Metabolism & Stability Prediction
AI models trained on human liver microsome and hepatocyte data predict clearance pathways, drug-drug interaction potential, and efflux liability to guide rational medicinal chemistry.
Why Metabolism & Stability Prediction Is the Critical Foundation
Poor metabolic stability and unexpected CYP inhibition kill more programs than target potency. Seed-stage biotechs lack the hepatocyte assay infrastructure to profile every analog. Pharma teams need early DDI and clearance predictions to prioritize candidates with QD dosing potential. We deliver P450 site-of-metabolism, microsomal half-life, and P-gp substrate predictions — enabling structure-guided stabilization before in vitro ADMET investment.
What Sets the Platform Apart
P450 Isoform Specificity
Models predict CYP1A2, 2C9, 2C19, 2D6, and 3A4 substrate/inhibitor status simultaneously, enabling polypharmacology-aware lead optimization.
Reactive Metabolite Trapping
AI-identified soft spots (benzylic, allylic, aromatic hydroxylation sites) trigger reactive intermediate alerts and toxicity pre-screening.
DDI Risk Quantification
Predicted Ki/Km values for major CYPs feed into drug interaction risk matrices aligned with FDA guidance.
Technology Suite
P450 Enzyme Substrate/Inhibitor Prediction
Key Features:
- Site-of-Metabolism (SOM) Prediction — 3D docking into CYP1A2, 2C9, 2C19, 2D6, and 3A4 crystal structures identifies likely oxidation sites with regioselectivity scoring.
- Inhibition Potential — ML models trained on 50,000+ CYP inhibition assays predict IC50 and mechanism-based inactivation (MBI) risk.
- Polypharmacology Mapping — Simultaneous prediction of substrate liability across all major CYPs to flag compounds with complex metabolic profiles.
- DDI Risk Matrix — Calculation of AUC ratio changes for co-medications based on predicted CYP inhibition.
Ideal For: Programs requiring once-daily dosing; combination therapies with known CYP substrates; geriatric populations with polypharmacy.
What We Offer:
A per-isoform metabolic profile with SOM maps, inhibition risk flags, and DDI predictions. Seed-stage biotechs avoid costly hepatocyte screening for obvious metabolic disasters. Pharma teams receive DDI risk matrices for IB and IND documentation.
Microsomal Stability Prediction
Key Features:
- Half-Life Regression — Models trained on human, mouse, rat, and dog liver microsome data predict t1/2 and intrinsic clearance (CLint) with R² > 0.75.
- Species Translation — Allometric scaling and interspecies correlation models translate rodent microsome data to predicted human clearance.
- Stabilization Guidance — AI recommends bioisosteric replacements and blocking groups for metabolic soft spots while preserving target affinity.
- Phase II Conjugation — Prediction of glucuronidation, sulfation, and glutathione conjugation sites via UGT and SULT isoform models.
Ideal For: Lead optimization campaigns optimizing PK half-life; prodrug design requiring predictable cleavage; formulation strategies for low-bioavailability compounds.
What We Offer:
A clearance prediction report with species-specific t1/2, CLint, and stabilization suggestions. For DMPK teams, we deliver IVIVE parameters and predicted human dose projections.
P-gp Substrate Prediction
Key Features:
- Efflux Liability Classification — ML models trained on MDCK-MDR1 and Caco-2 bidirectional data classify P-gp substrate likelihood with >80% accuracy.
- Blood-Brain Barrier Penetration — Integration of P-gp efflux with passive permeability to predict CNS penetration and BBB liability.
- Structure-Guided Evasion — AI recommends polarity and size modifications that reduce P-gp recognition while maintaining oral bioavailability.
- Tissue Distribution Modeling — Combined P-gp and BCRP predictions to estimate tumor penetration, placental transfer, and hepatic exposure.
Ideal For: CNS programs requiring brain penetration; oncology programs where tumor efflux drives resistance; oral programs optimizing absorption and bioavailability.
What We Offer:
An efflux profile report with P-gp substrate probability, BBB prediction, and evasion strategies. For formulation teams, we recommend co-administration or prodrug strategies to overcome efflux barriers.
