/tools

Graph2Token is a tool that utilizes graph neural networks to process molecular data, potentially for tasks such as classification and regression related to molecular properties. It involves pretraining a GNN encoder and fine-tuning for specific molecular tasks.

The 'idp-design' repository provides tools for optimizing intrinsically disordered proteins by simulating their properties and designing sequences that meet specific functional criteria. It includes various optimization examples, such as targeting the radius of gyration and designing binders for specific substrates.

This repository offers a Jupyter notebook for batch conversion of molecular file formats, such as SDF to PDB and SMILES to PDBQT. It streamlines the process of preparing molecular data for cheminformatics and drug discovery applications.

MD_ResearchTechniques is an educational resource designed for teaching computational chemistry techniques, specifically focusing on molecular dynamics simulations. It includes lectures, practical exercises, and materials for students to gain hands-on experience in using GROMACS and VMD software.

dash_ngl is a customized Dash component designed for visualizing molecular structures, particularly proteins and DNA. It allows users to select proteins via a dropdown and supports .pdb and .cif file formats, making it a useful tool for molecular representation.

The CB513_dataset repository contains datasets for predicting protein secondary structures, specifically designed for use in deep learning models. It includes filtered datasets that facilitate training and evaluation of models aimed at understanding protein structures.

The nf-binder-design repository provides a Nextflow pipeline for designing protein binders using advanced molecular modeling techniques such as RFdiffusion and BindCraft. It automates the generation and optimization of protein structures, facilitating research in protein engineering and drug discovery.

DEELIG is a tool designed for predicting binding affinity using deep learning models. It provides datasets and supplementary materials to facilitate research in molecular property prediction.

ML4SMILES is a tool designed for the automatic prediction of molecular properties by utilizing substructure vector embeddings within a feature selection workflow. It includes scripts for generating molecular descriptors, performing feature analyses, and optimizing predictive models, making it valuable for drug discovery and molecular property prediction.

The OpenMM Rigid Body Plugin enhances the OpenMM molecular dynamics framework by implementing rigid body dynamics. It allows users to simulate systems composed of rigid bodies and free atoms, providing tools for integration and system creation with rigid body templates.

ProteinTuneRL is a framework designed for optimizing protein sequences using infilling language models and reinforcement learning. It specifically supports antibody design by modifying regions of protein sequences to enhance properties like stability and binding affinity.

TAGMol is a framework for target-aware gradient-guided molecule generation, aimed at optimizing molecular properties for drug design. It includes functionalities for training models and evaluating generated molecules based on various criteria such as binding affinity and drug-likeness.

EquiHGNN is a framework for scalable rotationally equivariant hypergraph neural networks aimed at improving molecular modeling. It integrates symmetry-aware representations to enhance predictions of molecular properties using various datasets, including QM9 and PCQM4Mv2.

The CASP15 repository benchmarks various state-of-the-art protein structure prediction methods, including AlphaFold2 and RoseTTAFold. It provides generated protein structures and metrics for evaluating their predictive performance, making it a valuable resource for researchers in molecular biology and computational chemistry.

This repository contains code for predictive modeling of late-stage functionalization in chemistry using transfer learning techniques. It includes modules for training models, predicting regioselectivity of new molecules, and handling datasets relevant to molecular properties.

pLMSNOSite is an ensemble-based tool designed to predict protein S-nitrosylation sites by integrating supervised word embedding and embeddings from a protein language model. It allows users to input protein sequences and receive predictions regarding potential modifications.

The ProteinMPNN Wrapper enhances the usability of the ProteinMPNN model for protein design by allowing convenient sampling of protein sequences based on backbone structures. It simplifies the process of generating sequences for given protein backbones, making it a useful tool for researchers in molecular biology and computational chemistry.

Zinc-Million is a tool that allows users to download millions of small molecules from the ZINC database in 3D SDF format. It is designed for large-scale virtual screening against specific protein targets, making it useful for drug discovery and molecular simulations.

SCALOP is a Python tool that annotates the canonical structure of antibodies based on their sequences. It supports input in various formats and utilizes dependencies like HMMER for its functionality.

MCTS-RNA is a computational tool that solves the RNA inverse folding problem using Monte Carlo Tree Search. It allows for the design of nested and pseudoknot RNA structures while controlling the GC-content and its deviation precisely.

ChemDataExtractor TADF is a specialized toolkit for extracting chemical information from scientific literature, particularly for TADF materials. It allows users to perform text mining and data extraction, which can be useful for predicting molecular properties and understanding material behavior.

QSARtuna is a library for building predictive QSAR models with optimized hyperparameters using machine learning. It facilitates the optimization of molecular descriptors and algorithms to enhance the accuracy of molecular property predictions.

AutoChem is a virtual chemical reactor that generates products from a pool of compounds and a set of reactions. It automates quantum chemistry calculations and prepares input files for various quantum chemistry software, facilitating the analysis of chemical reactions and their free energies.