Hit to Lead
Structure-Guided SAR 
AI + FEP Prediction 
Orthogonal Biophysics 
40% Faster Cycles Most Hit-to-Lead programs stall not because chemists are slow, but because the optimization direction is wrong. We replace blind synthesis cycles with a computation-first closed loop: every R-group modification is FEP-scored, every derivative is structurally validated, and every dead-end is killed before it reaches the lab. For seed-stage biotechs, we operate as your virtual medicinal chemistry team — delivering lead candidates without the synthesis infrastructure. For pharma, we compress 6-month iteration loops into 3-week cycles with audit-ready structural data at each decision gate.
Creative Biostructure at a Glance
Why Partner With Us
Most drug discovery programs stall in Hit-to-Lead not because the science is wrong — but because the infrastructure is fragmented. Virtual biotechs burn runway on synthesis cycles that should have been killed computationally. Pharma teams lose months coordinating a computational CRO, a crystallography facility, and a medicinal chemistry shop. We built Creative Biostructure to eliminate that friction: one platform where generative AI design, physics-based free energy calculations, and high-resolution structural validation share the same project team, the same data system, and the same milestone clock.
Your CapEx is in compute and chemistry partnerships. Ours is in structural biology and biophysical validation.
| Stage | What We Deliver | What You Don't Need to Build |
|---|---|---|
| Hit Validation | Multi-technique orthogonal biophysics (SPR, ITC, NMR, TSA, BLI, DSC, MST, DLS, MS, QCM-D) | Biophysics instrumentation suite |
| Structural Elucidation | Co-crystal structures / Cryo-EM / NMR of protein-hit complexes | Synchrotron access, crystallography team |
| Computational Design | Generative AI derivative generation, FEP affinity prediction, ADMET risk flagging | GPU cluster, CADD software licenses |
| Data Handoff | Lead compound report with SAR rationale, structural coordinates, and Lead Optimization transition plan | — |
Production-Ready Deliverables: Every lead candidate ships with computational docking scores, FEP-derived binding affinity predictions, high-resolution structural data, and preliminary ADMET risk assessment — ready for your internal team or our Lead Optimization group to advance.
- ✓ Milestone-based pricing aligned with your fundraising cycles
- ✓ No vendor coordination overhead — computational design, synthesis coordination, and structural validation under a single project manager
GPCRs. Kinases. PPIs. Nucleic acid complexes. "Undruggable" is where our platform excels.
IP firewall & encrypted data infrastructure
Full audit trails, GLP-ready documentation, client retains 100% ownership of all data, structural coordinates, and derivative designs.
Core Service Modules
Service Module At-a-Glance
| Service | Core Capability | Structural + Computational Integration | Typical Timeline |
|---|---|---|---|
| Hit Biophysical Characterization | SPR, ITC, NMR, TSA, BLI, DSC, MST, DLS, MS, QCM-D orthogonal validation | Structure-based PAINS filtering; binding mode prediction guiding experimental design | 2–4 weeks |
| Fragment-to-Lead | Fragment growing, linking, merging; focused library synthesis | SBDD/LBDD simulation; FEP affinity prediction; AI generative derivative design | 6–10 weeks |
| Molecular Evolution & Library Design | R-group screening, scaffold modification, structure-based optimization | Generative AI for drug-like derivative design; ADMET pre-filtering; synthetic accessibility scoring | 4–8 weeks |
| Selectivity Profiling | Counter-screening (Safety Panel), off-target assessment, homolog protein testing | Structure-based homolog selectivity prediction; molecular docking for differential binding; selectivity fingerprint modeling | 3–6 weeks |
| Lead Compound Characterization | High-resolution crystallography / Cryo-EM; thermodynamic stability; kinetic measurement | MD simulation for binding stability validation; electron density and computational model cross-validation; binding free energy decomposition | 4–8 weeks |
Hit Biophysical Characterization
Orthogonal Validation Before You Invest in Synthesis
Key Features of Our Biophysical Characterization:
- Multi-Technique Orthogonal Platform — SPR (kinetics/affinity), ITC (thermodynamics), NMR (fragment/STD), TSA/DSC (stability), BLI, MST, DLS, MS, QCM-D — all under one roof.
- Computational PAINS Filtering — Structure-based algorithms and molecular fingerprint similarity analysis to exclude false positives before they consume synthesis budget.
- Binding Mode Prediction — Molecular docking and MD simulations guide experimental design, prioritizing which hits merit biophysical investment.
What We Offer:
For virtual biotechs, this means knowing which hits are real before you spend a dollar on chemistry. For pharma teams, it means audit-ready binding kinetic datasets (kon, koff, KD, ΔH, ΔS) that support regulatory filings from day one.
Explore Hit Biophysical Characterization →Fragment-to-Lead
From Weak Fragments to High-Affinity Leads
Key Features of Our Fragment-to-Lead Services:
- Fragment Growing & Linking — Rational expansion of weak-binding fragments (mM–μM) into lead-like compounds through structure-based design.
- AI Generative Derivative Design — Generative AI models explore chemical space while preserving core pharmacophores and optimizing synthetic accessibility.
