Combine deep learning, physics-based docking, and ultra-large library enumeration to identify novel hits and scaffolds. CD ComputaBio integrates Structure-Based Virtual Screening with AI-driven scoring, Molecular Dynamics Simulation, and targeted wet-lab validation to accelerate early discovery. From billion-compound libraries to focused covalent screens, we deliver enriched, synthesizable candidates.
Combine pharmacophore modeling, molecular docking, and AI-based rescoring (e.g., CNN, transformer) to identify high-quality hits from ultra-large libraries.
Use generative models (VAE, RNN, diffusion) to design novel compounds with desired properties, then dock and rank them in silico.
We coordinate biochemical assays (SPR, FP, thermal shift) for top-ranked compounds to confirm hits and provide feedback for model retraining.
Leverage cloud-accelerated docking and hierarchical filtering to screen libraries exceeding 10&sup9; molecules with rapid turnaround.
Screen focused covalent libraries with warhead-aware docking and AI scoring to identify reversible or irreversible covalent hits.
Predict selectivity against kinomes, GPCRs, ion channels, and hERG using ensemble docking and AI selectivity models.
Train or fine-tune 3D-CNNs and graph neural networks on target-specific data to predict binding affinity and pose correctness.
Dock into multiple receptor conformations from MD or cryo-EM, followed by MM-GBSA and Binding Free Energy (MMGBSA) Analysis.
Build ultra-large virtual libraries using combinatorial enumeration, then filter by drug-likeness, PAINS, and synthetic feasibility.
Process protein structure (PDB, AlphaFold), identify allosteric/cryptic pockets, and prepare multiple receptor conformations.
Generate or curate virtual libraries (10⁶–10&sup9;) with desired property filters and synthetic accessibility constraints.
High-throughput docking followed by neural network rescoring, active learning, and consensus ranking.
MM-GBSA, FEP, and short MD simulations for top candidates to validate binding stability and estimate residence time.
Select 20–100 compounds for experimental testing; we coordinate with wet-lab partners for biochemical validation.
Incorporate assay feedback into AI models, deliver a comprehensive report with hits, scaffolds, and optimization guidance.
| Research Goal | Recommended AI/CADD Strategy | Key Readouts | Decision Supported |
|---|---|---|---|
| Find novel hits against a known target | Ultra-large library docking + AI rescoring | Enrichment factor, novel scaffolds, predicted Kd | Hit identification, scaffold diversity |
| Scaffold hopping from a known active | Generative model + similarity search | Novel chemotypes, synthetic accessibility | Patent circumvention, IP strategy |
| Improve selectivity over anti-targets | Ensemble docking + AI selectivity model | Selectivity scores, off-target binding poses | Lead optimization, selectivity panel |
| Screen covalent inhibitors | Docking with covalent constraints + AI scoring | Warhead orientation, reversible/irreversible ranking | Covalent hit discovery |
| Validate hits experimentally | Integrated wet-lab assays (SPR, FP, DSF) | IC50, Kd, thermal shift | Hit confirmation, model retraining |
A client needed novel antagonists for a class B GPCR. We docked 500 million compounds using a homology model, applied AI rescoring, and identified 3 novel scaffolds with IC50 < 200 nM after synthesis.
We screened a focused covalent library (10⁶) with covalent docking constraints and AI scoring, prioritizing warheads with optimal orientation. Two compounds showed irreversible binding in SPR.
A client's lead compound had off-target activity against hERG. We used ensemble docking and AI selectivity models to design analogs with reduced hERG binding while maintaining on-target potency.
We merge state-of-the-art AI with rigorous physics-based methods, providing a seamless path from ultra-large libraries to validated hits. Our integrated wet-lab options close the loop between prediction and experiment, ensuring your project advances with confidence.
Goal: Screen 1 billion compounds against a kinase target and deliver 50 candidates for assay.
Goal: Generate 1000 novel analogs around a hit scaffold with improved predicted solubility.
Goal: Test 30 AI-predicted compounds in a biochemical assay to confirm hits.
We routinely screen libraries from 10⁶ to over 10&sup9; compounds using cloud-based docking and AI rescoring. For ultra-large libraries, we use active learning and hierarchical filters.
Yes, we coordinate with trusted CROs or your preferred lab to run biochemical assays (SPR, FP, DSF, etc.) for top-ranked compounds, and feed the data back into our models.
We use a combination of 3D-CNNs, graph neural networks, transformer-based models, and gradient boosting for scoring and generative design. Models can be fine-tuned on your data.
Absolutely. We have workflows for covalent docking, warhead orientation analysis, and AI scoring tailored to covalent reversible and irreversible inhibitors.
A standard AI-assisted screening project (10⁸–10&sup9; compounds, including refinement and reporting) typically takes 3–5 weeks, depending on library size and complexity.
Submit your target and library specifications. Our team will design a tailored AI-assisted virtual screening campaign, including physics-based refinement and optional wet-lab validation.
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