/tools

Bio-Diffusion is a geometry-complete diffusion generative model designed for generating and optimizing 3D molecular structures. It allows for both unconditional and property-conditional generation of small molecules, making it a valuable tool in molecular design and optimization.

nvMolKit is a GPU-accelerated library that facilitates key computational chemistry tasks, including molecular similarity assessments and conformer generation. It is optimized for performance on NVIDIA GPUs, making it suitable for various molecular modeling applications.

ASE_ANI is a prototype interface for the ANI-1x and ANI-1ccx neural network potentials, enabling predictions of molecular properties and facilitating molecular dynamics simulations. It is designed for use within the Atomic Simulation Environment (ASE) and supports various applications in computational chemistry.

Qrisp is a high-level framework designed for quantum algorithm development, providing tools for intuitive programming and efficient implementation of quantum algorithms. It includes capabilities for simulating molecular systems, making it relevant for researchers in computational chemistry.

MDCrow is a toolset that leverages a large language model (LLM) to conduct molecular dynamics simulations. It is built on Langchain and integrates with OpenMM to set up and execute simulations, making it a valuable resource for researchers in molecular dynamics.

nablaDFT is a comprehensive dataset and benchmark designed for evaluating neural network potentials in molecular property prediction and Hamiltonian prediction. It includes a large collection of drug-like molecules with calculated electronic properties, making it a valuable resource for computational chemistry and machine learning applications in drug discovery.

InterMol is a software tool that enables the conversion of molecular dynamics simulation files between different formats, such as Desmond, GROMACS, and LAMMPS. It aims to ensure that energy outputs remain consistent across these formats, making it a valuable resource for researchers in computational chemistry and molecular biology.

PLACER is a graph neural network that predicts protein-ligand conformational ensembles by generating structures of small molecules in the context of proteins. It excels in tasks such as docking and modeling conformational heterogeneity, providing a rapid and stochastic approach to molecular design.

PaDELPy is a Python wrapper for the PaDEL-Descriptor software, enabling users to calculate molecular descriptors and fingerprints from SMILES strings, MDL MolFiles, and SDF files. It facilitates property prediction and cheminformatics tasks by providing a straightforward interface for descriptor calculation.

MolScore is an automated scoring function that facilitates and standardizes the evaluation of generative models for de novo molecular design. It allows users to implement multi-parameter objectives for drug design, benchmark generative models, and evaluate generated molecules using various metrics.

MolTrans is a tool designed for predicting drug target interactions using a transformer-based model. It addresses challenges in molecular representation learning and provides datasets for training and evaluation.

chemlab is a Python library aimed at facilitating the work of computational chemists by providing a consistent API and utilities for molecular visualization and data handling. It serves as a foundation for building specific computational chemistry applications.

OpenMM-Torch is a plugin that integrates PyTorch with OpenMM, enabling users to define forces using neural networks for molecular simulations. It allows for the computation of potential energy and forces based on particle positions, facilitating advanced molecular dynamics simulations.

This repository provides scripts for evolving human antibodies based on general protein language models. It allows users to recommend mutations to antibody sequences and reproduce analyses from a related research paper.

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.

Transformer-M is a molecular modeling tool that utilizes a transformer architecture to process and generate representations of molecular data in both 2D and 3D formats. It has demonstrated strong performance in tasks related to molecular property prediction and has been used in competitive benchmarks.

RDKit.js is a cheminformatics and molecule rendering toolbelt for JavaScript, enabling users to visualize and manipulate molecular structures. It leverages the RDKit library's functionalities, making it suitable for applications in drug discovery and molecular analysis.

MolecularGraph.jl is a graph-based toolkit designed for cheminformatics and molecular modeling. It offers functionalities for analyzing molecular properties, performing substructure queries, and visualizing molecular graphs, making it a valuable resource for computational chemistry applications.

DrugEx is a deep learning toolkit designed for scaffold-constrained drug design using graph transformer-based reinforcement learning. It allows users to generate novel drug molecules by optimizing multiple objectives, making it a valuable tool in the field of drug discovery.

The 'protein-ligand-benchmark' repository offers a comprehensive dataset designed for testing parameters and methods of free energy calculations in protein-ligand interactions. It includes detailed metadata for various protein targets and ligands, facilitating research in molecular property prediction and computational chemistry.

The 'useful_rdkit_utils' repository provides a collection of useful functions built on the RDKit library, aimed at facilitating various cheminformatics tasks. It includes Jupyter notebooks that demonstrate how to utilize these functions for molecular analysis and manipulation.

Matbench Discovery is an evaluation framework that ranks machine learning models on tasks related to high-throughput discovery of stable inorganic crystals. It predicts material properties such as thermodynamic stability and thermal conductivity, providing insights for building large-scale materials databases.

WESTPA is a software toolkit for conducting and analyzing stochastic simulations using the weighted ensemble method. It allows researchers to perform simulations that can access longer timescales than traditional methods, making it a valuable tool in computational chemistry and molecular biology.

ProLLaMA is a multitask protein language model that facilitates protein generation and understanding. It employs an Evolutionary Protein Generation Framework to ensure generated proteins are biologically viable while also predicting properties such as solubility and superfamily classification.