DeepPurpose

DeepPurpose predicts drug–target interactions, adding a kinome-wide off-target read that a general ADMET panel does not cover. Reach for it when selectivity is the question — checking a compound against a panel of kinase targets during lead optimization, or profiling off-target liabilities before committing to a series. It is narrower than a broad ADMET screen: pair it with ADMET-AI for the fast safety panel. See what you can achieve for the full set.

DeepPurpose provides ready-to-use encoders for SMILES and protein sequences plus tools for drug–target, drug–drug, and side-effect predictions. Use these guides to stand up repeatable jobs for both research and production scoring.

Citations

Huang K, Fu T, Glass LM, Zitnik M, Xiao C, Sun J. DeepPurpose: a deep learning library for drug–target interaction prediction. Bioinformatics. 2021;36(22-23):5545-5547. doi: 10.1093/bioinformatics/btaa1005

Chat Usage

Example Prompts

• "Predict kinome-wide off-targets for sorafenib using DeepPurpose"
• "Run DeepPurpose DTI prediction for gefitinib against EGFR"
• "Screen my compound against a panel of kinase targets"

Workflow Chaining

• Feeds into: ADMET-AI (safety for predicted binders), Target Passport (off-target analysis)
• Accepts from: PubChem (compound SMILES), UniProt (target sequences), REINVENT (generated molecules)

FAQ

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