MagHelix™ AI-Based Drug Discovery (AIDD) Platform
Deep Learning Target Prediction
Generative Molecular Design
ADMET Forecasting
Traditional CADD relies on static structures and force fields. Our AIDD platform deploys deep learning, generative AI, and project-specific model training on your proprietary data — uncovering patterns invisible to structure-centric methods. A cross-functional team of biologists, chemists, data scientists, and AI experts builds and refines models with every experimental cycle, with direct handoff to Structural Biology, CADD, and Lead Optimization.
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 AI drug discovery platforms offer generic, pre-trained models that fail on novel chemical space or rare disease targets. Virtual biotechs lack the cross-functional team — biologists, chemists, data scientists, AI engineers — required to translate predictions into synthesizable compounds. Pharma teams watch AI-generated molecules crash in vivo because the models were never exposed to experimental feedback. We built this platform to solve that: every campaign trains project-specific local models on your data, with experimental results from our Structural Biology, Zebrafish, and ADMET platforms feeding back into model refinement.
Your CapEx is in chemistry and biology. Ours is in GPU clusters, data infrastructure, and cross-functional expertise.
| Stage | What We Deliver | What You Don't Need to Build |
|---|---|---|
| Data Strategy | Assessment of your proprietary data + public databases for model training | Data science team |
| Target Prediction | Deep learning target identification for complex diseases with unclear mechanisms | Bioinformatics infrastructure |
| Generative Design | AI-generated novel scaffolds; de novo molecular design; affinity fingerprinting | Generative AI software, GPU cluster |
| ADMET Forecasting | DNN-based hERG, CYP450, BBB, solubility, toxicity prediction before synthesis | ML model development team |
| Experimental Validation | Direct handoff to Structural Biology, CADD, Zebrafish, or ADMET | — |
Production-Ready Deliverables: Every campaign ships with project-specific trained models, AI-generated compound libraries, ADMET risk forecasts, and experimental validation data — creating a compounding learning asset for your portfolio.
- ✓ Milestone-based pricing aligned with your fundraising cycles
- ✓ No AI infrastructure overhead — GPU clusters, model training, and cross-functional team under a single project manager
Targets with unclear mechanisms. Rare diseases. Novel modalities. "Undruggable" is our starting point.
Data-centric advantage
Where conventional CADD fails due to lack of structural information, our AI platform leverages multi-omics, transcriptomic, and phenotypic data to identify targets and mechanisms in complex diseases.
Project-specific model training
Unlike static SaaS platforms, we build and improve local models with every experimental cycle — your Phase 0 data becomes training data for Phase 1.
IP firewall & encrypted data infrastructure
Full audit trails, client-isolated model weights, and contractual exclusivity on all trained models and generated compounds.
Core Service Modules
Service Module At-a-Glance
| Service | Core Capability | Platform Integration | Typical Timeline |
|---|---|---|---|
| AI Target Prediction & Validation | Deep learning target identification; multi-omics integration; mechanism deconvolution for complex diseases | AlphaFold3 structural validation; direct handoff to SBDD | 2–4 weeks |
| AI-Enhanced Virtual Screening | Deep neural network scoring; affinity fingerprinting; data fusion from multiple algorithms; 1M+ compound throughput | Integration with CADD Platform; experimental validation via Biophysics | 1–2 weeks |
| Generative Molecular Design | RNN/VAE/RL-based de novo design; scaffold hopping; PROTAC ternary complex prediction; antibody humanization | Direct synthesis handoff; FEP affinity validation; ADMET filtering | 2–4 weeks |
| AI ADMET Prediction & Risk Profiling | DNN-based physicochemical and ADMET property prediction; hERG, CYP450, BBB, P-gp, toxicity alerts; target fishing | Cross-validation with In Vitro ADME-Tox and Zebrafish; model retraining on experimental outcomes | 1–2 weeks |
AI Target Prediction & Validation
Finding Targets When Mechanisms Are Unknown
Key Features:
- Deep Learning Target Identification — Graph neural networks and transformer-based models analyze multi-omics data (genomics, transcriptomics, proteomics) to identify disease-relevant targets without prior structural information.
- Complex Disease Advantage — AI excels where conventional CADD fails: diseases with unclear targets or mechanisms (neurodegeneration, autoimmune, rare diseases) where structure-centric approaches lack starting points.
- AlphaFold3 Structural Validation — Predicted targets are structurally validated with AlphaFold3 to assess pocket druggability and guide downstream SBDD.
What We Offer: For biotechs pursuing novel indications, this module replaces years of target biology with data-driven hypotheses. For pharma, multi-omics integration reveals off-target liabilities and polypharmacology opportunities invisible to single-assay screens.
