/tools

ReMetaRNA is a model designed for RNA inverse folding using a message-passing neural network (MPNN). It includes components for dataset construction, model training, and evaluation, making it a useful tool for predicting RNA structures.

This project utilizes an axial-attention-based Transformer model to perform RNA inverse folding, allowing for the design of RNA sequences that correspond to specified secondary structures. It represents a tool for molecular design in the context of RNA.

The RNA 3D Folding project aims to predict the 3D structures of RNA molecules using advanced machine learning algorithms. It integrates bioinformatics data to enhance model training and provides a user-friendly interface for predicting RNA structures from sequences.

DeepPurpose-MD-Discovery is a comprehensive pipeline designed for drug discovery, integrating ligand-target prediction, structural docking, and GPU-accelerated molecular dynamics. It supports both protein and RNA targets, allowing for detailed analysis of ligand interactions and mechanistic insights through an interactive Google Colab interface.

The Oligo-Analytical-Characterization Jupyter notebook automates the data post-processing for high-throughput oligonucleotide analytical characterization. It analyzes LC-MS data to identify synthetic oligonucleotides, calculate purity, and profile impurities, providing detailed reports for each sample run.

BIC-RNA is a tool designed for creating RNA sequences that correspond to specified secondary structures. It addresses the inverse RNA folding problem by taking dot-bracket notation as input and generating the necessary RNA sequence.

This tool uses an evolutionary algorithm to generate RNA sequences that fold into specified secondary structures. It evaluates the generated sequences using the IPknot software to ensure they meet IUPAC constraints and maintain diversity.

The 'nanomech-rna' repository provides a reinforcement learning framework for designing RNA sequences that fold into target secondary structures. It utilizes Graph Neural Networks and includes training data for both behavioral cloning and reinforcement learning to optimize RNA design.

INFO-RNA is a software tool designed for inverse RNA folding that satisfies specific sequence constraints. It utilizes the Vienna RNA Package to predict RNA structures based on given sequences, aiding in the understanding of RNA behavior and design.

py2Dmol is a Python library designed for visualizing protein, DNA, and RNA structures in 2D. It is user-friendly and can be used in Google Colab and Jupyter environments, making it accessible for researchers and students in molecular biology and cheminformatics.

MCTS-RNA-tests contains scripts for testing the MCTS-RNA algorithm, which utilizes Monte Carlo tree search to determine RNA sequences that correspond to a given secondary structure. This tool is aimed at exploring RNA sequence space for inverse folding applications.

gRNAde is a geometric deep learning pipeline designed for 3D RNA inverse design, enabling the generation of RNA sequences based on specified 3D backbone conformations. It utilizes advanced techniques such as multi-state GNN encoders and robust sampling methods to facilitate RNA design and modeling.

This tool implements a thermodynamic model to simulate and predict the effective binding affinity of RNA-binding proteins with multiple domains. It includes scripts for running simulations and visualizing results, making it useful for studying protein-RNA interactions.

IRNAFolding is a tool designed for inverse RNA folding, which involves predicting the sequence of RNA that will fold into a given structure. This process is crucial in molecular biology for understanding RNA function and designing RNA molecules for various applications.

Dual-RAG-IF is a tool for RNA inverse folding that identifies minimal mutations needed for an RNA sequence to fold into a target dual graph structure. It employs a genetic algorithm for generating candidate sequences and optimizes them based on structural compatibility.

NEMO is a tool designed for RNA inverse folding using a nested iterated Monte Carlo approach. It allows users to predict RNA structures based on target configurations and optimize sequences accordingly.

This repository provides a workflow for designing a multi-epitope vaccine against West Nile Virus using immunoinformatics and structural bioinformatics methods. It includes steps for epitope prediction, toxicity screening, structural modeling, and immune simulation.

This repository provides a Monte Carlo Tree Search (MCTS) implementation for designing RNA sequences that fold into specified structures. It is aimed at applications in synthetic biology, biotechnology, and drug design.

RNA_Inverse_Folding_LSTM is a tool that utilizes a Long Short-Term Memory (LSTM) model to tackle the RNA inverse folding problem. This involves predicting the sequence of RNA that will fold into a given structure, which is a key aspect of molecular design and understanding RNA behavior.