ADMET Predictor® Tutorial Series: Python Module 1

Authors: Bachorz RA
Keywords: 3D geometry, admet predictor, HTPK, Jupyter Notebook, metabolite generation, Pandas, PI-ADMET Predictor, Python, RDKit, REST API, SMILES standardization, t-SNE
Software: ADMET Predictor®
Division: Cheminformatics

Discover the synergy between ADMET Predictor® and the Python programming language. This session guides you through the installation of the PI-ADMET Predictor module and its dependencies like RDKit, Pandas, and Scikit-Learn. Key use cases include:

  • SMILES Standardization: Converting raw SMILES into cleaned, standardized forms using command-line scripts.
  • Property Prediction: Accessing ADMET properties asynchronously through the RESTful API.
  • Exploratory Data Analysis: Performing dimensionality reduction using t-SNE to visualize chemical space.
  • Pharmacokinetics (HTPK): Extracting and plotting CP time curves directly in Jupyter Notebooks.
  • 3D Geometry & Metabolites: Generating 3D molecular structures and visualizing metabolite trees within your Python scripts