/tools

DeepAb is a tool for predicting the structure of antibodies from their sequences using deep learning models. It provides scripts for generating predictions, annotating structures, and scoring designed sequences, making it relevant for molecular biology and computational chemistry.

pVACtools is a computational toolkit that aids in the identification and prioritization of neoantigens from genomic data, facilitating the design of personalized cancer vaccines. It includes various tools for analyzing genetic variants and their implications in immunotherapy.

MOPAC is a molecular orbital package that allows for quantum mechanical calculations of molecules and materials. It is designed for high-throughput calculations and is useful in both educational and research settings for predicting molecular properties.

MaSIF-neosurf is a computational tool designed for surface-based protein design, specifically targeting protein-ligand interactions. It employs deep learning techniques to analyze and generate molecular surfaces, facilitating the design of proteins that can interact with small molecules.

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.

Time Machine is a high-performance differentiable molecular dynamics and forcefield engine that allows for the computation of derivatives of observables with respect to forcefield parameters. It supports various functional forms for molecular simulations and is designed for optimizing forcefield parameters.

sGDML is a reference implementation of the Symmetric Gradient Domain Machine Learning model, aimed at constructing accurate molecular force fields. It facilitates atomistic simulations and predictions of molecular properties, making it a valuable tool in computational chemistry.

CPPTRAJ is a program for processing and analyzing molecular dynamics trajectory data. It supports various popular molecular dynamics software packages and provides tools for trajectory analysis, making it a valuable resource for researchers in computational chemistry and molecular biology.

Pysmiles is a lightweight Python library designed for reading and writing SMILES strings, which are used to represent molecular structures. It allows users to create and manipulate molecular graphs, making it a useful tool in cheminformatics.

Fermi.jl is a quantum chemistry program written in Julia that implements various post Hartree-Fock methods for energy computations and molecular property predictions. It is designed for high-performance calculations in computational quantum chemistry.

FAPLM is an efficient PyTorch implementation of state-of-the-art protein language models, designed to optimize memory usage and inference time. It supports various protein sequence tasks and can be utilized for protein design and benchmarking against official implementations.

FPSim2 is a Python/C++ package that enables fast compound similarity searches using optimized algorithms. It is particularly useful in cheminformatics applications, allowing researchers to efficiently find similar molecules based on their chemical properties.

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.

MSMBuilder is a Python package that implements statistical models for analyzing high-dimensional time-series data from atomistic simulations of biomolecular dynamics. It focuses on feature extraction, dimensionality reduction, and constructing Markov state models to study protein dynamics.

Tinker is a comprehensive software package for molecular mechanics and dynamics, offering various algorithms and support for multiple force fields. It is designed for simulating large molecular systems, including biopolymers, and includes features for optimization and free energy calculations.

PXDesign is a model suite for designing protein binders using a diffusion generator and confidence models. It allows users to create and evaluate potential binders for specific protein targets, facilitating molecular design in drug discovery.

Chroma-PyTorch is an implementation of a generative model for proteins using denoising diffusion probabilistic models and graph neural networks. It aims to facilitate the design of proteins, including those that can bind to specific targets like the spike protein of the coronavirus.

ModelAngelo is an automatic atomic model building program designed for cryo-electron microscopy (cryo-EM) maps. It allows users to build models of proteins and nucleic acids from cryo-EM data, facilitating the identification and modeling of molecular structures.

QCFractal is a distributed compute and database platform that facilitates the execution and management of large-scale quantum chemistry calculations. It allows computational chemists to store, retrieve, and analyze extensive datasets generated from quantum chemistry computations.

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.

Uni-Fold-jax is a trainable implementation of a deep protein folding model based on AlphaFold, allowing users to train their own models for predicting protein structures. It provides tools for preparing data, training models, and inferring protein structures from input sequences.

IOData is a Python library that facilitates the input and output of quantum chemistry file formats, enabling users to generate input files for various computational chemistry applications. It serves as a valuable tool for researchers working in molecular simulations and related fields.

PyQuante2 is an open-source suite for developing quantum chemistry methods, allowing scientists to construct their own quantum chemistry programs. It includes various integral methods and wave function calculations, making it relevant for molecular property predictions and simulations.

AIMNet2 is a calculator that integrates a neural network potential for performing fast and reliable molecular simulations. It supports a wide range of molecular types and provides interfaces for popular simulation packages, making it versatile for various computational chemistry applications.