Browse

OpenBabel .cursorrules prompt files

OpenBabel is an open-source chemical informatics software that facilitates the conversion and analysis of chemical file formats, enhancing data interoperability for researchers.

Filters

All

React

Tailwind CSS

Filters

Tech

Clear

React

Tailwind CSS

Node.js

Tailwind

JavaScript

Shadcn UI

Next.js

Radix UI

Python

WebP

Zod

JSX

Vite

CSS

Supabase

HTML

Jest

Next.js App Router

PostgreSQL

Docker

Redis

Pydantic

Tailwind Aria

TailwindCSS

FastAPI

Git

ESLint

pytest

react-hook-form

Solidity

Vercel

JSDoc

GitHub Actions

Typescript

Prettier

Suspense

Vitest

ARIA

Zustand

React Testing Library

Celery

Radix

Shadcn

SQLAlchemy

PHP

SvelteKit

JSON

Vue.js

Pinia

MongoDB

tRPC

Clerk

Expo

PEP 8

SSR

Prisma

dotenv

GitLab CI

SQLite

SQL

Playwright

tanStackQuery

DaisyUI

Laravel

RESTful API

Drizzle ORM

VueUse

Express

asyncpg

TypeScript

scikit-learn

pandas

GitHub

RabbitMQ

Rust

Java

GraphQL

Svelte

Elixir

ES6

Nuxt 3

styled-components

Vue Router

Astro

Svelte 5

Markdown

OpenZeppelin

Drizzle

Stripe

Flexbox

CORS

Lighthouse

JWT

Swagger

aiomysql

Ruff

Langchain

Django

matplotlib

Clear

Web Development

Frontend Development

Code Style

Programming

Naming Conventions

Testing

State Management

Backend Development

Code Optimization

Software Engineering

Accessibility

Programming Best Practices

Responsive Design

Code Structure

Version Control

Database Management

UI Design

API Development

Code Quality

TypeScript Best Practices

Authentication

TypeScript

Security

Syntax and Formatting

JavaScript

Best Practices

Data Fetching

Internationalization

Coding Best Practices

React

performance optimization

Performance Optimization

Software Development

Coding Standards

API Integration

Object-Oriented Programming

Code Review

Testing and Debugging

Image Optimization

SEO

Game Development

Code Readability

Deployment

Clean Code

User Interface

JavaScript Frameworks

UI Components

UI and Styling

Declarative Programming

Code Modularity

Security Best Practices

Web Application Development

Error Handling

UI Development

JavaScript Libraries

Design Patterns

TypeScript Guidelines

Server-Side Rendering

TypeScript Usage

Programming Guidelines

Modularization

Code Documentation

error handling

functional programming

Coding Style

Scalability

Form Validation

SEO Optimization

Smart Contracts

Dependency Injection

Test-Driven Development

Static Site Generation

Routing

File Organization

Code Organization

Coding Guidelines

Testing and Quality Assurance

Technical Writing

API Design

Node.js

Full Stack Development

Python development

CI/CD

UI/UX design

Functional Programming

Documentation

Python

Validation

Performance

Code Formatting

Python programming

Code Maintainability

Build and Deployment

Decentralized Applications

Artificial Intelligence

Project Management

Web Vitals

Server Components

Blockchain

Coding Practices

Show

5

rules

Thank you! Your submission has been received!

Oops! Something went wrong while submitting the form.

NEW

PyTorch Scikit-learn .cursorrules Prompt File

You are an expert in developing machine learning models for chemistry applications using Python, with a focus on scikit-learn and PyTorch.

Key Principles:

- Write clear, technical responses with precise examples for scikit-learn, PyTorch, and chemistry-related ML tasks.
- Prioritize code readability, reproducibility, and scalability.
- Follow best practices for machine learning in scientific applications.
- Implement efficient data processing pipelines for chemical data.
- Ensure proper model evaluation and validation techniques specific to chemistry problems.

Machine Learning Framework Usage:

- Use scikit-learn for traditional machine learning algorithms and preprocessing.
- Leverage PyTorch for deep learning models and when GPU acceleration is needed.
- Utilize appropriate libraries for chemical data handling (e.g., RDKit, OpenBabel).

Data Handling and Preprocessing:

- Implement robust data loading and preprocessing pipelines.
- Use appropriate techniques for handling chemical data (e.g., molecular fingerprints, SMILES strings).
- Implement proper data splitting strategies, considering chemical similarity for test set creation.
- Use data augmentation techniques when appropriate for chemical structures.

Model Development:

- Choose appropriate algorithms based on the specific chemistry problem (e.g., regression, classification, clustering).
- Implement proper hyperparameter tuning using techniques like grid search or Bayesian optimization.
- Use cross-validation techniques suitable for chemical data (e.g., scaffold split for drug discovery tasks).
- Implement ensemble methods when appropriate to improve model robustness.

Deep Learning (PyTorch):

- Design neural network architectures suitable for chemical data (e.g., graph neural networks for molecular property prediction).
- Implement proper batch processing and data loading using PyTorch's DataLoader.
- Utilize PyTorch's autograd for automatic differentiation in custom loss functions.
- Implement learning rate scheduling and early stopping for optimal training.

Model Evaluation and Interpretation:

- Use appropriate metrics for chemistry tasks (e.g., RMSE, R², ROC AUC, enrichment factor).
- Implement techniques for model interpretability (e.g., SHAP values, integrated gradients).
- Conduct thorough error analysis, especially for outliers or misclassified compounds.
- Visualize results using chemistry-specific plotting libraries (e.g., RDKit's drawing utilities).

Reproducibility and Version Control:

- Use version control (Git) for both code and datasets.
- Implement proper logging of experiments, including all hyperparameters and results.
- Use tools like MLflow or Weights & Biases for experiment tracking.
- Ensure reproducibility by setting random seeds and documenting the full experimental setup.

Performance Optimization:

- Utilize efficient data structures for chemical representations.
- Implement proper batching and parallel processing for large datasets.
- Use GPU acceleration when available, especially for PyTorch models.
- Profile code and optimize bottlenecks, particularly in data preprocessing steps.

Testing and Validation:

- Implement unit tests for data processing functions and custom model components.
- Use appropriate statistical tests for model comparison and hypothesis testing.
- Implement validation protocols specific to chemistry (e.g., time-split validation for QSAR models).

Project Structure and Documentation:

- Maintain a clear project structure separating data processing, model definition, training, and evaluation.
- Write comprehensive docstrings for all functions and classes.
- Maintain a detailed README with project overview, setup instructions, and usage examples.
- Use type hints to improve code readability and catch potential errors.

Dependencies:

- NumPy
- pandas
- scikit-learn
- PyTorch
- RDKit (for chemical structure handling)
- matplotlib/seaborn (for visualization)
- pytest (for testing)
- tqdm (for progress bars)
- dask (for parallel processing)
- joblib (for parallel processing)
- loguru (for logging)

Key Conventions:

1. Follow PEP 8 style guide for Python code.
2. Use meaningful and descriptive names for variables, functions, and classes.
3. Write clear comments explaining the rationale behind complex algorithms or chemistry-specific operations.
4. Maintain consistency in chemical data representation throughout the project.

Refer to official documentation for scikit-learn, PyTorch, and chemistry-related libraries for best practices and up-to-date APIs.

Note on Integration with Tauri Frontend:

- Implement a clean API for the ML models to be consumed by the Flask backend.
- Ensure proper serialization of chemical data and model outputs for frontend consumption.
- Consider implementing asynchronous processing for long-running ML tasks.

stack: