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S5
AI Drug Virtual Screening
Target -> binding pocket -> molecular docking -> candidate molecule
TorchANI neural network potential accuracy approaches DFT. End-to-end virtual screening pipeline.
4-8 weeks300K-1M/projectCapability Coverage 70%
Pipeline Flow
End-to-end dry-computing loop
1
Target Structure
ESMFold prediction or PDB retrieval
2
Binding Pocket
P2Rank predicts druggable binding sites
3
Molecular Docking
AutoDock Vina large-scale virtual docking
4
ADMET Prediction
RDKit + QED evaluate metabolism and toxicity
5
Candidate Ranking
REINVENT AI generation + screening
6
Neural Network Potential Validation
TorchANI accuracy approaches DFT
7
Delivery Report
Candidate molecule SMILES + docking conformation + ADMET scores
Core Tools
ESMFoldP2RankAutoDock VinaRDKitQEDREINVENTTorchANI
Core Advantages
TorchANI
Accuracy approaches DFT but 1000x faster
End-to-end Automation
Target -> pocket -> docking -> ADMET -> ranking
AI Generation + Screening
REINVENT generates novel candidate molecules
Capability Coverage
70%Start Your Project AI Drug Virtual Screening
Start with just 1 project, no team needed.