/tools

SPROF-GO is a tool for predicting protein functions from sequences using a pretrained language model and homology-based label diffusion. It offers fast and accurate predictions and includes datasets and models for users interested in reproducing the results.

Platonic Transformers is a framework that integrates geometric group theory into transformer architectures to enhance molecular property prediction. It supports various molecular datasets, including QM9 for quantum chemistry properties and OMol for molecular learning tasks.

PyVibMS is a PyMOL plugin that allows users to visualize vibrations in molecules and solids. It supports various quantum chemistry programs and provides tools for analyzing vibrational spectra, making it useful for researchers in computational chemistry.

AutoTST is a framework that automates transition state theory calculations for reaction families commonly found in combustion chemistry. It utilizes existing quantum chemistry packages to optimize molecular geometries and obtain kinetic parameters.

The OpenMM Workshop repository provides training materials for using OpenMM to run molecular dynamics simulations. It includes Jupyter notebooks that cover topics such as protein-ligand complexes, custom forces, and machine learning potentials, aimed at helping users understand and utilize molecular simulation techniques.

TMbed is a tool that predicts transmembrane proteins and their segments using embeddings generated from a Protein Language Model. It allows users to generate predictions and embeddings for protein sequences, making it a valuable resource in the field of molecular biology.

The `pair_nequip` repository offers a pair style for LAMMPS that allows users to implement NequIP models in molecular simulations. Although deprecated, it serves as a bridge for integrating machine learning models into molecular dynamics workflows.

Galore is a Python package designed for applying Gaussian and Lorentzian broadening to data from ab initio calculations. It is primarily used for simulating electronic density-of-states and Raman spectra, making it a useful tool in the field of molecular simulations.

PaccMann Proteomics provides a framework for protein language modeling using transformer architectures to predict protein classification and binding interactions. It utilizes self-supervised learning techniques to handle unlabeled protein sequences and offers pre-trained models and datasets for various protein-related tasks.

This repository contains code for autoregressive neural-network wavefunctions aimed at solving the many-body Schrödinger equation in quantum chemistry. It provides tools for generating molecular data and running quantum chemistry experiments, leveraging machine learning techniques to enhance computational efficiency.

Jrystal is a JAX-based framework designed for differentiable density functional theory calculations, enabling efficient optimization workflows for quantum properties of materials. It supports solid-state calculations and is optimized for GPU performance, making it suitable for advanced electronic structure computations.

Modof is a tool designed for molecule optimization through fragment-based generative models. It allows users to train models on pairs of molecules to generate new molecules with improved properties, making it useful for applications in drug discovery and molecular design.

MapDiff is a PyTorch implementation of a deep diffusion model designed to improve the inverse protein folding task. It predicts feasible amino acid sequences from 3D protein backbone structures, contributing to the field of protein design.

The BA-DDG repository provides an implementation of a Boltzmann-Aligned Inverse Folding Model that predicts the effects of mutations on protein-protein interactions. It utilizes datasets like SKEMPI v2 and offers both supervised and unsupervised inference methods.

COBY (Coarse Grained System Builder) is a Python-based software tool for creating coarse-grained systems in Martini 3. It supports the construction of complex biological systems, including asymmetric membranes and protein insertion, making it useful for molecular simulations.

This repository provides Jupyter notebooks for tutorials on quantum computing and its applications in chemistry, including quantum algorithms for simulating molecular systems. It serves as an educational resource for understanding quantum machine learning and chemistry simulation techniques.

This repository offers scripts and instructions for conducting molecular dynamics simulations with GROMACS, a popular software for modeling atomic and molecular interactions. It includes guidance on preparing input files, running simulations, and analyzing results, making it a valuable resource for researchers in computational chemistry and molecular biology.

DeepNano is a tool designed for predicting interactions between nanobodies and antigens using ensemble deep learning and protein language models. It provides code and model weights for various prediction tasks related to protein-protein interactions.

NetSolP-1.0 is a tool that predicts the solubility and usability for purification of proteins expressed in E. coli using advanced protein language models. It provides both a webserver and standalone version for training and prediction, along with datasets and trained models.

pyscf-forge is a staging ground for code that enhances the PySCF package, which is utilized for quantum chemistry computations. It allows users to perform various molecular simulations and calculations, making it relevant for molecular property prediction and simulation tasks.

libcifpp is a C++ library designed to manipulate mmCIF and PDB files, allowing users to access and query molecular data efficiently. It includes features for symmetry calculations and 3D manipulations, making it useful for researchers working with protein structures.

GeoAB is a tool designed for realistic antibody design and reliable affinity maturation. It provides a framework for training models that can generate and optimize antibody structures, utilizing datasets for evaluation and refinement.

SiamDiff is a codebase for a diffusion-based pre-training algorithm that enhances protein structure encoders. It improves performance on tasks such as protein-protein interaction prediction and function annotation by learning effective representations from protein sequences and structures.