/tools

The mmCIF2BioLiP repository provides a web interface and scripts for curating the BioLiP database, which contains biologically relevant ligand-protein interactions. It facilitates the download and organization of data from the PDB, including binding affinities and other molecular information, making it a valuable resource for researchers in molecular biology and drug discovery.

RDMC is a software package designed for handling reaction data and molecular conformers, primarily in 3D. It offers functionalities for generating resonance structures, visualizing conformers, and performing bond analysis, making it a valuable tool for molecular modeling and simulations.

DeepBioisostere is a tool that utilizes deep learning to perform bioisosteric replacements, optimizing various molecular properties such as molecular weight and logP. It allows users to generate and optimize molecules based on specified criteria, making it relevant for molecular design and property prediction.

EdgeSHAPer is a bond-centric explanation method for graph neural networks that utilizes Shapley values to determine edge importance. It is applicable in medicinal chemistry for tasks such as predicting molecular properties and understanding model predictions related to small molecules and proteins.

NAMD-FEP is a tool designed for calculating the binding free energy difference between two small molecules interacting with the same protein target using free energy perturbation (FEP) methods. It provides a Jupyter Notebook tutorial for users to perform FEP simulations efficiently, leveraging cloud GPU resources.

MultiStepRetrosynthesisTTL is a tool designed for predicting multistep retrosynthesis routes by utilizing a disconnection aware triple transformer loop approach. It integrates various models to enhance the prediction of synthetic pathways, making it a valuable resource for molecular design and synthesis in computational chemistry.

DiffDock is a Colab implementation of a state-of-the-art molecular docking method that allows users to perform docking simulations for single and multiple protein-ligand complexes. It provides example data files and notebooks to facilitate the docking process, making it a useful tool for researchers in molecular biology and computational chemistry.

IDSL_MINT is a deep learning framework designed to interpret raw mass spectrometry data, enabling the prediction of molecular fingerprints and structures from MS/MS spectra. It utilizes transformer models to translate mass spectra into molecular descriptors and canonical SMILES, making it a valuable tool for cheminformatics and molecular property prediction.

FragNet is a Graph Neural Network that predicts molecular properties while providing interpretability insights into how substructures affect predictions. It supports various molecular types and includes functionalities for data creation and visualization.

The Multi-task-electronic repository provides a Python implementation of a multi-task equivariant graph neural network designed to predict molecular electronic properties with high accuracy. It includes functionalities for training models on molecular data and using pre-trained models for inference on new molecular systems.

The TABASCO repository presents a fast and simplified model for generating molecules with improved physical quality. It utilizes a non-equivariant Transformer architecture to treat molecular generation as a sequence modeling problem, making it efficient for generating small molecules.

PEGgenerator is a tool designed to generate PEG topology for molecular dynamics simulations in GROMACS and LAMMPS. It allows users to create configurations for PEG molecules with varying numbers of monomers and perform energy minimization and relaxation simulations.

Molify is a Python package that facilitates the creation of atomistic structures using RDKit and ASE, allowing users to convert SMILES strings into 3D molecular representations. It also integrates with Packmol to build periodic boxes of molecules, making it useful for molecular simulations.

Transformato is a Python package designed to facilitate relative alchemical free energy calculations of small molecules with a common scaffold. It supports estimates for relative solvation and binding free energies, making it a valuable tool in computational chemistry.

Chem-Faiss is a tool that utilizes vector similarity search from Faiss combined with chemical fingerprinting to create a scalable architecture for searching compounds. It is particularly useful for drug design and finding structural matches within large datasets.

The 'mol_property' repository provides tools for predicting pKa and other molecular properties from chemical structures using machine learning techniques. It also includes functionalities for calculating various molecular properties and assessing molecular similarity.

QuickBind is a light-weight and interpretable molecular docking model that predicts binding affinities for molecular complexes. It utilizes a dataset from PDBBind for training and evaluation, making it a valuable tool for researchers in drug discovery and molecular design.

Pseudocycle_small_molecule_binder is a tool for the de novo design of diverse small molecule binders using shape complementary pseudocycles. It provides scripts and notebooks for generating and optimizing molecular structures, specifically targeting small molecules for binding applications.

The 'vina_docking' repository contains Python scripts for performing molecular docking using AutoDock Vina. It allows users to preprocess receptor and ligand files, run docking simulations, and output results, making it a valuable tool for drug discovery and molecular interactions.

AEV-PLIG is a tool that utilizes a graph neural network to predict the binding affinity of protein-ligand complexes based on their 3D structures. It benchmarks its performance against established datasets and demonstrates how to train and use the model for predictions.

This repository explores QSAR models for predicting activity cliffs in small-molecule inhibitors, providing datasets and methodologies for molecular property prediction. It includes clean data for various targets and allows for the reproduction of experiments related to binding affinity and activity classification.

TCM-Network-Pharmacology is a tool for traditional Chinese medicine network pharmacology that includes processes like prognostic gene screening, molecular docking verification, and various diagram mappings. It provides functionalities for analyzing and validating molecular interactions and properties.

CompassDock is a framework for deep learning-based molecular docking that evaluates binding affinities and protein-ligand interactions. It provides tools for assessing the physical and chemical properties of ligands and their bioactivity favorability.

DrugGen is a tool that enhances drug discovery by using large language models to generate drug-like SMILES structures from protein sequences. It employs reinforcement learning and supervised fine-tuning to ensure the generated structures are chemically valid and effective.