/tools

FlowMol is a flow matching model that facilitates the generation of 3D small molecules from scratch. It employs advanced machine learning techniques to create novel molecular structures, making it a valuable tool for drug discovery and molecular design.

IgGM is a generative foundation model aimed at antibody design, enabling the creation of novel antibodies and nanobodies through various design tasks such as affinity maturation and structure prediction. It provides tools for designing sequences based on given frameworks and predicting their structures.

The ReinventCommunity repository provides a collection of Jupyter notebook tutorials for using REINVENT 3.2, focusing on molecular design through reinforcement learning and QSAR modeling. It includes examples of data preparation, model building, and various reinforcement learning scenarios to generate and optimize novel compounds.

ConfGF is an implementation of Learning Gradient Fields for generating molecular conformations. It provides tools for training models on molecular datasets and generating conformations from SMILES representations, making it useful for molecular design and related applications.

ChemTS is a software tool that utilizes Monte Carlo Tree Search combined with neural networks to design novel molecules with desired properties such as HOMO-LUMO gap and logP. It also allows for the design of molecules that are active against target proteins, facilitating advancements in drug discovery.

The Protein-LLM-Survey repository provides a comprehensive survey of large language models used for protein sequence modeling, understanding, and generation. It includes various methods and models that facilitate protein design and prediction of protein properties.

DrugEx is an open-source software library designed for the de novo design of small molecules using deep learning generative models within a multi-objective reinforcement learning framework. It provides various generator architectures and scoring tools to facilitate the generation and optimization of drug-like compounds.

PXDesign is a model suite for designing protein binders using a diffusion generator and confidence models. It allows users to create and evaluate potential binders for specific protein targets, facilitating molecular design in drug discovery.

Chroma-PyTorch is an implementation of a generative model for proteins using denoising diffusion probabilistic models and graph neural networks. It aims to facilitate the design of proteins, including those that can bind to specific targets like the spike protein of the coronavirus.

This repository provides a collection of Jupyter Notebooks aimed at teaching life science students the fundamentals of deep learning, with a focus on applications in cheminformatics. It includes notebooks on generative models for molecular design and datasets relevant to molecular machine learning.

PMDM is a software tool that enables the generation of 3D bioactive molecules and lead optimization by utilizing a dual diffusion model. It supports molecular docking and provides benchmarks for evaluating generated molecules, making it a valuable resource for drug discovery and molecular design.

G-SchNet is a generative model that creates 3D molecular structures in an autoregressive manner. It is designed to work with datasets like QM9, allowing for the generation and analysis of small molecules based on their atomic positions and types.

This repository provides an implementation of a denoising diffusion probabilistic model tailored for the conditional generation of protein distograms. It utilizes advanced generative modeling techniques to potentially enhance protein structure prediction and design.

OAReactDiff is a diffusion-based generative model designed to generate 3D chemical reactions efficiently. It accelerates the search for transition states and explores new chemical reactions, making it a significant contribution to molecular design and optimization.

Uni-3DAR is an autoregressive model designed for unified 3D generation and understanding of molecular structures, proteins, and crystals. It supports diverse tasks including molecular property prediction and generation, utilizing pretrained models and datasets for training and inference.

MolCRAFT is a series of projects aimed at developing deep learning models for structure-based drug design and molecule optimization. It introduces novel methodologies for generating molecules with high binding affinity and stable 3D conformations, addressing critical challenges in the field.

DrugFlow is a generative model designed for structure-based drug design, integrating advanced techniques to learn chemical and physical properties from protein-ligand data. It allows for the generation of novel molecules tailored to specific protein targets, enhancing the drug discovery process.

DrugAssist is a large language model aimed at optimizing molecules, making it a valuable tool in drug discovery. It includes a dataset for training and facilitates the generation and optimization of molecular structures.

SLICES is an innovative tool for encoding and decoding crystal structures, enabling the inverse design of solid-state materials with specific properties. It utilizes generative deep learning techniques to facilitate the creation of new materials, making it a valuable resource in computational chemistry and materials science.

This repository provides a conditional graph-based molecule generator designed for multi-objective de novo drug design. It allows users to generate molecules with controlled properties using a generative model, making it a valuable tool for drug discovery.

GLN is a tool for predicting retrosynthesis pathways using a Conditional Graph Logic Network. It includes datasets for training and testing models, making it useful for molecular design and generation tasks.

Pallatom is a protein generation model that produces protein structures with all-atom coordinates by modeling the joint distribution of structure and sequence. It employs a novel network architecture to enhance designability, diversity, and novelty in protein design, making it a valuable tool for researchers in molecular biology.

CrystalFormer is a transformer-based autoregressive model that generates crystalline materials while considering space group symmetries. It utilizes reinforcement learning for fine-tuning and can produce stable crystal structures based on specified prototypes.

Dirichlet Flow Matching is a tool designed for DNA sequence generation and optimization, particularly in the context of enhancer and promoter design. It utilizes advanced machine learning techniques to facilitate molecular design tasks.