admet-prediction

What This Skill Does

The admet-prediction skill is a sophisticated drug discovery tool designed for the OpenClaw AI agent. It evaluates the Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) profile of chemical compounds. By leveraging advanced Machine Learning and Quantitative Structure-Activity Relationship (QSAR) models, the skill helps medicinal chemists and researchers identify potential drug candidates earlier in the R&D pipeline. It offers comprehensive insights into drug-likeness (Lipinski, Veber), metabolic stability, and safety risks, enabling teams to prioritize compounds with higher probability of clinical success while filtering out those likely to fail due to poor pharmacokinetics or off-target toxicity.

Installation

You can integrate this skill into your OpenClaw environment by executing the following command in your terminal: clawhub install openclaw/skills/skills/huifer/admet-prediction

Ensure that you have the necessary dependencies for chemical structure processing installed, as this skill interacts with structural data formats like SDF to process multiple compounds simultaneously.

Use Cases

1. Early Lead Optimization: Rapidly screen virtual libraries to ensure structural modifications do not introduce toxicity or poor membrane permeability.
2. Pre-clinical Safety Assessment: Identify potential hazards such as DILI (Drug-Induced Liver Injury) or hERG cardiac risks before investing in expensive in vitro experiments.
3. PK Property Estimation: Predict clearance, volume of distribution (VDss), and half-life to inform dosing schedules for animal study designs.
4. Drug-Likeness Filtering: Automatically prune large datasets based on established medicinal chemistry rules such as Lipinski's Rule of Five, Brenk, and PAINS alerts.

Example Prompts

1. "Analyze the ADMET profile for SMILES CC1=CC=C(C=C1)CNC and generate a full report on its potential safety risks."
2. "Perform a toxicity screen on all compounds in compounds.sdf. Focus on hERG, DILI, and Ames mutagenicity with a confidence threshold of 0.7."
3. "Predict the metabolic stability and CYP450 inhibition profile for CHEMBL210. Is it a significant drug-drug interaction risk?"

Tips & Limitations

While the predictions provided by the ADMET skill are highly valuable for prioritization, they are derived from ML/QSAR models and should not replace final experimental validation. Use the confidence metrics (High/Medium/Low) in the output to guide your level of certainty. For 'Low' confidence predictions, proceed with caution and prioritize experimental synthesis. Additionally, ensure the input chemical structures are properly sanitized before processing to avoid errors in property calculation.