Platform Instrumentation
Core Instruments
| Instrument | Capability |
|---|---|
| Agilent 1290 Infinity II / 6545 Q-TOF | Metabolite identification, soft-spot mapping, and reactive intermediate trapping |
| Thermo Scientific EVOS M7000 | Live-cell imaging for Caco-2 and MDCK-MDR1 monolayer integrity |
| Molecular Devices FLIPR Penta | Real-time calcium flux for CYP induction and hepatotoxicity signaling |
| BioTek Cytation 7 | Automated imaging and multimode detection for microsomal stability assays |
| Beckman Coulter Biomek i7 | Automated hepatocyte and microsome assay preparation |
| Labcyte Echo 650 | Acoustic dispensing for compound serial dilution in ADMET panels |
| Agilent Seahorse XF Pro | Mitochondrial stress test for metabolic toxicity and hepatocyte health |
| Waters ACQUITY UPLC H-Class | Purity and stability profiling of metabolite standards |
Standardized Workflow
Project Workflow
A milestone-driven execution system from structure to metabolic risk profile.
01 Target Review
- Compound structures and target PK goals
- Known metabolic liabilities and DDI context
- Deliverable: Metabolism assessment plan
02 AI Prediction
- P450 substrate/inhibitor batch prediction
- Microsomal stability and P-gp classification
- Deliverable: AI metabolic profile
03 Metabolic Soft-Spot Mapping
- Reactive intermediate and SOM analysis
- Bioisosteric stabilization suggestions
- Deliverable: Soft-spot memo + redesign suggestions
04 Risk Ranking
- Composite metabolic risk matrix
- DDI risk and BBB penetration ranking
- Deliverable: Prioritized PK risk report
05 In Vitro Validation
- HLM half-life confirmation
- CYP inhibition IC50 assay
- Deliverable: Validated clearance data + final report
Sample Requirements
- Compound structures (SDF/SMILES) for 1–500 analogs
- Target PK profile (desired t1/2, Cmax, dosing route)
- Co-medication list for DDI assessment
- Species requirements for allometric scaling
Standard Deliverables
- P450 isoform substrate/inhibitor predictions
- Site-of-metabolism maps with regioselectivity
- Microsomal half-life and CLint predictions (human, rodent, dog)
- P-gp substrate probability and BBB penetration estimate
- DDI risk matrix and AUC ratio predictions
- Stabilization and evasion redesign suggestions
- Final metabolism risk report with DMPK formatting
Frequently Asked Questions
Case Study
Case Study: GNN-Driven Liver Microsomal Stability Prediction with Cross-Species Learning
Goal: Benchmark a graph-based regression pipeline that predicts human and mouse liver microsomal stability from molecular graphs, using contrastive pretraining and interspecies differences to improve metabolic liability screening.
Key Data:
- GCL-pretrained GNN outperforming language models: MetaboGNN achieved RMSE 27.91 (HLM) and 27.86 (MLM) on a 3,981-compound benchmark, surpassing ChemBERTa, MolFormer, and MolT5. Graph contrastive learning on 2.58M unlabeled molecules improved generalization over training from scratch.
- Interspecies multi-task learning: Explicitly modeling HLM–MLM differences as an auxiliary task reduced RMSE by ~7% for both species, particularly for compounds with large metabolic discrepancies, capturing enzymatic variation beyond simple physicochemical trends.
- Attention-driven fragment interpretation: EdgeSHAPer analysis identified destabilizing substructures (aromatic ethers, benzylic carbons prone to CYP oxidation) and stabilizing motifs (fluorinated rings, amides), aligning with known metabolic mechanisms.
Why it matters: This independent 2025 study demonstrates that graph-based representations with species-aware multi-task learning can capture metabolic soft spots and cross-species drift from limited labeled data. By pinpointing bond-level contributions to instability, the model offers an interpretable, structure-based approach to metabolic stability prediction—directly supporting our Metabolism & Stability Prediction services for early PK liability triage and lead optimization.
Figure 1. Bond-centric attention analysis highlighting destabilizing (red) and stabilizing (blue) molecular fragments for liver microsomal stability, with top substructural motifs ranked by metabolic contribution scores. (Park JH, et al. 2025)
Reference
Park JH, et al. MetaboGNN: predicting liver metabolic stability with graph neural networks and cross-species data. J Cheminform. 2025 Sep 3;17(1):140.
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