/tools

ConfVAE-ICML21 is an end-to-end framework designed for generating molecular conformations using a variational autoencoder approach. It provides tools for training models on molecular datasets and generating conformations for various molecules, making it a valuable resource in computational chemistry and molecular design.

Origin-1 is a generative AI platform designed for the de novo design of antibodies targeting novel epitopes. It includes data on binding affinity and optimization results, making it a valuable resource for antibody development in molecular biology.

PropMolFlow is a tool for property-guided molecular generation using SE(3) equivariant flow matching. It includes datasets for training, benchmarks for molecular properties, and supports the generation of molecules conditioned on specific properties.

This repository implements a machine learning-guided framework for protein design through directed evolution. It utilizes large language models to predict fitness scores and generate novel protein sequences, streamlining the optimization process in protein engineering.

The openff-fragmenter is a Python package designed to fragment molecules for quantum mechanics torsion scans. It allows users to visualize and manipulate molecular fragments, making it a useful tool in molecular design and computational chemistry.

CGCF-ConfGen is a tool for generating molecular conformations using neural generative dynamics. It allows users to train models and generate multiple conformations for molecules, making it useful for molecular design and optimization tasks.

3D-MCTS is a framework for structure-based de novo drug design that utilizes a fragment-based molecular editing strategy. It efficiently generates molecules with improved binding affinity and synthesizability, making it a valuable tool in drug discovery.

The 'diffusion-conformer' repository provides a Python implementation for generating multiple conformers of drug-like molecules using a physics-informed generative model. It allows users to generate conformers for single or multiple molecules and includes training capabilities for custom models.

Modof is a tool designed for molecule optimization through fragment-based generative models. It allows users to train models on pairs of molecules to generate new molecules with improved properties, making it useful for applications in drug discovery and molecular design.

GeoAB is a tool designed for realistic antibody design and reliable affinity maturation. It provides a framework for training models that can generate and optimize antibody structures, utilizing datasets for evaluation and refinement.

dZiner is an AI framework designed for the rational inverse design of materials, allowing for the generation and assessment of new molecular candidates based on user-defined properties and constraints. It incorporates a human-in-the-loop approach to refine designs and improve chemical feasibility.

AliDiff implements a method for aligning target-aware molecule diffusion models with exact energy optimization. It provides tools for data generation, training, and evaluation, including molecular docking capabilities, making it useful for molecular design and drug discovery applications.

ProteinReDiff is a framework that utilizes equivariant diffusion-based generative models to redesign ligand-binding proteins. It allows for the generation of high-affinity proteins based on initial sequences and ligand SMILES, facilitating advancements in drug discovery and protein engineering.

MindlessGen is a Python package designed for the semi-automated generation of small molecules using a rule-based algorithm. It allows users to specify element compositions and generates molecules by placing atoms randomly in coordinate space, making it useful for molecular design and exploration.

DENOPTIM is a software package that facilitates the de novo design and virtual screening of functional molecules by assembling building blocks and analyzing their properties. It employs genetic algorithms for optimization and is suitable for various types of chemical entities.

MolRL-MGPT is a code repository for a NeurIPS 2023 paper that presents a method for de novo drug design using multiple GPT agents in a reinforcement learning framework. It incorporates large molecular datasets and benchmarks for evaluating the generated molecules, making it a relevant tool in the field of molecular design and drug discovery.

The RDKit Tutorials repository offers a collection of Jupyter notebooks that guide users through various cheminformatics tasks using RDKit. It covers fundamental and advanced topics such as property calculations, QSAR modeling, and molecular structure manipulation, making it a valuable resource for those interested in molecular design and analysis.

Torchchem is an experimental repository that offers high-quality tools for molecular machine learning with PyTorch. It focuses on various aspects of molecular modeling, including property prediction and molecular generation.

This repository provides tools for predicting binding affinity and generating kinase inhibitors using active site sequence representations. It includes scripts for training models and optimizing molecular structures based on predicted affinities.

PeptideDesign is a codebase for full-atom d-peptide co-design utilizing flow matching methods. It includes functionalities for training models to generate peptides based on binding pockets, making it a valuable tool for peptide design in molecular biology.

GIT-Mol is a multi-modal large language model that integrates graph, image, and text data to perform various molecular tasks, including property prediction and molecule generation. It utilizes a novel architecture called GIT-Former to map different modalities into a unified latent space, facilitating advanced molecular analysis and generation.

This repository contains code for predicting properties of quantum systems using conditional generative models. It includes tools for generating datasets, training models, and simulating quantum systems, making it relevant for molecular property prediction and simulation tasks.

gcWGAN is a tool developed for de novo protein design using a guided conditional Wasserstein GAN. It leverages deep generative models to explore sequence-structure relationships and generate novel protein sequences based on given folds.

The GenUI framework offers a backend for molecular generation and QSAR modeling through a REST API, enabling users to manage datasets and visualize chemical spaces. It supports the integration of various molecular generators and facilitates the upload and download of bioactivity datasets.