QSAR Analysis
Ligand-Based Modeling 
Multi-Dimensional QSAR 
De Novo Design Integration QSAR models that correlate chemical structure with biological activity, guiding lead optimization and de novo design for targets where structural data is scarce.
Why QSAR Analysis Is the Critical Foundation
For targets with little structural information, structure-based methods fail. Seed-stage biotechs pursuing GPCRs, ion channels, or transcription factors without crystal structures need ligand-based guidance. Pharma teams need quantitative rules that predict how each substitution modulates potency, selectivity, and ADMET. We deliver validated QSAR models — from classical CoMFA/CoMSIA to AI-enhanced 4D/5D/6D frameworks — that predict new compound activity, optimize existing scaffolds, and feed directly into virtual screening and de novo design.
What Sets the Platform Apart
Multi-Dimensional QSAR
Classical 3D-QSAR (CoMFA, CoMSIA) combined with 4D/5D/6D extensions and modern ML, capturing conformational, solvation, and induced-fit contributions.
Ligand-First Design
Purpose-built for targets lacking experimental structures, turning bioactivity data alone into actionable design rules.
Continuous Validation Loop
Rigorous internal/external validation with automated model updating as new assay data arrives, ensuring predictions stay reliable across program stages.
Technology Suite
Molecular Descriptor & Dataset Curation
Comprehensive Descriptor Generation and Training Set Design
Key Features:
- Comprehensive Descriptor Generation — 2D topological (Morgan, ECFP), 3D geometric (WHIM, GETAWAY), and quantum mechanical (DFT charges, HOMO-LUMO) descriptors computed at scale.
- Training & Test Set Design — Cluster-based and diversity-driven compound selection ensuring model generalizability across chemical space.
- Data Quality Control — Automated outlier detection, assay artifact flagging, and activity cliff annotation to prevent noise-driven models.
- Ligand Alignment & Conformational Sampling — Multiple alignment strategies (atom-based, pharmacophore-based, field-based) for 3D-QSAR input preparation.
Ideal For: Hit-to-lead campaigns with 20–200 analogs; fragment expansion where structural data is absent; patent-bypass campaigns requiring rapid property prediction.
What We Offer:
A curated descriptor matrix and validated dataset split. Seed-stage biotechs receive ligand-based design rules without crystallography investment. Pharma teams get continuously updated models as new screening data arrives.
3D/4D/5D/6D-QSAR Modeling
Classical and Advanced QSAR Modeling from CoMFA to AI-Enhanced 6D Frameworks
Key Features:
- CoMFA & CoMSIA — Comparative molecular field analysis and similarity indices analysis mapping steric, electrostatic, hydrophobic, and hydrogen-bond fields around aligned ligands.
- Advanced 3D-QSAR — Topomer CoMFA for scaffold-independent alignment; COMBINE for binding energy decomposition; CoMSA for surface-based analysis; CoRIA for residue interaction contributions; HQSAR for fragment-based holographic models.
- 4D/5D/6D Extensions — 4D-QSAR incorporating multiple conformational states and induced-fit ensembles; 5D-QSAR with solvation models; 6D-QSAR capturing dynamic receptor flexibility via MD snapshots.
- AI-Enhanced Hybrid Models — Graph neural networks and ensemble ML integrating classical QSAR fields with modern deep learning for improved prediction accuracy.
Ideal For: Lead optimization requiring spatial understanding of substitution effects; scaffold hopping where 2D similarity fails; allosteric modulator programs with limited structural data.
What We Offer:
A validated QSAR model with cross-validated R²/Q² and external test set metrics. For medicinal chemistry teams, we deliver 3D contour maps showing exactly where to add steric bulk or polarity to boost activity.
SAR Interpretation & De Novo Design Integration
AI-Driven QSAR-Guided Design with SHAP Analysis and Bioisosteric Replacement
Key Features:
- SHAP & Matched Molecular Pair Analysis — Atom-level contribution maps and transformation rules quantifying the effect of specific substitutions on potency and ADMET.
- Bioisosteric Replacement — AI-suggested replacements that preserve QSAR-favorable fields while improving synthesizability or escaping IP constraints.
- QSAR-Guided Virtual Screening — Enrichment of commercial libraries (Enamine, Mcule) using 3D pharmacophore + QSAR consensus scoring.
- ADMET Property Prediction — Simultaneous QSAR models for solubility, hERG, microsomal stability, and CYP inhibition in a unified multi-objective framework.
Ideal For: De novo drug design from scratch; SAR campaigns with flat activity relationships; multi-parameter optimization balancing potency, safety, and PK.
