/tools

DockStream is a docking wrapper that automates docking execution and post hoc analysis, providing access to various ligand embedders and docking backends. It is integrated with the REINVENT platform, allowing for the incorporation of docking into the molecular design process.

MolGym is a tool that utilizes reinforcement learning to design molecules in three-dimensional space, guided by principles of quantum mechanics. It allows users to train agents to build molecular structures by placing atoms on a canvas, facilitating the generation and optimization of new molecular designs.

kGCN is a graph-based deep learning framework that focuses on the classification and prediction of molecular properties using graph convolutional networks. It supports the generation of molecular data and provides tools for dataset preparation and model training in cheminformatics.

DrugGPT is a tool that employs a GPT-based strategy to design potential ligands for specific proteins. It utilizes deep learning to explore chemical space and optimize ligand design, enhancing the drug development process.

PPIFlow is a framework for the de novo generation of high-affinity biological binders, including antibodies and nanobodies. It integrates a design workflow that supports various tasks such as binder design, antibody design, and motif scaffolding, utilizing deep learning techniques for protein structure generation.

PLAID is a multimodal generative model designed for generating protein sequences and all-atom structures based on specific prompts. It includes features for both unconditional and conditional sampling, making it a valuable tool for protein design and molecular generation.

LSTM_Chem is a tool that implements generative recurrent networks for de novo drug design, allowing users to generate new molecular structures based on learned patterns from existing data. It utilizes SMILES representations for molecules and is built using TensorFlow and Keras.

CatLearn is a machine learning environment focused on atomic-scale modeling for surface science and catalysis. It provides utilities for building and testing machine learning models, including Gaussian Processes for predicting molecular properties and optimizing atomic structures.

ChemGAN challenge provides code for a study on using AI to reproduce natural chemical diversity for drug discovery. It employs generative models to design and generate molecules, making it a valuable resource in the field of computational chemistry.

SynFlowNet is a GFlowNet model that generates molecules based on chemical reactions and available building blocks, allowing for the design of diverse and novel molecules while considering synthesis constraints. It includes functionalities for training the model and sampling molecules guided by user-defined rewards.

ProGen is an implementation of a language model designed for generating protein sequences, akin to GPT for text. It utilizes deep learning techniques to facilitate the design and generation of proteins, making it a valuable tool in the field of molecular biology.

DrugHIVE is a software tool that implements a deep hierarchical variational autoencoder for structure-based drug design. It allows for the generation and optimization of ligands, making it a valuable resource in the field of molecular design and drug discovery.

LocalRetro is a tool for predicting retrosynthetic pathways for organic molecules using machine learning. It implements a model that derives local reaction templates and predicts reactants based on given products, facilitating the design and generation of new molecules.

PepGLAD is a tool for full-atom peptide design that utilizes geometric latent diffusion models to co-design peptide sequences and structures. It supports binding conformation generation and provides datasets for benchmarking the models.

ResGen is a tool for generating 3D molecular structures that are aware of their binding pockets, utilizing parallel multi-scale modeling. It is designed for molecular generation tasks, particularly in the context of drug discovery and protein-ligand interactions.

OVO is an open-source ecosystem designed for de novo protein design, integrating models, workflows, and data management. It provides a scalable platform for scaffold and binder design, utilizing advanced methods for protein structure generation and validation.

FlowSite and HarmonicFlow are tools for generating binding structures for single and multi-ligands, as well as designing binding site residues. They utilize advanced generative models to optimize molecular interactions, making them valuable for applications in drug discovery and protein design.

MEAN is a tool for conditional antibody design utilizing a multi-channel equivariant attention network. It provides functionalities for redesigning antibody CDRs and optimizing binding affinities, making it a valuable resource in the field of molecular design and drug discovery.

CatKit is a collection of computational tools aimed at facilitating research in catalysis. It includes modules for generating various catalytic structures and automating workflows, making it useful for researchers in the field of molecular catalysis.

PepFlow is a tool for full-atom peptide design utilizing multi-modal flow matching techniques. It allows for the generation and evaluation of peptides based on their interaction with receptor binding pockets, providing a framework for peptide optimization and design.

ProteinDT is a framework for text-guided protein design that allows for the generation and editing of protein sequences. It utilizes advanced machine learning techniques to optimize protein properties and facilitate design tasks.

RITA is a family of autoregressive models designed for generating protein sequences. It leverages deep learning techniques to facilitate the design and optimization of proteins, making it a valuable tool in molecular biology and computational chemistry.

COCR is a tool designed to convert images of handwritten chemical structures into graphical representations of molecules. It utilizes Optical Character Recognition techniques to facilitate the recognition of chemical formulas, making it useful for cheminformatics applications.

ProGen3 is a repository that contains models for scoring and generating protein sequences using machine learning techniques. It allows users to evaluate the likelihood of sequences and generate new protein sequences based on prompts, making it a valuable tool for molecular design in bioinformatics.