Screen billions of compounds computationally to identify lead candidates with validated hit rates of 10-30%. Our deep learning platform combines physics-based docking with neural network scoring for superior prediction accuracy at unprecedented scale.
Traditional high-throughput screening requires physically testing millions of compounds, consuming time, resources, and precious samples. Our AI-powered virtual screening identifies the most promising candidates before any wet lab work begins.
Screen libraries of billions of compounds efficiently. Our distributed computing infrastructure handles ultra-large-scale virtual screening.
Our validated hit rates of 10-30% dramatically exceed random screening. Deep learning models trained on experimental data capture subtle binding patterns physics-based methods miss.
Focus experimental resources on top-ranked candidates. Virtual pre-filtering reduces wet lab screening costs by 90% while improving hit quality.
Receive ranked hit lists with direct purchasing links for commercially available compounds. Skip synthesis and proceed directly to experimental validation.
We combine physics-based molecular docking with deep learning scoring for accurate binding affinity prediction at scale.
AutoDock Vina, Gold, and Glide perform rigorous conformational sampling and energy scoring. Multi-precision workflows balance accuracy with throughput.
Graph neural networks and transformers trained on PDB binding data predict binding affinity and distinguish true binders from false positives with superior accuracy.
Combine multiple scoring functions for robust ranking. Apply drug-likeness, synthetic accessibility, and ADMET filters to prioritize viable lead compounds.
Our virtual screening platform supports various lead discovery and optimization scenarios.
Screen ultra-large libraries (millions to billions of compounds) against your target. Pre-built ZINC, Enamine, and ChemBridge libraries plus custom focused collections.
Improve initial hits through targeted analog screening, growing, and linking strategies. Predict SAR and guide synthesis priorities.
Evaluate off-target binding to predict selectivity profiles. Screen against anti-targets (hERG, CYP450s) to deprioritize compounds with liability risks.
Identify fragment hits from small libraries (thousands) that can be grown into potent leads. Ideal for targets with challenging binding sites.
Find compounds active against related targets based on binding site similarity. Leverage structural genomics data to identify cross-reactive scaffolds.
Confirm predicted binding modes through molecular dynamics simulation. Assess binding stability and identify key interaction pharmacophores.
We combine rigorous computational methods with experimental validation for reliable lead identification.
We analyze the binding site geometry, identify key pharmacophore features, and prepare protein structures from PDB or Enginoma Structure predictions with appropriate protonation states.
Compound libraries are curated for drug-likeness, availability, and structural diversity. Reactive compounds and PAINS are filtered out before virtual screening.
Multi-stage screening with increasingly rigorous methods. Fast pre-filtering followed by detailed docking and DL-based scoring of top candidates.
Multiple scoring functions are combined to generate robust rankings. Compounds consistently ranked highly across methods are prioritized for experimental testing.
Top-ranked compounds are selected based on binding pose quality, structural diversity, and commercial availability. Priority compounds are purchased or synthesized.
Selected compounds undergo biochemical testing. Results validate virtual screening accuracy and guide subsequent optimization rounds.
From target preparation to validated hits, our streamlined process delivers results efficiently.
Prepare protein structure, analyze binding site, and define screening parameters.
Screen compound libraries using multi-stage docking and DL scoring.
Consensus ranking, ADMET filtering, and diversity selection for the most promising candidates.
Biochemical testing of top-ranked compounds to validate virtual screening predictions.
Common questions about our virtual screening and molecular docking services.
We screen commercially available libraries (ZINC, Enamine, ChemBridge), custom focused libraries, and can generate de novo compounds. Our platform handles libraries from thousands to billions of compounds with equal efficiency.
We use multiple scoring functions and consensus scoring to improve prediction accuracy. Top-ranked compounds undergo retrospective validation against known actives when available, plus prospective validation through experimental testing of top 50-100 compounds.
We utilize PDB structures, Enginoma Structure predictions, and can generate homology models. Our platform handles apo structures, holo structures with bound ligands, and ensemble docking with multiple protein conformations.
Our validated hit rates range from 10-30% depending on target tractability and library quality. This significantly exceeds random screening (typically <1%) and is comparable to physical HTS.
Yes. We provide full virtual screening rankings with predicted binding scores, purchasing information for commercially available compounds, and synthetic feasibility assessments for novel compounds. Full reports include 2D structures, ADMET predictions, and patent considerations.
Our virtual screening services support drug discovery programs at universities, pharmaceutical companies, and biotech startups worldwide.
Our virtual screening platform is based on peer-reviewed computational methods and validation studies.
Zhang, X. et al. Efficient and accurate large library ligand docking with KarmaDock. Nat. Comput. Sci. 3, 789–804 (2023). https://doi.org/10.1038/s43588-023-00511-5
Yu, L. et al. Deep Learning with Geometry-Enhanced Molecular Representation for Augmentation of Large-Scale Docking-Based Virtual Screening. J. Chem. Inf. Model. 63, 6501–6514 (2023). https://doi.org/10.1021/acs.jcim.3c01371
Gentile, F. et al. Deep Docking: A Deep Learning Platform for Augmentation of Structure Based Drug Discovery. ACS Cent. Sci. 6, 939–949 (2020). https://doi.org/10.1021/acscentsci.0c00229
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