CDPL Python Tutorial

Note

Please install CDPKit before proceeding with the tutorial. Detailed installation instructions can be found in section Installing CDPKit.

Welcome to the “Getting Started with Python” tutorial! If you’re familiar with cheminformatics libraries like RDKit, you’ll find that CDPKit offers a unique set of tools and a feature-rich software development library that can take your computational drug design projects to new heights.

The Chemical Data Processing Library (CDPL) serves as the basis for CDPKit’s own applications and provides powerful basic functionality for the development of advanced drug discovery applications. Whether you’re a researcher, a student, or an industry professional, this tutorial aims to introduce the basic concepts and functionalities of CDPKit, enabling you to harness its full potential.

In the subsequent sections, we’ll walk you through the foundational concepts and dive into practical applications with hands-on examples. By the end, you’ll be well-equipped to incorporate CDPKit into your workflow and start crafting effective drug design solutions using Python.

Tip

For a detailed description of a particular function, method or class please visit the CDPL Python API Documentation pages. There is also a a powerful search tool on the top right corner of the page that you can use to find particular functions or modules within the documentation.

So, grab a coffee (or tea) and let’s embark on this exciting journey into the realm of computational drug design with CDPKit!

• 1. Molecular Input/Output
  • 1.1. Reading molecules from an input file
  • 1.2. Retrieving structure data from MDL SD-files
  • 1.3. Writing molecules to an output file
  • 1.4. Description of structural atom environments as SMILES strings
  • 1.5. ChEMBL molecule standardization and parent structure extraction
  • 1.6. Standardization/prediction of protonation states
• 2. 3D Structure and Conformer Ensemble Generation
  • 2.1. Generating single low-energy 3D structures
  • 2.2. Generating conformer ensembles
• 3. Pharmacophore Generation and Processing
  • 3.1. Pharmacophore generation for single-conformer molecules
  • 3.2. Pharmacophore generation for multi-conformer molecules
  • 3.3. Generating ligand-receptor interaction pharmacophores
  • 3.4. Alignment of molecules to a reference pharmacophore
  • 3.5. Retrieving information about pharmacophores and features
• 4. Calculation of Atom Properties
  • 4.1. Calculation of atom classification properties
  • 4.2. Calculation of connectivity properties
• 5. Calculation of Molecular Structure Descriptors
  • 5.1. Extended connectivity fingerprints (ECFPs)
  • 5.2. FAME atom environment fingerprints
• 6. Force Field Calculations
  • 6.1. Calculation of MMFF94 atom charges
• 7. Substructure Searching and Matching
  • 7.1. Filtering molecules based on a SMARTS pattern