AI-Enhanced Virtual Screening
Data Fusion Beats Single-Algorithm Scoring
Key Features:
- Deep Neural Network Scoring — DNNs trained on project-specific data generate affinity fingerprints that outperform generic docking scores, particularly for novel scaffolds.
- Data Fusion & Consensus — Multi-algorithm integration combining DNN predictions, docking scores, pharmacophore matches, and QSAR outputs into a unified ranking.
- Low-Data Learning — Transfer learning and few-shot ML enable effective screening even with limited training examples — critical for rare disease and novel target programs.
What We Offer: For virtual biotechs with proprietary assay data, this module transforms sparse historical results into predictive models. For pharma, data fusion reduces false positives by 50–70% compared to single-method VS.
Generative Molecular Design
AI Creates Novel Molecular Structures from Scratch
Key Features:
- De Novo Design — RNN, VAE, and reinforcement learning models generate novel molecular structures with desired pharmacological properties from target structure or ligand pharmacophores.
- PROTAC & Ternary Complex Design — AlphaFold3 predicts ternary complex structures (protein-ligand-protein) for targeted protein degradation; AI optimizes linker length and geometry.
- Antibody & Biologics — AI-driven humanization and affinity maturation for therapeutic antibodies; sequence-based property prediction without structural templates.
What We Offer: When HTS and docking fail to deliver viable scaffolds, generative AI explores chemical space beyond existing libraries. For difficult targets, this is often the only path to novel leads.
AI ADMET Prediction & Risk Profiling
Predicting Liability Before Synthesis
Key Features:
- Deep Neural Network Predictions — DNNs show strong performance in predicting physicochemical parameters and ADMET properties (hERG, CYP450 inhibition, BBB permeability, P-gp substrate, oral bioavailability) from molecular structure alone.
- Project-Specific Model Training — Models are trained and refined on your proprietary data and experimental outcomes, improving accuracy as the project progresses.
- Experimental Validation Loop — Predictions cross-validated with In Vitro ADME-Tox and Zebrafish Screening; experimental results feed back to retrain models.
What We Offer: For lead optimization, ADMET forecasting prioritizes analogs with favorable safety profiles before synthesis. For early discovery, toxicity alerts flag liabilities before chemistry investment.
Technology Platform
Integrated AIDD Infrastructure: Data Acquisition + Deep Learning + Generative AI + Experimental Feedback, Zero Handoffs
Traditional AI drug discovery platforms operate as black boxes — predictions in, compounds out, with no experimental validation loop. Our platform unifies data science, computational chemistry, and wet-lab biology under one project team, with every experimental result feeding back into model retraining.
Computational Platform — Dry Lab
| Capability | Details |
|---|---|
| Deep Learning Target Prediction | Graph neural networks (GNNs), transformer-based models, and multi-omics integration for target identification in complex diseases |
| Generative Molecular Design | RNN + RL (ReLeaSE), VAE, diffusion models for de novo scaffold generation; PROTAC linker optimization |
| DNN Virtual Screening | Deep neural network scoring functions; affinity fingerprinting; few-shot learning for low-data targets |
| ADMET Forecasting | DNN and ensemble models for hERG, CYP450, BBB, solubility, toxicity; multitask learning across related endpoints |
| Data Fusion & Consensus | Multi-algorithm integration of DNN, docking, pharmacophore, and QSAR outputs for robust hit prioritization |
| AlphaFold3 Integration | Structural validation of predicted targets; ternary complex modeling for PROTACs; pocket druggability assessment |
Hardware & Data Resources
| Resource | Specification |
|---|---|
| GPU Cluster | A100/H100 GPUs for distributed deep learning training and generative model inference |
| Data Infrastructure | Client-isolated project folders; full audit trails; automated ML pipeline versioning (MLflow) |
| Databases | Integration with public databases (ChEMBL, PubChem, DrugBank) and proprietary in-house Drug Information system |
| Software Stack | PyTorch, TensorFlow, RDKit, DeepChem, scikit-learn; custom model development for project-specific needs |
| Cross-Functional Team | Biologists, medicinal chemists, data scientists, and AI/ML engineers co-located under single project management |
Platform Edge: The ability to ingest your assay data on Monday, train a project-specific DNN model by Wednesday, generate and rank novel compounds by Thursday, and validate predictions in our Zebrafish or Biophysics lab by Friday — all under one project team with unified data architecture.
Biacore 8K+ SPR
Rigaku XtaLAB Synergy-R
Zeiss Axio Observer Z1
Bruker Avance NEO 800 MHz
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 Experimental Truth
Static AI models trained on public data decay when exposed to novel chemical space. Our platform feeds every experimental result back into model weights — so each campaign improves the next.
