/tools

STARLING is a tool for predicting coarse-grained ensembles of intrinsically disordered proteins from their sequences using a generative model. It allows for the generation of multiple conformations and provides functionalities for analyzing and converting ensemble data.

The SMARTS-RX project provides tools for generating and managing molecular representations using SMARTS notation. It facilitates the classification and identification of various molecular types, making it useful for cheminformatics applications.

Dragonfly_gen is a tool for de novo drug design that utilizes deep interactome learning to generate novel molecules based on predefined properties. It allows users to preprocess data, sample from binding sites, and rank generated molecules based on pharmacophore similarity.

SCISOR is a diffusion model that generates shrunken protein sequences by learning from evolutionary data. It aims to optimize protein design by suggesting deletions that preserve functional motifs, making it a valuable tool in molecular biology.

RFDpoly is a diffusion-based machine learning model that facilitates the de novo design of various polymers, including DNA, RNA, and proteins. It provides tools for generating molecular structures and optimizing their designs, making it a valuable resource in molecular biology and computational chemistry.

MoleculeMO is a tool for multiobjective de novo drug design that utilizes recurrent neural networks to generate and optimize molecules based on various properties. It includes data preprocessing, model training, and validation of generated molecules for their pharmacokinetic properties.

GENiPPI is an interface-aware molecular generative framework aimed at designing modulators for protein-protein interactions. It utilizes a dataset of PPI interfaces to generate novel compounds, enhancing the capabilities of structure-based drug design.

DiffIUPAC is a diffusion-based generative model that facilitates molecular editing by converting chemical natural language (IUPAC names) to SMILES strings. It aims to enhance molecular design and optimization, demonstrating applicability in drug design through various case studies.

HyFactor is an open-source architecture designed for generating molecular structures using a novel Hydrogen-count Labelled Graph approach. It includes implementations for both HyFactor and ReFactor architectures, facilitating the translation between molecular graphs and their representations.

RELATION is a software tool that implements a deep generative model for structure-based de novo drug design. It allows users to prepare molecular datasets, train models, and sample new molecular structures, making it relevant for drug discovery and molecular design.

This repository contains scripts for designing proteins by utilizing the TrRosetta neural network to generate scaffolds for functional motifs. It includes methods for folding design models and scoring the generated designs, making it a valuable tool for protein engineering.

The Flexible Topology project develops a machine learning-based tool for dynamically designing potential drug molecules. It utilizes PyTorch to create models that predict molecular structures and poses, and incorporates molecular dynamics simulations to optimize these designs toward drug-like candidates.

XtalNet is an end-to-end tool for predicting crystal structures based on Powder X-Ray Diffraction data. It utilizes advanced deep learning techniques to generate and evaluate crystal structures, making it significant for materials science and molecular modeling.

MolDesigner is an interactive web interface that helps drug developers design efficacious drugs using deep learning predictions. It allows users to input drug molecules and receive real-time predictions on important indices related to drug efficacy, facilitating iterative design.

This repository implements algorithms for link prediction in a Chemical Reaction Knowledge Graph, enabling the prediction of novel reactions and products. It utilizes models like SEAL and Graph Auto-Encoder to facilitate the design and generation of chemical compounds.

ESM2-RL Designer is a framework for controllable protein design that fine-tunes a pretrained protein language model using reinforcement learning. It aims to generate protein sequences with specific properties such as stability and diversity through a multi-objective reward system.

TransAntivirus is a transformer-based molecular generative model aimed at designing antiviral drugs. It allows for the generation of novel compounds and is built upon existing frameworks for molecular generation, making it a valuable tool in the field of drug discovery.

NAG2G is a neural network model designed for predicting retrosynthesis pathways in molecular chemistry. It supports enhanced stereochemistry features and provides datasets and pretrained weights for effective model validation and usage.

The openprotein-python repository offers a user-friendly interface for the OpenProtein.AI API, enabling users to perform tasks related to protein analysis, including sequence generation and scoring using generative models. It supports various functionalities for protein modeling and design, making it a valuable tool in the field of molecular biology.

ChemBounce is a Python tool that facilitates scaffold hopping by generating new molecular structures based on input SMILES. It uses pre-computed fingerprints for efficient similarity searches and allows users to customize scaffold databases for enhanced performance.

Hotpot is a Python package designed to facilitate communication among various chemical and materials calculation tools. It integrates deep learning with traditional cheminformatics to predict molecular properties, generate new molecules, and optimize experimental conditions.

PRO-LDM is a framework that utilizes a conditional latent diffusion model to design and optimize protein sequences. It enables the generation of natural-like sequences with tailored properties, making it a valuable tool for protein engineering and molecular design.

InSilicoQ is a quantum computation-based package designed for drug design and discovery, utilizing quantum algorithms for property prediction and molecule generation. It integrates machine learning techniques to enhance virtual screening and small molecule design.

This repository provides tools for exhaustive exploration of chemical space using a transformer model. It includes functionalities for generating molecular structures, preprocessing datasets, and computing molecular fingerprints, making it a valuable resource for molecular design and cheminformatics.