/tools

DeepChem provides an open-source toolchain that facilitates the application of deep learning in drug discovery, quantum chemistry, and biology. It supports various molecular tasks such as property prediction, molecular generation, and offers extensive tutorials for users to learn and apply these techniques.

This repository provides a collection of Jupyter notebooks designed to teach practical cheminformatics using open-source software. It covers various topics including molecular property prediction, generative molecular design, and machine learning models applicable to cheminformatics workflows.

TeachOpenCADD is a teaching platform designed to educate users on computer-aided drug design (CADD) through interactive Jupyter Notebooks. It covers various topics in cheminformatics and structural bioinformatics, providing practical examples and resources for students and researchers in the field.

PyRosetta.notebooks offers Jupyter Notebooks that serve as a learning resource for the PyRosetta platform, which is used for biomolecular structure prediction and design. The repository includes tutorials on protein folding, docking, and design, making it a valuable tool for researchers in computational biology and chemistry.

This repository contains demonstrations of quantum machine learning and quantum chemistry applications using the PennyLane library. It provides tutorials and implementations that showcase various techniques in quantum computing relevant to molecular research.

DL4Proteins provides a series of Jupyter notebooks that teach deep learning methodologies for predicting and designing biomolecular structures, particularly proteins. It covers advanced topics such as AlphaFold and graph neural networks, making it a valuable resource for researchers in the field of protein engineering.

This repository contains tutorials for beginners on how to use LAMMPS, a molecular dynamics simulation tool. The tutorials cover various aspects of running simulations, including calculating energy structures and visualizing results.

Psi4NumPy combines the Psi4 electronic structure package with NumPy to create an interactive environment for quantum chemistry education and development. It includes reference implementations of quantum chemical methods and tutorials for learning how to program these methods.

The RDKit Tutorials repository offers a collection of tutorials designed to help users learn how to effectively utilize the RDKit toolkit for various molecular tasks. It covers practical applications in cheminformatics, including molecular property predictions and manipulations.

This repository contains a series of Google Colab tutorials focused on structural bioinformatics, covering topics such as protein folding, molecular dynamics simulations, and molecular docking. It serves as an educational resource for learning about molecular modeling and simulation techniques.

This repository provides a hands-on tutorial on using graph neural networks (GNNs) for various applications in life sciences, including predicting properties of small and macro-molecules, and drug discovery. It includes practical sessions on training GNN models for molecular property prediction and binding affinity prediction for protein-ligand pairs.

This repository contains resources and code for teaching cheminformatics, covering topics such as molecular dynamics, machine learning applications, and various cheminformatics techniques. It serves as a comprehensive educational tool for students and educators in the field of computational chemistry.

This repository offers educational materials for a course on drug discovery computing techniques, covering various topics such as molecular dynamics, docking, and solubility. It aims to provide both course-specific resources and broader educational content for the community.

GNNs For Chemists provides implementations of various graph neural networks tailored for chemical problems. It serves as an educational resource, guiding users through the process of representing molecules as graphs and building models for predicting molecular properties.

This repository provides a collection of Jupyter Notebooks aimed at teaching life science students the fundamentals of deep learning, with a focus on applications in cheminformatics. It includes notebooks on generative models for molecular design and datasets relevant to molecular machine learning.

The Flex ddG Tutorial provides a framework for modeling and predicting changes in binding free energies of proteins upon mutation using the Rosetta software. It includes example scripts for running the protocol and analyzing results, making it a valuable resource for researchers in computational biology and molecular design.

This repository contains tutorials for using LAMMPS, a molecular dynamics simulation software. It offers step-by-step guides for beginners and advanced users to perform various molecular simulations, including studies on polymers, carbon nanotubes, and electrolyte systems.

This repository provides a collection of interactive Jupyter notebooks focused on quantum mechanics and computational materials science. It includes tutorials on molecular dynamics and other related concepts, making it a useful educational tool for understanding molecular behavior and simulations.

AI4Science101 is an initiative aimed at educating researchers about the application of AI in scientific fields, with a specific focus on molecular dynamics. It offers a series of tutorials and a knowledge base to bridge the gap between AI and scientific discovery.

This repository provides tutorials and code for training neural network potentials to simulate protein thermodynamics. It aims to improve the efficiency and accuracy of traditional methods used in molecular simulations.

The 'pyemma_tutorials' repository provides a comprehensive tutorial on using the PyEMMA software for analyzing molecular dynamics simulations. It includes various notebooks that cover topics such as data I/O, MSM estimation, and analysis, making it a valuable resource for those interested in molecular dynamics and Markov state modeling.

DFT_PIB_Code is a collection of interactive Jupyter Notebooks designed to teach the fundamentals of Density-Functional Theory (DFT) through visualizations and practical examples. It allows users to explore molecular properties and behaviors using DFT, making it a valuable educational resource in computational chemistry.

This repository contains OPLS-AA force field parameters for ionic liquids, enabling accurate molecular simulations. It includes both unscaled and scaled parameters, along with a tutorial for constructing and simulating ionic liquid systems using GROMACS.

PythonCompphys is a collection of Jupyter notebooks that cover various topics in computational physics, with a strong emphasis on computational chemistry methods like Hartree-Fock and Density Functional Theory. It serves as a tutorial resource for understanding molecular simulations and quantum mechanics.