Predict ADMET properties from SMILES using GraphMVP ensemble models.
npx skills add https://github.com/PharMolix/OpenBioMed --skill admet-prediction
Please help me install this Agent Skill. Skill: admet-prediction Source: https://github.com/PharMolix/OpenBioMed/tree/main/skills/admet-prediction Command: npx skills add https://github.com/PharMolix/OpenBioMed --skill admet-prediction
Predict comprehensive ADMET properties for drug candidate molecules using GraphMVP ensemble models to accelerate early-stage drug discovery.
Input a molecule's SMILES string and run the GraphMVP ADMET ensemble to obtain BBB penetration, SIDER probabilities, Caco-2 permeability, half-life, and LD50 estimates.
High-intent search queries and answers about installing and using this skill.
To predict ADMET properties, input a molecule's SMILES string and run the GraphMVP ensemble to output BBB penetration, SIDER side effects, Caco-2 permeability, half-life, and LD50 estimates.
During early-stage drug discovery, you can evaluate safety and pharmacokinetic risk profiles by predicting BBB penetration, SIDER side effects, Caco-2 permeability, half-life, and LD50 using GraphMVP ensemble models.
Yes, performing deterministic ADMET inference via the OpenBioMed pipeline requires SMILES input along with access to GraphMVP checkpoints and configuration YAMLs.
Yes, you can rapidly screen drug candidates for Caco-2 permeability and LD50 safety profiles before synthesis by running the GraphMVP ensemble ADMET predictors.
GraphMVP ADMET prediction is limited to deterministic inference via the OpenBioMed pipeline, requiring specific checkpoints and YAML configurations to output BBB, SIDER, Caco-2, half-life, and LD50 metrics.
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