Data Ingestion & Target Prediction
Multi-omics and proprietary assay data train project-specific target prediction models; AlphaFold3 validates structural druggability
→ Feeds into Generative Design
AI Generation & Virtual Screening
Generative AI creates novel scaffolds; DNN scoring and data fusion rank candidates; ADMET models filter liabilities before synthesis
→ Feeds into Experimental Validation
Experimental Validation
Biophysical, structural, and in vivo data validate AI predictions and identify model failure modes
→ Feeds into Model Retraining
Model Retraining
Experimental outcomes (activity, toxicity, PK) retrain project-specific models, improving next-cycle accuracy and creating a compounding IP asset
→ Feeds back into AI
Industrial Value:
For Biotechs
Your first campaign's experimental data trains the models for your second target. Project-specific model weights become a compounding IP asset that increases in value with every compound tested.
For Pharma
Every model prediction is linked to an experimental outcome with project ID, timestamp, and model version — fully audit-ready for regulatory submissions and internal portfolio reviews.
Project Management & Execution
Project Workflow
A standardized, milestone-driven execution system. From data ingestion to AI-validated candidates.
01 Data Strategy
- Proprietary data assessment; multi-omics integration; target hypothesis generation; public database curation
Deliverable: Data strategy report + target hypothesis
02 Model Training
- Project-specific DNN/GNN model training; transfer learning from related targets; cross-validation and uncertainty quantification
Deliverable: Trained model weights + validation metrics
03 AI Generation / Screening
- Generative AI de novo design or DNN-enhanced virtual screening; data fusion ranking; docking pose validation
Deliverable: Ranked AI-generated or screened compound list
04 ADMET Filtering
- DNN-based ADMET forecasting (hERG, CYP450, BBB, toxicity); target fishing for off-target liability; structural alert filtering
Deliverable: ADMET risk profile + liability assessment
05 Experimental Validation & Handoff
- Biophysical validation; structural biology confirmation; zebrafish or ADMET experimental validation; model retraining
Deliverable: Validated candidates + retrained model + regulatory documentation
Sample Requirements
| Sample Type | Specification |
|---|---|
| Proprietary Data | Historical assay data (activity, toxicity, PK); format: CSV/SDF with SMILES and endpoint values; minimum 50–100 data points for effective model training |
| Target Information | Disease indication; known targets (if any); multi-omics data (RNA-seq, proteomics) welcome |
| Screening Scope | De novo generation vs. virtual screening; target class (kinase, GPCR, PPI, ion channel); modality (small molecule, peptide, antibody) |
| Prioritization Criteria | Synthetic accessibility; patent constraints; ADMET thresholds; desired affinity range |
Standard Deliverables
- Project-specific trained model weights and architecture documentation
- AI-generated or screened compound library with ranking rationale
- ADMET risk forecast (hERG, CYP450, BBB, solubility, toxicity alerts)
- Target liability and polypharmacology assessment
- Experimental validation data package
- Direct handoff to Structural Biology, CADD, Lead Optimization, or ADMET
Our technical team responds within 24 hours. All inquiries protected under NDA.
Frequently Asked Questions
Case Study
Case #1: Structure-Based Virtual Screening
Goal: Identify bioactive compound candidates through computational structure-based virtual screening, leveraging protein structural models for docking-based hit prioritization.
Methods: Target protein structure prepared from PDB database or AI-generated virtual models; molecular docking performed to assess compound binding interactions and generate protein-compound complex predictions.
Results: Delivered a ranked compound list with predicted binding poses and interaction maps, enabling prioritized compound procurement for downstream experimental validation.
Figure 1. Target protein structure model used as receptor for virtual screening.
Figure 2. Pharmacophore-based and docking-based virtual screening cascade.
Figure 3. Representative protein-compound binding mode from docking analysis.
Case #2: Cell-Based GPCR Agonist Assay
Goal: Functionally validate compound activity against the CALCRL-RAMP3 GPCR target to close the AI prediction-to-experiment loop.
Methods: Cell-based agonist assay measuring dose-dependent receptor activation; efficacy and potency parameters derived from dose-response curves.
Results: Confirmed dose-dependent agonist activity with defined efficacy metrics; provided experimental validation data to refine AI predictive models for GPCR-targeted campaigns.
Figure 1. Cell-based CALCRL-RAMP3 agonist assay dose-response curve, validating computationally prioritized compounds.
Need AI-driven drug discovery to accelerate your pipeline? Our team can design a customized AIDD strategy tailored to your data assets, target class, and regulatory milestones. Contact our scientific team today.
