/tools

ChemicaLite is an SQLite database extension designed for cheminformatics applications. It allows users to store molecules and fingerprints, compute chemical descriptors, and perform chemical queries on a database.

LucaVirus is a tool designed to model the evolutionary and functional landscape of viruses using a unified genome-protein language model. It provides datasets of viral sequences and supports various tasks such as predicting protein properties and classifying viral sequences.

The Otter Knowledge repository enhances protein sequence and SMILES drug databases with a multimodal knowledge graph, improving predictions on drug-target binding affinity benchmarks. It provides pre-trained models and datasets for representation learning in drug discovery.

LP-PDBBind is a repository that develops scoring functions using the PDBBind dataset, providing tools for dataset creation and model retraining for predicting molecular properties such as binding affinities. It includes compiled datasets and scripts for preparing and analyzing protein-ligand complexes.

pyCOFBuilder is a Python package designed for the automated assembly of Covalent Organic Framework structures using a reticular approach. It allows users to generate a variety of COF structures based on specified building blocks and functional groups, facilitating high-throughput molecular design.

Molgraph is a Python package that implements graph neural networks using TensorFlow and Keras, aimed at molecular machine learning. It provides tools for building models that can predict molecular properties and analyze molecular graphs.

DiffCSP-PP is an implementation for generating crystal structures constrained by space groups. It includes functionalities for training models on datasets and evaluating crystal structure predictions, making it a useful tool for molecular design and generation in materials science.

Overreact is a library and command-line tool designed for building and analyzing microkinetic models based on first-principles calculations. It facilitates hypothesis testing in chemical kinetics by allowing users to specify elementary reactions and analyze their outcomes using computational chemistry data.

pLM-BLAST is a tool for detecting remote homology in proteins by comparing embeddings generated from protein language models. It allows users to search databases of protein sequences and visualize results, making it a valuable resource in the field of bioinformatics.

MegaFold is a system designed to accelerate protein structure prediction models, such as AlphaFold2 and AlphaFold3, by optimizing performance on heterogeneous hardware. It provides enhancements in training speed and memory efficiency, making it a valuable tool for researchers in molecular biology.

The MycoplasmaGenitalium repository provides tools and models for simulating and visualizing the structure of Mycoplasma genitalium, including DNA and protein configurations. It utilizes cellPACKgpu for generating 3D models and integrates data from computational models to facilitate molecular modeling.

DuIvy provides scripts for the analysis and visualization of molecular dynamics simulations. It also includes tutorials that may assist users in understanding and applying these tools effectively.

Evolla is a generative model designed to decode the molecular language of proteins, facilitating the generation and understanding of protein sequences. It provides tools for protein design and can be utilized for various applications in molecular biology.

HAC-Net is a deep learning architecture designed for highly accurate prediction of protein-ligand binding affinities. It utilizes a hybrid approach combining 3D convolutional neural networks and graph convolutional networks to achieve state-of-the-art results in this domain.

iSIM is a module designed to perform simultaneous comparisons of multiple molecules, providing an efficient method to calculate average pairwise similarities. It utilizes binary fingerprints and real number descriptors, making it applicable in chemical sampling, clustering, and visualization tasks.

3D-MIL-QSAR is a repository that implements a multi-instance learning approach for predicting the bioactivity of molecules based on their conformers. It includes datasets extracted from ChEMBL and provides a modeling pipeline for molecular machine learning.

BinderFlow is a pipeline for designing protein binders, allowing users to manage and monitor the design process efficiently. It includes features for batch processing, design filtering, and integration with DNA synthesis workflows.

LEFTNet is a framework for building efficient and expressive 3D equivariant graph neural networks, specifically designed to predict molecular properties using datasets such as QM9 and MD17. It leverages advanced machine learning techniques to enhance molecular simulations and property predictions.

ProteinSolver is a deep neural network tool designed to generate amino acid sequences that can fold into specific protein structures. It utilizes graph neural networks to tackle protein design challenges, making it a valuable resource in the field of bioinformatics.

Forte is an open-source plugin for Psi4 that provides various quantum chemistry methods for strongly correlated electrons. It facilitates molecular simulations and can be used to predict molecular properties.

MOFDiff is a diffusion model designed for generating coarse-grained structures of metal-organic frameworks (MOFs). It includes functionalities for optimizing these structures based on specific properties and simulating their behavior in gas adsorption scenarios.

DMPfold is a deep learning tool for de novo protein structure prediction that uses iteratively predicted structural constraints to model protein structures from sequences. It is designed to extend the coverage of protein modeling across genomes and provides a framework for generating multiple structural models for a given protein sequence.

ABFE_workflow is a SnakeMake-based workflow designed for performing Absolute Binding Free Energy calculations using GROMACS. It allows for high-throughput scaling and is aimed at facilitating drug discovery by predicting binding affinities between ligands and protein receptors.

PepINVENT is a generative reinforcement learning framework for designing peptides, including both natural and non-natural amino acids. It allows users to specify objectives for peptide generation and optimization, making it applicable for peptide-based drug design and development.