/tools

ManyFold is a library that facilitates the training and validation of protein folding models, including AlphaFold and pLMFold. It allows users to generate and optimize protein structures using advanced machine learning techniques.

FeNNol is a library that facilitates the creation and training of force-field-enhanced neural network potentials for molecular simulations. It is optimized for speed and flexibility, allowing users to perform molecular dynamics simulations effectively.

The Qiskit addon for sample-based quantum diagonalization (SQD) enables the estimation of eigenvalues and eigenstates of quantum systems, particularly useful in quantum chemistry applications. It combines quantum sampling with classical processing to improve accuracy in determining molecular properties.

Str2Str is a framework designed for zero-shot protein conformation sampling, utilizing a score-based approach to perturb protein structures and sample conformations. It integrates with existing tools for molecular dynamics simulations and protein folding, making it a valuable resource for computational biology.

MolDQN-pytorch is a PyTorch implementation of a deep reinforcement learning approach for optimizing molecular properties. It allows users to train models for property optimization tasks, making it a valuable tool in the field of molecular design and drug discovery.

LangSim is an application that leverages Large Language Models to assist in computational materials science by providing a natural language interface for querying scientific simulation codes and calculating physical properties. It aims to simplify the interaction with complex simulation tools, making them more accessible to users.

GEMORNA is a deep generative model that designs mRNA sequences with enhanced translational capacity and stability. It supports the generation of coding sequences and untranslated regions, making it a valuable tool for advancing mRNA therapeutics and vaccines.

The 'himp-gnn' repository provides a PyTorch implementation of a hierarchical inter-message passing framework designed for learning on molecular graphs. It is suitable for tasks such as predicting molecular properties and can be utilized in various molecular machine learning applications.

JAXChem is a JAX-based deep learning library that facilitates complex chemical modeling. It aims to support various chemical modeling tasks, including property prediction and molecular generation.

Architector is a Python package that generates 3D chemical structures of organometallic complexes from minimal 2D information. It supports high-throughput in-silico construction and is capable of producing chemically sensible conformers and stereochemistry for various metal complexes.

ReQFlow is a generative model designed for efficient and high-quality protein backbone generation. It utilizes a rectified quaternion flow approach to enhance the designability and speed of protein structure generation, making it a valuable tool in protein design and molecular biology.

The NVIDIA BioNeMo blueprint provides a framework for generative virtual screening in drug discovery, utilizing advanced AI models to design and optimize small molecules and predict protein-ligand interactions. It integrates various tools for protein structure prediction and molecular generation, facilitating efficient drug discovery workflows.

MDBenchmark is a tool that facilitates the generation, execution, and analysis of benchmarks for molecular dynamics simulations. It optimizes the use of computing resources by allowing users to compare performance across different simulation setups and visualize the results.

Caliby is a software tool for protein sequence design based on the Potts model, enabling users to condition designs on structural ensembles. It includes functionalities for sequence scoring and ensemble generation, making it a valuable resource for molecular design in computational biology.

The riff_diff_protflow repository provides an implementation of the RiffDiff pipeline, which is designed for generating and optimizing enzyme structures from theozymes. It utilizes various protein design tools and scripts to create fragment libraries and refine structures, facilitating the design of novel proteins.

NeuroBayes provides a framework for fully and partially Bayesian Neural Networks, enabling uncertainty quantification and modeling of complex relationships in scientific data. It is particularly useful for applications in molecular property prediction and active learning, making it relevant to computational chemistry and molecular biology.

VisualFactory is a collection of scripts designed for high-quality 3D molecular visualization within the ChimeraX environment. It allows users to create detailed and scientifically accurate representations of molecular structures and dynamics, enhancing the understanding of molecular interactions.

GraphLoG is a tool for self-supervised graph-level representation learning, with applications in the chemistry domain. It provides pre-training and fine-tuning capabilities for models that can be used to analyze molecular data.

LigParGen is an automatic parameter generator for small organic molecules using OPLS-AA force fields. It accepts multiple molecular formats and produces output files compatible with various molecular dynamics software, facilitating molecular simulations and parameterization.

HANDE is an efficient and highly parallel code designed for stochastic quantum chemistry. It can be utilized as a standalone program or as a library within existing quantum chemistry codes, facilitating advanced molecular simulations.

DockingPie is a consensus docking plugin for PyMOL that provides a user-friendly interface for conducting molecular docking analyses. It integrates several docking programs, allowing users to perform consensus docking and scoring analyses effectively.

This repository provides an implementation of Thompson Sampling for virtual screening of un-enumerated libraries in molecular design. It allows users to efficiently search and score potential molecules based on various scoring functions, facilitating the exploration of chemical space.

The 'AI in chemistry workshop' repository provides educational resources and hands-on sessions on applying AI and machine learning techniques in chemistry, particularly focusing on molecule generation and data exploration for drug discovery.

AI4S-agent-tools is a collection of agent-ready tools designed for scientific research, focusing on materials science and chemistry. It includes functionalities for deep learning simulations, materials screening, and reaction calculations, making it a valuable resource for molecular and computational chemistry applications.