CD ComputaBio provides Structure-based Drug Design (SBDD) Service to help research teams turn protein structures, binding pockets, docking poses, and SAR observations into testable drug design decisions. Starting from a prepared target model, crystal structure, cryo-EM structure, or protein structure modeling result, our scientists design customized workflows for hit discovery, lead optimization, binding mode interpretation, and candidate prioritization.
We prepare target structures, refine missing regions, assign protonation states, evaluate cofactors or ions, and map ligand-binding regions for drug design.
For teams that need starting points, we support compound library preparation, docking-based screening, filtering, and hit shortlist generation.
We analyze how compounds interact with the binding site and translate hydrogen bonds, hydrophobic contacts, electrostatics, and steric effects into design suggestions.
We help compare analogs, identify optimization vectors, and prioritize modifications that may improve affinity, selectivity, stability, or developability.
For promising poses or analog series, we can evaluate complex stability and binding energy trends using molecular dynamics and free-energy methods.
SBDD can also guide protein-drug interfaces, peptide binders, PROTAC ternary complexes, ADC components, and nucleic-acid-targeting molecules.
| Project Question | Recommended Computational Entry Point | Key Readouts | Next Experimental Decision |
|---|---|---|---|
| I only have a sequence or predicted target model. Can I start drug design? | Structure quality review, pocket detection, druggability modeling, and model preparation | Binding-site confidence, pocket geometry, residue environment, model limitations | Proceed to docking, refine model, test mutation, or request additional structural data |
| Which compounds should I buy or test first? | Virtual screening, docking, scoring, clustering, and ADMET filtering | Ranked hit list, pose quality, chemical diversity, risk flags | Purchase/synthesize top candidates for biochemical or cell-based assays |
| Why are some analogs active while others are weak? | Comparative docking, interaction mapping, SAR interpretation, and hotspot analysis | Lost/gained interactions, steric clashes, water-mediated effects, subpocket fit | Prioritize modification sites and design focused analogs |
| Is the docked pose stable enough to support lead optimization? | Protein-ligand molecular dynamics simulation and interaction persistence analysis | Pose stability, conformational drift, H-bond persistence, key residue contacts | Select compounds for synthesis, reject unstable poses, or redesign the binding hypothesis |
| Could this compound show off-target or selectivity risk? | Cross-target docking, pocket comparison, reverse docking, and ADMET/off-target prediction | Target selectivity trend, off-target binding hypotheses, property risk profile | Plan counter-screening assays or redesign selectivity features |
We clarify whether the goal is target enablement, hit discovery, docking validation, analog prioritization, selectivity improvement, mechanism explanation, or lead optimization.
We prepare PDB, AlphaFold, homology, cryo-EM, or modeled structures; correct missing atoms or loops when needed; define protonation states, cofactors, metals, waters, and binding-site constraints.
We evaluate pocket volume, shape, polarity, residue composition, flexibility, conserved hotspots, and ligandability to decide whether the target is suitable for SBDD.
We prepare ligands, analogs, fragments, or focused libraries; generate conformers and protonation states; then select suitable docking strategies such as rigid docking, flexible docking, induced-fit docking, or covalent docking.
We inspect docking poses using structural criteria, interaction networks, conserved residues, subpocket occupancy, ligand strain, water effects, and agreement with known SAR or mutagenesis data.
For high-priority candidates, we apply MD simulation, binding free-energy analysis, or other advanced methods to improve confidence before synthesis or assay testing.
We deliver a ranked compound/design list, structural rationale, risk notes, visualized binding modes, and recommended next experiments or optimization directions.
When experimental structures are unavailable, we can support protein modeling, homology modeling, receptor modeling, membrane protein modeling, and binding-site preparation.
Docking settings are selected based on ligand flexibility, target movement, covalent warhead design, peptide/macrocycle behavior, and project confidence requirements.
For fragment-oriented projects, Fragment-based Drug Design (FBDD) can help identify ligand-efficient starting points and expansion directions.
We can combine SBDD with pharmacophore modeling to capture essential interaction features and guide focused screening.
When a clear pocket and design objective are available, AI-based drug design can be used to explore novel chemical ideas under structural constraints.
We can incorporate in silico ADMET prediction, physicochemical property modeling, and off-target assessment to reduce downstream risk.
Support target-based hit discovery, hit-to-lead optimization, lead prioritization, and binding mode interpretation for small molecule drug design.
Guide warhead selection, linker design, ternary-complex hypothesis generation, and structural optimization for PROTAC linker design.
Evaluate peptide docking poses, binding interfaces, cyclization effects, conformational stability, and amino-acid modification strategies.
Support antibody-antigen interface interpretation, binding-site prediction, affinity improvement, and developability-related structural review.
Assess small molecule, peptide, or protein interactions with DNA/RNA targets using docking, interaction modeling, and structural analysis.
Connect target structure, mutation effects, ligand binding, and experimental observations to strengthen validation hypotheses and guide follow-up assays.
Goal: choose compounds for first-round assays from a docking hit list.
Goal: understand SAR and prioritize the next analogs to synthesize.
Goal: decide whether a predicted or modeled target structure is suitable for drug design.
No. Experimental structures are ideal, but many projects can start from AlphaFold models, homology models, or other predicted structures after model quality assessment and binding-site review. If the model is not suitable for direct docking, we will recommend refinement or an alternative strategy.
No. SBDD is commonly used for small molecules, but structure-guided workflows can also support peptides, macrocycles, PROTACs, protein binders, antibody interfaces, ADC components, and nucleic-acid-targeting molecules.
Yes. We can review docking setup, inspect poses, compare interaction patterns, run additional docking or MD validation, and help prioritize compounds for testing or redesign.
We combine score review with pose geometry, interaction quality, known SAR, pocket fit, chemical reasonability, target flexibility, MD simulation when needed, and property filters. The final recommendation is based on multiple evidence layers.
Yes. For lead selection or optimization, we can combine structure-based ranking with physicochemical property prediction, ADMET screening, and off-target risk assessment to support more balanced candidate prioritization.
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