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ADMET: A Machine Learning Platform for ADMET Evaluation
ADMET-AI, a machine learning platform designed to accelerate drug discovery by prioritizing drug candidates based on their ADMET properties. Predicting a molecule's Absorption, Distribution, Metabolism, Excretion, and Toxicity is a critical step in finding drug molecules that are both safe and effective. ADMET-AI is specifically a machine learning platform for the evaluation of large-scale chemical libraries.
How ADMET Works
ADMET-AI leverages sophisticated machine learning models to transform the time-consuming process of candidate screening into an efficient in silico prioritization step.
Machine Learning Prediction: The platform uses its integrated machine learning models to predict a wide range of key ADMET properties.
Large-Scale Capability: The tool is explicitly designed to handle and evaluate large-scale chemical libraries, enabling high-throughput screening.
Prioritization: By quickly and accurately assigning a predicted ADMET profile to each molecule, the platform allows researchers to prioritize only the most promising candidates for expensive and labor-intensive experimental validation.
What is Tamarind Bio?
Tamarind Bio is a pioneering no-code bioinformatics platform built to democratize access to powerful computational tools for life scientists and researchers. Recognizing that many cutting-edge machine learning models are often difficult to deploy and use, Tamarind provides an intuitive, web-based environment that completely abstracts away the complexities of high-performance computing, software dependencies, and command-line interfaces.
The platform is designed provide easy access to biologists, chemists, and other researchers who may not have a background in programming or cloud infrastructure but want to run experimental models with their data. Key features include a user-friendly graphical interface for setting up and launching experiments, a robust API for integration into existing research pipelines, and an automated system for managing and scaling computational resources. By handling the technical heavy lifting, Tamarind empowers researchers to concentrate on their scientific questions and accelerate the pace of discovery.
Accelerating Discovery with ADMET on Tamarind Bio
Using ADMET-AI on a platform like Tamarind could drastically accelerate structure-based drug discovery by providing an efficient and automated prioritization workflow.
High-Throughput Screening: The platform can seamlessly integrate ADMET-AI into virtual screening workflows to filter millions of novel or generated molecules based on their predicted safety and efficacy profiles.
Rational Candidate Selection: By accurately predicting the Absorption, Distribution, Metabolism, Excretion, and Toxicity of candidates, the platform ensures that experimental resources are focused only on molecules with the best overall therapeutic potential.
Streamlined Workflow: Tamarind.bio would handle the computational complexity of running a large-scale machine learning model, providing a single, comprehensive report of ADMET scores that allows researchers to make rapid, data-driven decisions.
How to Use ADMET on Tamarind Bio
To leverage ADMET-AI's power, a researcher could follow this streamlined workflow on Tamarind:
Access the Platform: Begin by logging in to the tamarind.bio website.
Select ADMET: From the list of available computational models, choose the ADMET tool.
Input Chemical Structures: Provide the structures of your candidate molecules (e.g., as SMILES strings).
Run ADMET-AI: The platform runs ADMET-AI to predict the full panel of ADMET properties for all input molecules.
Prioritize Candidates: The output provides scores and predictions for multiple properties, allowing researchers to filter and rank drug candidates to identify those with the most favorable overall ADMET profile for further development.