CD ComputaBio provides Structure-based Virtual Screening (SBVS) Service to help pharmaceutical, biotechnology, and academic research teams identify high-potential compounds from large chemical libraries using target 3D structures. By integrating target preparation, molecular docking, compound library curation, binding-site analysis, rescoring, and ADMET filtering, our SBVS workflow helps clients reduce experimental screening cost and move faster toward testable hits.
We prepare target structures, evaluate model quality, define binding pockets, and generate docking-ready receptor files for screening.
We prepare screening libraries using property filters, structural alerts, duplicates removal, protonation-state generation, conformer preparation, and client-specific constraints.
We perform structure-based docking, apply screening funnels, inspect pose quality, and prioritize compounds using both scoring and structural criteria.
We explain top-ranked hits through pocket fit, hydrogen bonding, hydrophobic contacts, electrostatics, salt bridges, ligand strain, and subpocket occupancy.
For high-value hits, we can apply molecular dynamics, MM/GBSA, MM/PBSA, or other rescoring strategies to improve confidence before purchasing or synthesis.
We can add drug-likeness, physicochemical property, ADMET, toxicity, and off-target risk filters to make the final hit list more suitable for follow-up testing.
| Project Situation | Recommended Strategy | Best-Fit Input | Typical Output |
|---|---|---|---|
| Target 3D structure or reliable binding-site model is available | Structure-based Virtual Screening (SBVS) | PDB, AlphaFold model, homology model, cryo-EM structure, known active site | Ranked docked hits, binding modes, interaction maps, purchase/test shortlist |
| Known active ligands are available but target structure is weak or unavailable | Ligand-based Virtual Screening (LBVS) | Known actives, inactive compounds, fingerprints, similarity constraints, bioactivity data | Similarity-based and ML/QSAR-ranked candidate list |
| You need to identify potential targets of a known compound | Inverse Virtual Screening Service | Compound structure, disease context, target panel, pathway hypothesis | Predicted target list, docking evidence, off-target or repositioning hypotheses |
| You need early ligand-efficient starting points | Fragment-based Virtual Screening Service | Fragment library, pocket structure, binding-site constraints | Fragment hits, growth vectors, merge/link suggestions |
| You know key interaction features but need broader compound discovery | Pharmacophore-based Virtual Screening Service | Protein-ligand complex, known actives, pharmacophore features, SAR rules | Feature-matched hit list and docking/refinement recommendation |
| You need selectivity across several targets or isoforms | Multiple-target Virtual Screening Service | Target panel, isoforms, homologs, off-target structures, selectivity requirements | Multi-target ranking, selectivity matrix, counter-screening candidates |
We define whether the project aims to discover novel hits, repurpose compounds, prioritize analogs, find fragments, evaluate covalent candidates, or support target validation.
We prepare crystal structures, cryo-EM structures, AlphaFold models, or homology models; check missing residues, protonation states, cofactors, metals, waters, and binding-site feasibility.
We define the active site using known ligands, pocket prediction, conserved residues, mutagenesis data, substrate information, or structural alignment.
We curate compound libraries, remove unsuitable molecules, generate 3D conformers, assign protonation states, filter by drug-likeness or custom rules, and prepare compounds for high-throughput docking.
We run docking campaigns with a suitable protocol and generate ranked compound lists using docking scores, pose quality, binding-site constraints, and chemical diversity.
We cluster top hits, inspect representative poses, perform optional rescoring, and remove candidates with poor geometry, unstable interaction patterns, or undesirable properties.
We can add property, ADMET, toxicity, PAINS, and off-target filters to generate a balanced list of compounds for experimental testing.
We deliver ranked hits, binding modes, structural rationale, data files, risk notes, and recommended next steps for purchase, synthesis, assay validation, or lead optimization.
Suitable for identifying purchasable or synthesizable compounds with balanced molecular weight, polarity, hydrogen-bonding, and drug-likeness profiles.
