CD ComputaBio provides Natural Product Virtual Screening to accelerate the discovery of bioactive compounds from complex natural sources. We combine large-scale compound library preparation, target fishing, pharmacophore-based screening, molecular docking, and ADMET prediction to prioritize hits with genuine therapeutic potential. Our workflows are tailored to the unique chemical diversity and challenging scaffolds of natural products, and we support seamless integration with wet-lab validation for confirmed actives.
We build clean, ready-to-screen libraries from your compound lists, natural product databases, or predicted metabolite sets, with standardization of tautomers, stereochemistry, and protonation states.
When a target structure is available, we dock your natural product library against the binding site, apply consensus scoring, and select top candidates for experimental testing.
When no target structure is available, we use known actives to build pharmacophore models or shape queries and screen natural product libraries for matching scaffolds.
For bioactive natural products with unknown mechanisms, we perform reverse docking against target libraries to predict potential protein partners and propose a mechanism of action.
Natural products often have poor pharmacokinetic profiles. We predict absorption, distribution, metabolism, excretion, and toxicity to flag compounds with developability issues early.
We help translate computational hits into experimental tests by suggesting appropriate assays, providing compound prioritization, and analyzing preliminary wet-lab results to refine the next screening round.
We use flexible-ligand docking protocols optimized for natural product scaffolds, with scoring functions calibrated for the chemical space of natural compounds.
Ligand-based pharmacophore models capture the essential interaction features of known actives and enable fast screening of large natural product libraries.
Top docking poses are refined with molecular dynamics simulations to assess binding stability, account for receptor flexibility, and eliminate false positives.
We apply machine learning models trained on natural product bioactivity data to predict activity, toxicity, and ADMET properties, improving hit selection beyond docking scores alone.
For targets with flexible binding sites, we use ensemble docking across multiple receptor conformations or induced-fit protocols to better capture natural product binding modes.
Some natural products act through covalent or allosteric mechanisms. We offer specialized screening protocols to identify covalent binders and allosteric modulators.
We clarify your target, natural product source, and screening goals, then assess the size and chemical diversity of your compound collection to select the most appropriate screening strategy.
Your compounds are curated, standardized, and filtered for pan-assay interference compounds, reactive groups, and other liabilities. Descriptors and ADMET properties are calculated for the full library.
If a target structure is available, we prepare the binding site, define key interactions, and set up docking grids. If not, we build a pharmacophore or shape model from known actives.
We screen the natural product library using molecular docking, pharmacophore matching, or a combination of both, generating binding poses and scores for every compound.
Top-ranked compounds are rescored with more accurate methods, filtered by ADMET and drug-likeness criteria, and clustered by scaffold to ensure structural diversity in the final hit list.
Selected hits undergo molecular dynamics simulation to validate binding stability, identify key interactions, and calculate binding free energies for final ranking.
We deliver a ranked hit list with predicted binding modes, ADMET profiles, and suggested experimental assays. Our team can also analyze preliminary wet-lab results to refine the next screening round.
| Project Scenario | Recommended Strategy | Key Outputs | Wet-Lab Integration |
|---|---|---|---|
| Known target, natural product library to screen | Structure-based virtual screening with molecular docking | Ranked hit list, binding poses, interaction fingerprints | Assay recommendations for top hits |
| Known target, no structure available | Pharmacophore or shape-based screening | Pharmacophore-matched compounds, scaffold clusters | Compound prioritization for testing |
| Bioactive natural product, unknown target | Reverse docking and target fishing | Ranked target hypotheses, binding mode predictions | Suggested target validation experiments |
| Natural product with poor PK properties | ADMET prediction and lead-likeness filtering | ADMET profiles, liability alerts, optimization suggestions | Guidance on structural modification |
| Large natural product library with diverse scaffolds | Hierarchical screening: pharmacophore pre-filter + docking | Diverse hit set, scaffold analysis, predicted binding modes | Prioritized compounds for isolation or purchase |
| Need to identify novel scaffolds for a target | Scaffold hopping and shape-based screening | Novel chemotypes, patentable scaffolds, binding hypotheses | Medicinal chemistry starting points |
Screen plant, marine, or microbial natural product libraries against therapeutic targets to identify novel lead compounds with diverse scaffolds.
For natural products with observed bioactivity but unknown mechanism, reverse docking and target fishing predict potential protein targets.
Screen known natural products against new targets to uncover repurposing opportunities and expand the therapeutic utility of existing compounds.
Identify natural compounds with antioxidant, anti-aging, or skin-repair activities by screening against relevant targets and pathways.
Discover natural compounds with pesticidal or herbicidal activity by screening against insect, fungal, or plant targets.
Find natural product-derived chemical probes to study biological pathways and validate new drug targets.
Natural products present unique challenges for virtual screening: complex stereochemistry, large flexible scaffolds, and frequent PAINS-like behavior. Generic docking workflows often fail to prioritize the right compounds. CD ComputaBio builds screening strategies specifically for natural product chemistry, combining multiple computational methods with wet-lab-aware hit selection.
We can screen any compound collection you provide, including in-house natural product libraries, commercially available natural product databases, predicted metabolite sets, and virtual libraries generated through combinatorial biosynthesis or synthetic biology approaches. We also work with extract-level information to help prioritize which fractions or compounds to isolate.
Natural products often have complex ring systems, multiple stereocenters, and high flexibility. We address this by using expanded conformational sampling, strain energy correction, and scoring functions that account for the unique interaction profiles of natural scaffolds. We also apply PAINS filters and promiscuity alerts to reduce false positives.
Yes. Our reverse docking and target fishing workflow screens your compound against a curated panel of protein targets, ranks potential binding partners, and provides binding mode hypotheses. This is particularly useful for natural products with observed phenotypic activity but unknown mechanism of action.
We provide assay recommendations for top-ranked hits, suggest appropriate concentrations and controls, and can analyze preliminary experimental results to refine the next screening round. For clients who need experimental validation, we can coordinate with our partner wet-lab facilities or work with your existing assay team.
Timelines depend on library size and screening complexity. A standard structure-based screening of a few hundred to a few thousand compounds typically takes 2–4 weeks, including library preparation, docking, post-screening analysis, and report generation. Reverse docking and MD-refined projects may take 4–8 weeks.
Yes. We offer specialized covalent docking protocols for natural products with reactive groups and allosteric site detection for compounds that may act outside the orthosteric pocket. These require additional target analysis but can uncover mechanisms missed by standard screening.
Share your natural product library, target information, or bioactive compound data. Our team will recommend a tailored virtual screening workflow and deliver an actionable hit list to accelerate your discovery pipeline.
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