.
For Pharma
Every prediction is paired with an experimental outcome, timestamp, and model version. Fully audit-ready for IND pre-submission meetings, regulatory dialogue, and internal portfolio reviews.
Project Management & Execution
Project Workflow
A standardized, milestone-driven execution system. From compound intake to ADMET-validated data package.
01 Strategy
- Compound library review, ADMET risk matrix definition, and screening scope
- QSAR model selection or custom model training
Deliverable: Project proposal with Gantt-chart, budget, and liability targets
02 Prediction
- AI toxicity scoring: hERG, Ames, CYP450, hepatotoxicity
- Metabolism prediction: P450 profile, microsomal stability, clearance
Deliverable: AI ADMET risk card per compound (Top 50–100)
03 Triage
- In vitro ADME-Tox panel: solubility, permeability, metabolic stability
- NMR pharmacometabonomics for organ injury biomarkers
Deliverable: In vitro validation report with Z'-factor QC
04 Validation
- Zebrafish toxicity and safety profiling
- PBPK model calibration with experimental PK parameters
Deliverable: Zebrafish safety report and PK summary
05 Delivery
- Complete ADMET risk assessment with predicted and experimental data
- PBPK-simulated human PK profile and dose projection
Deliverable: Final technical report + data package + Lead Opt transition plan
Sample Requirements
| Sample Type | Specification |
|---|---|
| Compound Structures | SMILES, SDF, or MOL2 format; 10–10,000 compounds per batch |
| Reference Data | Known ADMET properties for model calibration (if available; not required for standard prediction) |
| Target Profile | Desired PK parameters (e.g., human clearance < 10 mL/min/kg), CNS or peripheral selectivity, dosing route |
Standard Deliverables
Upon project completion, clients receive comprehensive experimental reports including:
- AI ADMET risk cards per compound: solubility, permeability, hERG, CYP450, Ames, hepatotoxicity, metabolic stability
- Predicted human PK profile via PBPK simulation
- In vitro ADME-Tox validation data with QC statistics
- Zebrafish toxicity screening results and imaging archives
- QSAR model report with validation metrics and applicability domain
- Follow-up optimization recommendations and Lead Optimization transition plan
Our technical team responds within 24 hours. All inquiries protected under NDA.
Frequently Asked Questions
Case Study
Published Evidence: Fungal Metabolite Screening with MD Stability and ADMET Gating for SIRT2
Reference: Masum MHU, et al. Bioactive fungal metabolites as SIRT2 antagonists: A computational quest for cancer treatment. PLoS One. 2025 Dec 22;20(12):e0339474.
Research Goal: To prioritize developable SIRT2 inhibitors from natural products by coupling docking scores with MD stability metrics and ADMET safety profiling.
Published Data:
- MD stability gate: Top docked hits MSID001658 (–10.9 kcal/mol) and MSID000672 (–10.2 kcal/mol) showed divergent 100-ns MD profiles — MSID001658 maintained compact, low-RMSD binding (0.81 Å) while MSID000672 exhibited higher flexibility (1.14 Å) and solvent exposure
- ADMET safety gate: All nine advanced metabolites passed SwissADME and ProTox 3.0 filters with high GI absorption, full Lipinski compliance, and zero hepatotoxicity/carcinogenicity/mutagenicity
Industrial Translation:
The authors demonstrated that high docking scores alone do not guarantee binding stability — their top two hits separated only under MD trajectory analysis. Their approach — docking triage plus MD stability and ADMET validation — aligns with our integrated screening workflow: AI toxicity and PBPK predictions front-load developability, while MD trajectories serve as quantitative stability gates before synthesis commitment. For biotechs, this eliminates liability-ridden compounds without wet-lab overhead. For pharma, it provides an auditable geometric-dynamic confidence metric alongside ADMET risk cards for IND readiness.
Figure 1. Post-simulation analysis of the protein-ligand complexes (Masum, et al. 2025)