Useful for research programs focused on bioactive natural products, unique scaffolds, or mechanism-inspired compound discovery.
Support fragment hit discovery, pocket exploration, scaffold hopping, and focused screening around known actives or structural hypotheses.
For targets with suitable nucleophilic residues, covalent drug virtual screening can evaluate warhead positioning and covalent docking feasibility.
For broad chemical-space exploration, we can design multi-stage filters to reduce millions of molecules into a manageable candidate shortlist.
Client-provided compounds can be prepared, standardized, screened, clustered, and prioritized while preserving project-specific constraints and confidentiality.
| Decision Point | SBVS Analysis Layer | Key Evidence | Recommended Next Step |
|---|---|---|---|
| Is the target ready for virtual screening? | Structure quality review, pocket analysis, ligandability assessment | Pocket geometry, model confidence, key residues, ligand-site consistency | Proceed to docking, refine target model, or generate an alternative receptor model |
| Which compounds should be tested first? | Docking score triage, pose inspection, chemical diversity selection | Rank, pose quality, interaction pattern, scaffold diversity, property profile | Purchase or synthesize a focused shortlist for experimental validation |
| Which hits have strong structural rationale? | Binding mode analysis and residue interaction mapping | Hydrogen bonds, hydrophobic contacts, electrostatics, conserved residue contacts | Prioritize compounds with interpretable and testable binding hypotheses |
| Which hits need further validation? | MD simulation, binding free-energy rescoring, clustering | Pose stability, interaction persistence, MM/GBSA or MM/PBSA trend | Run second-stage validation before purchase or synthesis |
| Which candidates are risky? | ADMET, off-target, PAINS, reactivity, synthetic feasibility filters | Property liabilities, toxicity flags, promiscuity risk, off-target hypotheses | Remove or deprioritize high-risk compounds from the testing set |
Screen drug-like or focused compound libraries against a prepared target structure to identify a testable hit shortlist for biochemical or cell-based assays.
Use natural product libraries to discover structurally diverse scaffolds with plausible binding modes for enzyme, receptor, viral protein, or disease-relevant targets.
Identify ligand-efficient fragments and growth vectors for small or underexplored pockets before fragment expansion or fragment-based drug design.
Screen analogs around known actives and combine docking with SAR interpretation to select compounds for synthesis or purchase.
Compare binding across wild-type, mutant, homologous, or isoform structures to support selectivity design and resistance-risk interpretation.
Combine SBVS with drug repositioning or inverse screening to generate new target or indication hypotheses for known compounds.
Structure-based virtual screening is most valuable when the screening funnel is built around a clear biological question and a realistic experimental follow-up plan. CD ComputaBio combines structural biology, docking, compound library design, molecular modeling, MD simulation, ADMET prediction, and drug discovery experience to help clients move from a target structure to a prioritized hit list.
Goal: identify candidate inhibitors for a known active-site enzyme.
Goal: find natural product scaffolds with plausible binding against a disease-related target.
Goal: improve confidence in 20–50 docked hits before purchase.
An experimental structure is preferred, but SBVS can also start from a carefully evaluated AlphaFold model, homology model, or other reliable target model. Before screening, we assess whether the binding site is suitable for docking and hit prioritization.
The screening scale depends on the target, library source, docking method, filtering strategy, and required turnaround. Projects may range from focused libraries of hundreds or thousands of compounds to large-scale libraries containing millions of molecules.
Yes. Client-provided libraries can be standardized, deduplicated, filtered, prepared for docking, screened, clustered, and prioritized according to project-specific rules.
No. Docking scores are only one evidence layer. We also consider pose quality, key residue contacts, chemical diversity, ligand strain, binding-site fit, known SAR, ADMET risk, and optional rescoring or MD validation.
Yes. The final shortlist is designed for experimental follow-up, such as compound purchase, biochemical assays, cell-based screening, SPR/BLI, enzymatic assays, or lead optimization studies.
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