Hit Biophysical Characterization

Weak-Affinity Fragment Detection and Epitope Mapping in Solution

Key Features:

• Bruker 600 MHz+ AVANCE NEO — Detects weak-affinity ligand binding and maps epitopes without immobilizing the target, essential for intrinsically disordered proteins and dynamic PPI interfaces.
• MD-Guided Epitope Assignment + AI PAINS Filtering — Molecular dynamics-simulated binding poses focus NMR experiments on the most informative residues, while AI classifiers eliminate false-positive chemotypes before sample prep.
• Ideal For — Fragment hit confirmation; epitope mapping on disordered proteins and dynamic PPI targets.

Explore the Power of Computationally Directed NMR:

For fragment-based screening campaigns, STD-NMR is often the only technique that validates millimolar-level hits with structural context. For PPI targets like c-Myc/Max, MD simulations predict transient cryptic sites that focus epitope experiments on residues that matter—replacing blanket scans with targeted structural questions. Seed-stage biotechs use this as a low-protein-consumption validation step before committing to SPR scale-up; pharma teams use the epitope data to generate binding hypotheses that feed directly into medicinal chemistry.

High-Throughput Ligand-Induced Stability Screening

Key Features:

• 384-well DSF Format — Measures ligand-induced thermal stability shifts with microgram-scale protein per well, fully compatible with automated liquid-handling and compound library logistics.
• ML-Predictive Pre-Ranking — An ML model trained on ligand descriptors predicts ΔTm to pre-rank compounds and optimize buffer conditions before the assay, conserving protein budget.
• Ideal For — Early hit triage; compound series ranking; buffer optimization.

What We Offer:

TSA is the fastest, lowest-cost entry point for virtual biotechs without biophysical infrastructure—it validates screening outputs before you commit to scale-up synthesis. For larger pharma teams, it serves as a first-line filter: only compounds showing meaningful ΔTₘ shifts advance to ITC or SPR for full kinetic and thermodynamic profiling. The ML pre-ranking model is continuously retrained on internal program data, improving prediction accuracy for your target class with every campaign.

Real-Time Label-Free Kinetic Profiling

Key Features:

• Biacore™ 8K — Real-time, label-free kinetics (kon, koff, KD) with fragment-level sensitivity down to sub-100 Da compounds; eight-channel parallel processing enables unattended 24-hour selectivity panels.
• Docking-Guided Assay Design — Molecular docking-predicted binding orientation informs sensor-chip surface chemistry and regeneration strategy, cutting assay development time by 30–40%.
• Ideal For — Kinetic profiling; selectivity screening across protein family members; fragment hit validation.
• Ideal For — Kinetic profiling; selectivity screening across protein family members; fragment hit validation.

Why It Matters:

SPR is the workhorse of biophysical validation—but only when the assay is designed for your specific target. For kinases, standard orientations work; for PPI disruptors or membrane proteins, docking predictions determine whether to immobilize the target or the ligand, and which surface chemistry prevents denaturation. This computational pre-calibration is why our SPR campaigns rarely fail assay development—unlike generic CRO approaches that treat every target as a kinase and burn your protein on incompatible formats.

Parallel Kinetic Readout for High-Throughput Binding Studies

Key Features:

• Octet® RH16 — 16-channel simultaneous acquisition, crude-lysate and supernatant-compatible, enabling rapid concentration-series studies when purified protein is still limited.
• AI Solubility Pre-Filter — In-silico solubility and aggregation classifiers flag at-risk compounds before the BLI run, eliminating false negatives caused by colloidal instability.
• Ideal For — High-throughput kinetic characterization; biologics QC; limited-purification workflows.

How It Works:

BLI measures binding by detecting interference patterns at the tip of disposable biosensors—no microfluidics, no surface regeneration bottlenecks. For seed-stage biotechs expressing target protein in small-scale cultures, BLI validates binding before investing in large-scale purification. For biologics teams, the 16-channel format supports rapid developability assessment across concentration series. The AI pre-filter ensures only compounds predicted to remain soluble and monodisperse enter the run, reducing failed experiments and data rejection rates that otherwise derail timelines.

Gold-Standard Thermodynamic Profiling in a Single Experiment

Key Features:

• MicroCal PEAQ-ITC — Label-free direct measurement of ΔH, ΔS, ΔG, and stoichiometry in a single automated titration; gold-standard for mechanism-of-action classification.
• MD-Derived Enthalpy Prediction — Molecular dynamics decomposes predicted binding enthalpy to forecast ΔH/ΔS contribution profiles before the experiment, guiding buffer selection and injection protocol.
• Ideal For — Thermodynamic profiling; mechanism-of-action classification; enthalpy-driven optimization strategies.

The Strategic Advantage:

ITC is the only technique that gives you the full thermodynamic signature in one experiment. For lead optimization, knowing whether a hit is enthalpy-driven or entropy-driven changes your medicinal chemistry strategy entirely—enthalpic binders are optimized by adding polar interactions, entropic binders by expanding hydrophobic contacts. MD simulations predict these signatures before the first injection, so your ITC campaign confirms a hypothesis rather than searching blindly. This is how you turn thermodynamic data into concrete optimization vectors instead of raw curves.

