Embedded Files
In our lab, we develop and use specific scripts and tools to analyze, organize, classify and synthesize virtual molecules.
In our lab, we develop and use specific scripts and tools to analyze, organize, classify and synthesize virtual molecules.
In the following sections, you will find specific tools designed for specific tasks and purposes that involve RDKit or other virtual chemical tools or code.
Note: You don't necessarily know how to code to use this programs. However, it is recommended that at least you know how to use virtual environments one way or the other; see the end of this webpage for more.
In silico amide coupling
In silico amide coupling
Brief description: This Python script utilizes the RDKit library to perform and manage chemical reactions between carboxylic acids and amines, resulting in amide products.
GitHub Link:
In silico Suzuki coupling
In silico Suzuki coupling
Brief description: This Python script utilizes the RDKit library to perform and manage chemical reactions between boronoic acids and ArX, resulting in C-C bond formation.
GitHub Link:
Molecules Slide Generator
Molecules Slide Generator
Brief description: This Python script leverages RDKit for chemical processing and Python-pptx for slide generation, enabling the creation of a PowerPoint presentation containing images of molecular structures from SMILES strings.
GitHub Link:
SUFEX In silico reaction
SUFEX In silico reaction
Brief description: The script then performs a specific chemical reaction between the sulfonyl fluorides and amines to generate sulfonamide products, utilizing a predefined SMARTS reaction pattern.
GitHub Link:
Molecules as text? SMILES
Molecules as text? SMILES
In cheminformatics, the codification of molecules using text strings is a fundamental practice that allows for the efficient representation, storage, and computational handling of chemical structures. Two of the most prevalent and powerful text string formats used in cheminformatics are the Simplified Molecular Input Line Entry System (SMILES) and the International Chemical Identifier (InChI). These formats allow researchers and software tools to easily and accurately exchange and manipulate molecular data.
In cheminformatics, the codification of molecules using text strings is a fundamental practice that allows for the efficient representation, storage, and computational handling of chemical structures. Two of the most prevalent and powerful text string formats used in cheminformatics are the Simplified Molecular Input Line Entry System (SMILES) and the International Chemical Identifier (InChI). These formats allow researchers and software tools to easily and accurately exchange and manipulate molecular data.
SMILES
SMILES
Atoms and Bonds: Atoms are represented by their elemental symbols (e.g., C for carbon, H for hydrogen). Bonds between atoms are depicted by symbols (- for a single bond, = for a double bond).
Branching: Branches in chemical structures are denoted by parentheses. For example, a methyl group attached to the main chain might be represented as -CH3 but would be placed in parentheses if branching off a ring or another chain.
Rings and Cycles: Ring structures are indicated by using numbers to show the connection point in the ring. For instance, cyclohexane might be represented as C1CCCCC1, with the '1' indicating the start and end of the ring.
Stereochemistry: The stereochemistry of molecules can be indicated in SMILES using symbols like / and \ to represent the geometric configuration around double bonds or chiral centers.
Little Introduction to Virtual Environments: Python
Little Introduction to Virtual Environments: Python
Virtual environments will help you to operate our developed python scripts. You can either opt to set up your own environments or use jupyter notebooks, depending on what you are more comfortable.
Virtual environments will help you to operate our developed python scripts. You can either opt to set up your own environments or use jupyter notebooks, depending on what you are more comfortable.
Why Use Virtual Environments?
Why Use Virtual Environments?
Dependency Management: Each project can have its own dependencies, or even different versions of the same library, without affecting other projects.
Consistency: Ensures that all project collaborators have a consistent development environment.
Simplifies Deployment: Reduces the chances of encountering bugs and inconsistencies when moving projects between development and production environments.
Here, we are not going to show how to set up virtual environments, however, several free resources are encouraged before starting:
Anaconda for Beginners Guide and Virtual Environments Introduction: https://www.youtube.com/watch?v=MUZtVEDKXsk
HitchHicker's guide to Python, pipenv and Virtual environments: https://docs.python-guide.org/dev/virtualenvs/
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