- FEP-Guided Affinity Prediction — Free Energy Perturbation (FEP) calculations predict binding affinity changes before synthesis, eliminating dead-end modifications.
What We Offer:
Traditional fragment-to-lead relies on intuition and iterative synthesis. Our closed-loop approach uses computational hotspot analysis to identify optimal growing vectors, then validates each design with FEP and co-crystal structures before a single compound is made.
Explore Fragment-to-Lead →Molecular Evolution & Library Design
AI-Driven Compound Optimization
Key Features of Our Molecular Evolution Services:
- Deep Learning ADMET Prediction — Transformer-architecture models preemptively flag toxicity risks, solubility liabilities, and metabolic soft spots at the design stage.
- Synthetic Accessibility Scoring — Every AI-generated derivative is scored for route feasibility, ensuring designs are "makeable" by your CRO or internal team.
- R-Group & Scaffold Exploration — Structure-based R-group screening and scaffold modification strategies guided by binding pocket analysis.
What We Offer:
For biotechs without internal medicinal chemistry, we operate as your computational design team — delivering focused libraries of 20–30 high-potential derivatives with predicted potency, selectivity, and ADMET profiles. For pharma, we supplement your internal team with AI-driven exploration of chemical space that manual design might miss.
Request Project Scoping →Selectivity Profiling
Off-Target Risk Assessment Before Lead Commitment
Key Features of Our Selectivity Services:
- Safety Panel Counter-Screening — Orthogonal assessment against homolog proteins, hERG, CYP450 panel, and known anti-targets.
- Structure-Based Selectivity Prediction — Homology modeling and docking for differential binding mode screening across target families.
- Selectivity Fingerprint Modeling — ML-based prediction of off-target liability using structural and physicochemical descriptors.
What We Offer:
Selectivity failures kill programs in Phase I. Our front-loaded computational profiling identifies off-target risks before synthesis, directing optimization toward clean scaffolds and away from liability-laden chemical matter.
Request Project Scoping →Lead Compound Characterization
Structural and Thermodynamic Certainty
Key Features of Our Lead Characterization:
- High-Resolution Structure Determination — X-ray crystallography and Cryo-EM of protein-lead complexes at atomic resolution.
- Thermodynamic & Kinetic Profiling — ITC (ΔG, ΔH, ΔS), SPR (kon/koff), and DSC for complete binding characterization.
- MD Validation — Microsecond-scale MD validates binding stability under dynamic conditions and identifies potential off-target risks computationally.
What We Offer:
Every lead candidate exits our platform with a structural passport: co-crystal coordinates, thermodynamic binding data, and computational stability assessment — the exact package your Lead Optimization team needs to advance with confidence.
Technology Platform
Integrated Discovery Infrastructure: Computation + Structure + Synthesis Coordination
Our platform spans generative AI design, physics-based free energy calculations, and high-resolution structural validation — enabling Hit-to-Lead evolution without the delays of coordinating a computational CRO, a crystallography facility, and a medicinal chemistry shop.
Computational Platform — Dry Lab
Powered by our MagHelix™ CADD Platform and MagHelix™ AIDD Platform
| Capability | Details |
|---|---|
| AI/ML Generative Engine | VAE/GAN-based molecular derivative generation; Transformer ADMET prediction; proprietary ML scoring pipelines retrained with each closed-loop project |
| Free Energy Perturbation (FEP) | GROMACS/AMBER GPU-accelerated FEP and Non-Equilibrium Switching (NES) for relative binding affinity prediction |
| Molecular Dynamics | Microsecond all-atom simulations; membrane protein-lipid systems; enhanced sampling (metadynamics, REST2) |
| Structure-Based Docking | Glide, AutoDock-GPU; induced-fit and covalent docking enabled |
| ADMET Prediction | Deep learning panel: Fsp3, LogP, hERG, CYP450, Papp, BBB permeability |
| Virtual Library Access | >10 billion compounds + AI-customized focused libraries |
NVIDIA DGX A100
Dell PowerEdge XE8545
NVIDIA RTX A6000
Biophysical & Structural Validation — Wet Lab
Powered by our MagHelix™ Structural Biology and SBDD Platform
| Capability | Details |
|---|---|
| X-ray Crystallography | Co-crystal soaking, automated screening robots, high-throughput ligand soaking |
| Cryo-EM | Thermo Fisher Glacios / Krios G4; single-particle analysis for large complexes and membrane proteins |
| NMR | Bruker 600MHz+; STD-NMR for fragment validation |
| SPR/ITC/BLI | Biacore 8K+, MicroCal PEAQ-ITC, Octet RH16; full kinetic and thermodynamic profiling |
| Thermal Stability | Prometheus NT.48 / DSC |
Platform Edge: For difficult targets, the ability to generate co-crystal structures within days of compound synthesis — then feed those coordinates back into FEP calculations for the next design cycle — compresses traditional 6-month loops into 3-week iterations.
Biacore 8K+
Thermo Fisher Krios G4
Octet RH16
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 Validation
Traditional CROs separate computational design from experimental validation — creating handoff delays and information loss. Our platform operates as a closed-loop system: every biophysical measurement and structural coordinate feeds back into our AI and FEP models in real time.