High-Precision Thermal Unfolding for Complex Targets

Key Features:

• MicroCal VP-Capillary DSC — Characterizes ligand effects on protein thermal unfolding transitions with precision that 384-well TSA cannot match, detecting subtle stability shifts in multi-domain targets.
• MM-PBSA/GBSA-Guided Interpretation — Calculated protein-ligand complex stability (MM-PBSA/GBSA) predicts ΔTₘ direction before the experiment, preventing misinterpretation of complex multi-domain transitions.
• Ideal For — Target stability profiling; selectivity assessment across variants; formulation screening.

What We Offer:

DSC is essential when your target has multiple domains, when TSA results are ambiguous, or when you need to prove that your compound stabilizes the correct domain without destabilizing others. For epigenetic readers with tandem domains, DSC resolves which domain is ligand-bound. For biologics formulation teams, it identifies aggregation-prone thermal transitions that threaten shelf stability. MM-PBSA/GBSA calculations predict the direction and magnitude of thermal shifts before the experiment, so you know what to expect and how to interpret complex data—rather than guessing.

Viscoelastic Sensing for Membrane and Lipid Systems

Key Features:

• Q-Sense Analyzer — Real-time label-free binding with viscoelastic surface property capture, revealing rigid vs. flexible receptor conformations that rigid surface techniques miss.
• MD-Assisted Membrane Interpretation — Molecular dynamics simulation of membrane-protein lateral diffusion and lipid-bilayer dynamics assists data interpretation, distinguishing true binding from lipid-reorganization artifacts.
• Ideal For — Membrane proteins; cell-surface receptors; lipid-interaction and mechanistic studies.

Explore the Power of Membrane-Protein Biophysics:

Standard SPR and BLI often fail for GPCRs, ion channels, and other integral membrane proteins because immobilization denatures the target or disrupts the lipid environment. QCM-D captures binding in native lipid bilayers while reporting viscoelastic changes that reveal mechanistic insight—whether the compound induces rigidification or flexibility in the receptor. MD simulations of membrane-protein lateral diffusion predict these viscoelastic signatures, ensuring QCM-D data is interpreted as mechanistic evidence, not just another kinetic curve.

Solution-Phase Quantification Without Immobilization

Key Features:

• Monolith NT.258 — Immobilization-free, solution-phase quantification; membrane-protein compatible in native lipid nanodiscs or detergent. Eliminates surface-artifact false positives entirely.
• In-Silico Labeling Optimization — Predictive modeling of fluorophore placement cuts protein consumption to microgram scale per experiment and reduces preparation time.
• Ideal For — Challenging targets; low-solubility compounds; PPI disruptors; membrane proteins that resist surface formats.

Why It Matters:

MST is the default choice when your target cannot be immobilized without losing activity—whether it is a membrane protein in nanodiscs, an intrinsically disordered protein, or a PPI disruptor with no stable pocket. For virtual biotechs with microgram-scale protein yields, MST provides K_D data that SPR cannot deliver due to surface constraints. In-silico labeling-site prediction ensures the fluorophore is placed where it reports binding without interfering with the interaction—a computational step that generic CROs skip and that often determines assay success or failure.

First-Line Colloidal Stability and PAINS Screening

Key Features:

• Zetasizer Nano — Detects particle size and aggregation with only microliter volumes, serving as the first line of defense against colloidal instability that corrupts downstream kinetic and thermodynamic readouts.
• AI PAINS & Aggregation Classifier — Flags PAINS-like and detergent-interfering compounds in silico before screening, typically eliminating 15–25% of the queue before protein is consumed.
• Ideal For — PAINS triage; colloidal stability assessment; formulation and aggregation-risk screening.

How It Works:

Aggregators and PAINS compounds destroy kinetic data by producing artifactual Tₘ shifts and false-positive SPR signals. Our AI classifier, trained on 500+ programs, predicts colloidal instability from chemical structure before a sample is prepared. For seed-stage biotechs, this means fewer wasted synthesis cycles on compounds that will fail in vivo. For teams working with scarce, three-week-expression membrane proteins, the protein savings alone justify the workflow. Every compound entering the MagHelix™ platform is computationally scored for aggregation risk, ensuring downstream biophysical data is built on clean, monodisperse samples.

Label-Free Complex Visualization and Covalent Confirmation

Key Features:

• Waters / Thermo LC-MS Platform — Label-free complex visualization via native MS, SEC-MS, and HDX-MS modalities. Confirms covalent stoichiometry, ternary complex integrity, and conformational dynamics in a single analytical pipeline.
• MD-Guided HDX Footprint Prediction — Molecular dynamics-guided HDX footprinting identifies allosteric sites and conformational hot spots for targeted experimental probing, replacing blanket scans with directed structural questions.
• Ideal For — Covalent binding confirmation; ternary complex stoichiometry; conformational analysis; allosteric site mapping.

The Strategic Advantage:

MS is the only biophysical technique that gives you stoichiometry, covalent adduct confirmation, and conformational dynamics in one platform. For covalent inhibitor programs, native MS proves 1:1 or 2:1 binding before cell-based assays. For PROTAC and molecular-glue teams, SEC-MS validates ternary complex formation—failure to assemble the ternary complex kills the program early, before expensive cell studies. MD-guided HDX predictions focus experiments on allosteric sites predicted to undergo conformational change upon ligand binding, maximizing structural insight per experiment and conserving precious instrument time.