/tools

Libra is a computational chemistry library designed for methodology discovery in quantum nonadiabatic dynamics. It provides tools for simulating molecular interactions and dynamics, particularly in the context of quantum chemistry.

HiGNN is a hierarchical graph neural network framework designed for predicting molecular properties by leveraging molecular graphs and BRICS fragments. It includes datasets for training and demonstrates its effectiveness on various drug discovery-related tasks.

SeqDance and ESMDance are biophysics-informed protein language models that predict dynamic properties of proteins based on their sequences. They leverage molecular dynamics data to inform mutation effects on protein folding stability, making them valuable tools in protein design and property prediction.

DFMDock is a diffusion model that integrates sampling and ranking for protein docking tasks. It allows users to perform inference on protein structures using PDB files, facilitating the study of molecular interactions.

Molecular-graph-BERT is a tool designed for semi-supervised learning aimed at predicting molecular properties. It includes functionalities for pre-training and fine-tuning models on specific tasks, as well as building datasets for molecular property prediction.

RamaNet is a tool that utilizes machine learning and PyRosetta to autonomously generate novel helical protein structures. It performs de novo design by creating a topology and optimizing the sequence to fit, while also providing datasets for training the neural network.

RNAPro is a state-of-the-art model for predicting the 3D structure of RNA molecules, developed in collaboration with experts from a Kaggle competition. It utilizes advanced techniques such as template modeling and multiple sequence alignment to enhance the accuracy of RNA structure predictions.

RiboDiffusion is a tool for tertiary structure-based RNA inverse folding utilizing generative diffusion models. It allows users to generate RNA sequences from given PDB structures, facilitating molecular design and exploration in RNA biology.

Novana is a cheminformatics tool that allows for the decomposition of molecules into their scaffolds and shapes, enhancing the analysis of molecular datasets. It can be used for clustering and creating training/validation sets for machine learning, making it a valuable resource in molecular property prediction and design.

DeePMD-gnn is a plugin for the DeePMD-kit that integrates various graph neural network models, allowing users to perform molecular dynamics simulations and train models for predicting molecular properties. It supports models like MACE and NequIP, facilitating advanced molecular simulations and active learning cycles.

PPIformer is a machine learning model that predicts the effects of mutations on protein-protein interactions by estimating binding energy changes. It utilizes pre-training and fine-tuning on specific datasets to enhance its predictive capabilities for protein interactions.

The PAR-NeurIPS21 repository provides a PyTorch implementation of Property-Aware Relation Networks for predicting molecular properties in a few-shot learning context. It includes datasets like Tox21 and SIDER, making it a valuable tool for drug discovery and molecular property prediction.

ROBERT is an automated machine learning tool that processes CSV databases of molecular descriptors or SMILES to generate publication-quality results in chemistry. It streamlines the workflow for predicting molecular properties and enhances reproducibility in computational chemistry studies.

DockM8 is an all-in-one structure-based virtual screening workflow that utilizes consensus docking to prepare libraries and proteins, perform docking, and rank poses. It is designed to facilitate drug discovery by streamlining the virtual screening process.

The std2 program computes excited states and response functions using various simplified TD-DFT methods, making it a valuable tool for researchers in quantum chemistry and molecular simulations.

ConfVAE-ICML21 is an end-to-end framework designed for generating molecular conformations using a variational autoencoder approach. It provides tools for training models on molecular datasets and generating conformations for various molecules, making it a valuable resource in computational chemistry and molecular design.

CGRtools is a Python library designed for the manipulation of molecules and reactions using the Condensed Graph of Reaction (CGR) approach. It supports various operations such as format conversion, molecule standardization, and subgraph searching.

LABODOCK is a collection of Jupyter Notebooks designed for molecular docking using Google Colab. It simplifies the docking process by automating pre- and post-docking tasks and integrates various cheminformatics tools for effective in-silico experimentation.

Indigo ELN is an open-source electronic lab notebook tailored for chemistry applications. It supports drug discovery processes and provides a platform for managing chemical data and experiments.

eMolFrag is a Python-based tool designed for molecular fragmentation using the BRICS algorithm. It aids in the decomposition of small molecules for fragment-based drug design, making it useful for predicting molecular properties and generating new molecular structures.

RMD_Digging is a toolkit designed for pre-processing and post-processing tasks in reactive molecular dynamics simulations using the ReaxFF force field. It includes functionalities for data extraction, statistical analysis, and visualization of molecular structures and trajectories.

The ipymolstar repository offers a Jupyter Notebook-based tool for visualizing and analyzing molecular structures using the Mol* framework. It allows users to interact with molecular data, including loading and displaying structures, which is essential for molecular biology and computational chemistry applications.

RiboNN is a deep learning model designed to predict translation efficiency from mRNA sequences. It allows users to train models on specific datasets and make predictions using pretrained models, focusing on the molecular aspects of RNA translation.