Go beyond binding poses. Elucidate interaction fingerprints, quantify non-covalent forces, and derive structure–activity relationships with our specialized interaction modeling workflows.
Generate binary or weighted interaction fingerprints for H-bonds, hydrophobic contacts, ionic interactions, and π-stacking. Enable rapid similarity searching, clustering, and diversity analysis across compound sets.
Decompose binding free energy into residue-wise contributions (van der Waals, electrostatics, solvation). Identify hotspots and critical residues for potency and selectivity.
Map water molecules in the binding site, assess their thermodynamic profiles, and identify displaceable waters to guide rational lead optimization and affinity improvement.
Map interaction patterns to experimental activity data. Uncover activity cliffs, identify key interaction motifs, and prioritize compounds for synthesis.
Compare interaction fingerprints and energy decompositions across related targets to guide selectivity design and minimize off-target effects.
Analyze interaction stability and dynamics from molecular dynamics trajectories, including contact occupancies, hydrogen bond lifetimes, and water residence times.
Using PLIF, SIFt, or custom pharmacophore-based fingerprints to encode protein-ligand interactions into robust descriptors for chemoinformatics and machine learning.
End-point free energy methods with residue-wise decomposition to quantify per-residue contributions and solvation effects.
Grid-based water analysis to compute enthalpic and entropic contributions of water molecules in the binding site.
Detailed analysis of interatomic distances, angles, and occupancies for all non-covalent interactions.
Extract interaction fingerprints, H-bond occupancies, and water residence times from molecular dynamics simulations.
Automated generation of publication-quality interaction diagrams with color-coded interaction types and residue labels.
Challenge: Lead compound shows off-target activity against a related kinase.
Solution: Cross-target interaction profiling identifies a unique hydrophobic pocket; guide design of selective analogs with 50x selectivity.
Challenge: 50 hits from screening; limited capacity for synthesis.
Solution: IFP clustering + SAR correlation prioritizes 5 chemotypes with optimal interaction profiles and activity.
Challenge: Docking poses inconsistent with mutagenesis data.
Solution: Interaction fingerprinting and energy decomposition identify the most plausible binding mode consistent with experimental data.
Challenge: Two fragments bind in adjacent pockets; linking strategy unclear.
Solution: Interaction analysis identifies optimal vectors and linker geometries for fragment merging.
Challenge: Identify which residues drive binding affinity for a series.
Solution: Per-residue energy decomposition across analogs pinpoints critical hotspots for optimization.
Challenge: Compound shows activity against a related family member.
Solution: Interaction fingerprint comparison reveals differences in a key subpocket, guiding selective design.
Our team combines deep expertise in computational chemistry with a focus on actionable insights. We don't just generate data – we interpret it in the context of your project goals, delivering clear recommendations for design and optimization.
Curate protein and ligand structures, assign protonation, tautomers, and optimize geometry.
Generate high-quality binding poses using flexible docking protocols.
Compute interaction fingerprints, energy decomposition, and solvent analysis.
Correlate interaction patterns with activity data to prioritize compounds and identify optimization vectors.
Deliver interactive 3D visualizations, 2D interaction diagrams, and a comprehensive technical report.
Goal: rationalize activity cliffs.
Approach: docking + IFP clustering + activity mapping → identified key H-bond as activity switch.
Goal: improve kinase selectivity.
Approach: per-residue decomposition across 5 kinases → designed selective analog with 50x selectivity.
Goal: boost potency.
Approach: WaterMap + docking → replaced a water with a methyl group → 8x affinity gain.
Docking focuses on generating binding poses and ranking by affinity. Interaction modeling goes further: it quantifies and classifies the specific interactions (H-bonds, hydrophobic, etc.), decomposes energy, and correlates with SAR, providing deeper mechanistic insight.
Yes. We can analyze covalent adducts and include the covalent bond in interaction fingerprints and energy decomposition, offering a complete picture of binding.
Not necessarily. However, if you have activity data, we can perform SAR correlation analysis to map interaction patterns to potency, which is invaluable for lead optimization.
Typical projects range from 2–4 weeks depending on library size and complexity. Rapid turnaround is available for urgent projects.
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