High-Throughput Virtual Drug Screening Service
Our high-throughput virtual drug screening service integrates AI-assisted virtual screening, structure-based docking, and binding energy analysis to accelerate the discovery of novel drug candidates. By simulating interactions between large chemical libraries and protein targets in silico, this service helps researchers identify high-affinity binders, predict selectivity, and prioritize compounds before experimental validation—saving both time and cost in the early stages of drug discovery.
Key Features
- AI-Driven Screening Platform
Combines deep-learning predictive models with molecular docking for accurate ranking of ligand–target interactions.
- Pocket-Focused Target Analysis
Automatically identifies and characterizes active and allosteric binding sites for targeted screening.
- Massive Parallel Screening Capability
GPU-accelerated infrastructure supports virtual screening of 10⁴–10⁶ compounds across multiple protein targets simultaneously.
- Multi-Layer Scoring System
Integrates docking scores, MM/GBSA free energy estimations, and machine learning–based affinity predictions for hit refinement.
- Off-Target and Selectivity Profiling
Evaluate compound specificity across protein families to minimize adverse interactions and optimize safety profiles.
- Customizable Library Integration
Compatible with commercial, in-house, or custom-designed compound libraries.
Main Virtual Screening Approaches
Virtual drug screening (VDS) is a computational approach to identify potential bioactive compounds by simulating molecular interactions between targets (proteins, enzymes, receptors) and ligands (small molecules, peptides). It significantly reduces time and cost in early drug discovery by focusing only on the most promising candidates for experimental validation.
| Category |
Description |
Representative Tools |
Typical Use |
| Structure-Based Virtual Screening (SBVS) |
Uses the 3D structure of target proteins (from X-ray, Cryo-EM, or AlphaFold2) to predict ligand binding via docking simulations. |
AutoDock, AutoDock Vina, Glide, GOLD, DOCK, LeDock |
Hit discovery, lead optimization, binding mode analysis |
| Ligand-Based Virtual Screening (LBVS) |
Identifies potential ligands by comparing to known active compounds using chemical similarity, pharmacophore, or machine learning models. |
ROCS, Pharmit, OpenEye, DeepChem, LigandScout |
When protein structure is unavailable; scaffold hopping |
| AI-Driven Screening / Deep Learning Models |
Predicts bioactivity and binding affinity using neural networks trained on large protein–ligand datasets (e.g., PDBbind, ChEMBL). |
DeepDock, GraphDTA, ChemProp, GNINA, AlphaBind |
High-speed hit prioritization, binding affinity prediction |
| Fragment-Based Virtual Screening (FBVS) |
Screens fragment libraries for small molecular building blocks that bind weakly but selectively to the target. |
Schrodinger FEP+, MOE, SeeSAR |
Early-stage discovery; structure-guided fragment linking |
| Pharmacophore Modeling |
Uses 3D spatial arrangements of functional groups essential for binding to model or predict ligands. |
LigandScout, Phase, Discovery Studio |
When limited known ligands are available |
| Molecular Dynamics–Enhanced Screening |
Combines docking and MD simulations to refine ligand binding stability and compute free energies. |
AMBER, GROMACS, NAMD, BioEmu, Desmond |
Post-screening refinement, energy validation |
Workflow
- 1Target Preparation & Pocket Identification
Model and refine target protein structures (experimental or AlphaFold2-based).
- 2Compound Library Processing
Format and optimize ligands for docking simulation.
- 3High-Throughput Virtual Screening
Execute large-scale docking simulations with automated scoring and clustering.
- 4Binding Energy & Interaction Analysis
Compute affinity values and map key molecular interactions.
- 5Hit Identification & Prioritization
Rank top-performing compounds for further optimization or synthesis.
Deliverables
- Ranked list of candidate compounds with docking and affinity scores
- 3D binding poses and pocket visualizations
- Interaction maps and binding energy reports
- Statistical overview of screening results
- Optional integration with MD simulation or lead optimization
Applications
- Early-stage hit identification
- Lead compound discovery and optimization
- Allosteric modulator and fragment-based screening
- Drug repurposing and polypharmacology analysis
- Predictive screening for novel therapeutic targets
Advantages
- Fast & Scalable: Screen millions of compounds in days
- Cost-Effective: Reduce experimental screening costs by >80%
- Accurate: Enhanced by AI re-scoring and energy refinement
- Flexible: Compatible with diverse targets and compound types
- Actionable: Provides ready-to-use hit lists and structural insights
Why Choose Us?
With a robust combination of AI modeling, molecular docking, and computational chemistry, our high-throughput virtual screening service delivers rapid, data-driven insights to accelerate your discovery pipeline. From early hit discovery to advanced lead refinement, we help transform virtual predictions into real-world therapeutic candidates.
* For Research Use Only.
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