What We Offer:
An interpretable design report with 3D contour maps, fragment contribution tables, and a prioritized list of next analogs. For virtual screening, we deliver QSAR-enriched hit lists ready for docking and biophysical validation.
Platform Instrumentation
Core Instruments
| Instrument | Capability |
|---|---|
| NVIDIA DGX H100 | AI model training and 4D/5D/6D-QSAR ensemble inference at scale |
| KNIME Analytics Platform | Automated descriptor generation, model validation, and pipeline deployment |
| CDD Vault | Collaborative data management and SAR visualization |
| PerkinElmer ChemDraw / Signals | Structure drawing and cheminformatics data curation |
| Agilent 1290 Infinity II / 6470 LC-MS | Analytical confirmation of predicted analogs and metabolite identification |
| Waters ACQUITY UPLC H-Class | Purity and solubility profiling of QSAR-designed compounds |
| Tecan Fluent 780 | Automated assay preparation and compound handling for validation |
Standardized Workflow
Project Workflow
A milestone-driven execution system from bioactivity data to validated design rules.
01 Target Review
- Target class and structural data availability
- Bioactivity dataset inventory and assay context
- Deliverable: QSAR strategy report
02 Data Curation
- Structure standardization and activity normalization
- Descriptor generation and outlier removal
- Deliverable: Curated dataset + descriptor matrix
03 QSAR Model Building
- CoMFA/CoMSIA field mapping or ML model training
- Internal/external validation (Y-randomization, scaffold split)
- Deliverable: Validated model + performance metrics
04 Interpretation & Design
- 3D contour map and SHAP interpretation
- Matched-pair rule extraction
- Deliverable: Design rationale + next-analog list
05 Experimental Validation
- Synthesis of prioritized analogs
- Biophysical / cellular assay confirmation
- Deliverable: Validated predictions + model update
Sample Requirements
- Compound structures (SDF/SMILES) with measured activity (IC50, Kd, %inhibition, EC50)
- Assay protocol, normalization method, and known artifacts
- Target structure or pharmacophore (if available for 3D-QSAR alignment)
- Desired ADMET properties for multi-objective optimization
Standard Deliverables
- Curated descriptor matrix and dataset
- Validated QSAR model files (CoMFA, CoMSIA, ML, GNN) with R²/Q²/RMSE
- 3D contour maps (steric, electrostatic, hydrophobic fields)
- Applicability domain definition and reliability scoring
- SHAP/attention-based interpretation maps
- Matched molecular pair transformation table
- Recommended next analogs with predicted activity and ADMET profile
- Final technical report with medicinal chemistry rationale
Frequently Asked Questions
Case Study
Case Study: Multidimensional Chirality-Aware QSAR for CCR2 Antagonist Potency
Goal: Benchmark a classical 2D/3D-QSAR pipeline that leverages novel relative chirality indices (RCIs) to quantify stereoelectronic contributions and predict receptor binding affinity for chiral drug candidates.
Key Data:
- Multidimensional chirality descriptor space: A family of 198 relative chirality indices (RCIs) was computed from topological, information-theoretic, and shape descriptors for 20 chiral CCR2 antagonists, capturing branching patterns and bond multiplicity around stereocenters that conventional 2D descriptors cannot distinguish.
- PCA-driven model construction: Dimensionality reduction extracted 3 principal components (explaining 94.3% variance), yielding a 3-predictor regression model with R² = 0.823 and Adj R² = 0.790. Translating PCs back to interpretable RCIs (RCIOPM, RCIBIC0, RCIJ) gave a practical 3-descriptor equation with R² = 0.742 and five-fold cross-validation Rcv² = 0.839.
- Mechanistic interpretability: The selected descriptors encode overall path multiplicity, bond information content, and Balaban J-index—reflecting how branching topology and bond-type distribution at the chiral center govern CCR2 antagonistic potency.
Why it matters: This independent 2025 study validates that a structured, Hansch-type QSAR workflow—combining stereochemistry-aware descriptors, PCA-based dimensionality reduction, and cross-validated linear regression—delivers both predictive accuracy and mechanistic interpretability for chiral ligands. It demonstrates our standardized 2D/3D-QSAR modeling capability: from molecular descriptor generation through statistical validation to actionable SAR insights for stereoselective drug design.
Figure 1. Predicted versus experimental pIC50 values for 20 chiral CCR2 antagonists, demonstrating strong correlation across PCA-based and descriptor-based QSAR models. (Natarajan R, et al. 2025)
Reference
Natarajan R, et al. Quantitative Structure-Activity Relationship (QSAR) Modeling of Chiral CCR2 Antagonists with a Multidimensional Space of Novel Chirality Descriptors. Molecules. 2025 Jan 14;30(2):307.
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