FEP-Guided Synthesis Prioritization
Predicted ΔΔG values from Free Energy Perturbation prioritize high-confidence modifications before synthesis, reducing dead-end compound cycles by over 40%.
FEP → Synthesis
Structure-Validated Docking Updates
Experimentally validated binding modes from co-crystal and Cryo-EM structures feed back into docking protocols, updating induced-fit parameters and eliminating incorrect design hypotheses.
Co-crystal → Docking
Biophysics-Calibrated FEP Models
Thermodynamic and kinetic data from SPR/ITC calibrate target-class-specific FEP parameters, improving affinity prediction accuracy for subsequent design cycles.
ITC/SPR → FEP
ADMET-Aware Generative AI
Cellular and in vivo toxicity signals train multi-parameter optimization functions in our generative models, enabling early elimination of liability-laden scaffolds before they reach synthesis.
ADMET → Generative AI
Industrial Value:
For biotechs
Your first campaign calibrates the models for your second. Structural data from your Phase 0 target becomes training data for your Phase 1 target — a compounding learning partnership.
For pharma
Every computational prediction is linked to an experimental outcome with project ID, timestamp, and model version — fully audit-ready for regulatory submissions.
Project Management & Execution
Project Workflow
A standardized, milestone-driven execution system. From hit quality assessment to lead candidate delivery.
01 Hit Validation
- Multi-technique orthogonal biophysics (SPR, ITC, NMR) to exclude PAINS and establish reliable starting points
- Computational PAINS filtering and binding mode prediction
Deliverable: Validated hit ranking with biophysical data package
02 Molecular Evolution
- SBDD/LBDD-based computational simulation with AI-assisted focused library design
- FEP affinity prediction for prioritized derivatives
- ADMET risk assessment and synthetic accessibility scoring
Deliverable: 20–30 high-potential derivative designs with predicted profiles
03 Synthesis Testing
- Focused library synthesis (internal or coordinated CRO partner)
- IC50/EC50 determination and selectivity counter-screening
Deliverable: Experimental potency and selectivity dataset
04 Lead Characterization
- High-resolution co-crystal or Cryo-EM structure determination
- Thermodynamic and kinetic binding characterization
- MD simulation validation of binding stability
Deliverable: Structural passport + complete biophysical profile for lead candidates
05 Integrated Deliverables
- Comprehensive lead compound report with SAR rationale
- Computational models, FEP analysis, and structural coordinates
- ADMET risk assessment and follow-up recommendations
- Lead Optimization transition plan
Deliverable: Final technical report + electronic data package
Sample Requirements
To ensure efficient Hit to Lead project execution, please provide the following starting materials and information:
| Sample Type | Specification |
|---|---|
| Target Protein | Recombinant protein purity >95%; concentration ≥2 mg/mL; stability data (DSF Tm curve strongly recommended) |
| Hit Compounds | 1–5 structurally defined compounds; purity >90%; quantity ≥5 mg each; primary screening data (IC50/SPR) required |
| Reference Compounds | Known active reference compounds for methodology validation (if available) |
| Project Background | Target biological function, disease relevance, known inhibitor information, selectivity watch list, synthetic constraints |
If protein expression or compound synthesis support is needed, contact our upstream services team.
Standard Deliverables
- Validation Report: Including binding kinetic parameters and thermodynamic analysis
- Structural Data: High-resolution electron density maps and coordinate files (PDB format) for protein-Hit/Lead complexes
- Computational Models: Molecular docking scores, binding mode predictions, and MD trajectory analysis reports
- Follow-up Recommendations: Detailed optimization roadmaps for the Lead Optimization stage
Our technical team responds within 24 hours. All inquiries protected under NDA.
Frequently Asked Questions
Case Study
NanoDSF Thermal Profiling: Platform Reliability Gate
Goal: Validate target protein batch stability and consistency before committing to structure-based lead optimization and biophysical screening.
Key Data:
- Instrument: Prometheus NT.Plex (NanoTemper), 20°C–95°C ramp, 1.5°C/min
- Samples: 2 customer protein batches, 0.2 mg/mL in PBS, duplicate replicates
- Tm1 (Boltzmann fit): 55.98°C (Sample 1) and 55.69°C (Sample 2) — batch-to-batch variation <0.3°C
- Thermal architecture: Two-step unfolding (onset ~45.5°C, second transition ~76°C) with no aggregation signal
Why it matters: In Hit-to-Lead, protein stability is the hidden variable that kills programs. Unstable or batch-variable target protein produces irreproducible SPR/ITC data and fails to crystallize — wasting synthesis cycles and screening budget. This case demonstrates our quality gate protocol: every protein entering our SBDD pipeline is NanoDSF-profiled to confirm thermal stability and batch consistency before compound soaking or biophysical validation begins. For biotechs outsourcing protein production, this means confidence that your material is screening-ready. For pharma, this means audit-ready QC documentation that protects downstream data integrity.
Figure 1. NanoDSF thermal unfolding profiles showing dual transitions (onset ~45.5°C; Tm2 ~76°C) with replicate agreement and no aggregation signal.
