py3dmol

Jupyter Dock is a set of Jupyter Notebooks for performing molecular docking protocols interactively, as well as visualizing, converting file formats and analyzing the results.

X-Mol aims to provide the platform to explore the world of chemistry 🌎. It helps to understand the chemical structure in a better manner by interacting with the 3D chemical structure and also the nomenclature from SMILES 😊. The application also focuses on teaching the rules, examples, resources, test and certification to make base more strong 🏆

Interactive 3D Protein Modeler & Ligand Binding Site Predictor. Built with Streamlit, py3Dmol, and RCSB API.

🔬 BioSynth is an innovative tool designed to simulate and visualize protein synthesis from DNA sequences. It integrates modern bioinformatics methods to provide a detailed understanding of the relationship between DNA sequences and their corresponding protein structures.

Production-grade Dockerfiles and container configurations for multi-omics analysis (Scanpy+Seurat, GATK4+VEP, QIIME2+DADA2, Py3Dmol+RDKit).

A modular Streamlit UI kit and component library for building scientific web applications with Plotly charts, py3Dmol 3D viewers, and genomic file parsers.

🧬 Lectures for course ML-protein-design

Cheminformatics screening tool for early-stage drug discovery. Computes Lipinski's Rule of Five descriptors — molecular weight, LogP, H-bond donors and acceptors — from SMILES input via RDKit, classifying compounds by oral bioavailability potential across interactive Plotly dashboards and a py3Dmol 3D structure inspector.

An Enterprise-grade, Multi-Agent IDE for Biological Research. Built with Actor-Critic LLM Validation, Local Vector Database (RAG), dynamic Python Execution (REPL), and native 3D Protein Rendering (py3Dmol). Inspired by Anthropic's Claude Science. 🧬