/tools

learnMSA is a tool designed for deep protein multiple alignments using large language and hidden Markov models. It allows for the alignment of millions of protein sequences with high accuracy, leveraging GPU acceleration and advanced modeling techniques.

T-ALPHA is a deep learning model designed to predict the binding affinity of small molecules to protein targets. It utilizes a hierarchical transformer framework and includes features for uncertainty estimation, making it a valuable tool for drug discovery applications.

The Peptide Dashboard provides a web-based platform for predicting peptide properties using deep learning models. It includes a sequence-based solubility predictor that outperforms existing methods, making it a valuable tool for researchers in molecular biology and computational chemistry.

DE-STRESS is a web application that provides tools for evaluating protein designs, making the process more reliable and accessible. It allows users to select promising protein designs for laboratory testing and includes functionalities for batch processing of protein structures.

ChemInformant is a Python library that provides an all-in-one solution for retrieving chemical properties from the PubChem database. It supports batch processing and offers analysis-ready outputs, making it suitable for various applications in drug discovery and cheminformatics.

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.

MDPOW is a Python package that automates the calculation of water/solvent partition coefficients through molecular dynamics simulations. It supports various parameter sets and requires minimal input from the user, making it a useful tool for predicting molecular properties.

LightDock-Rust is a Rust implementation of the LightDock software designed for macromolecular docking. It utilizes scoring functions like DFIRE to optimize the binding of proteins and peptides, making it a valuable tool for molecular simulations in bioinformatics.

Pytoda is a Python package that simplifies the handling of biochemical data for deep learning applications using PyTorch. It is particularly useful for researchers working on molecular design and related tasks in computational chemistry.

PREFER is a benchmarking and property prediction framework that automates the evaluation of different molecular representations and machine learning models for predicting molecular properties. It supports various models and configurations, allowing users to predict properties like solubility and logD using data-driven molecular representations.

Kanzi is a tool for modeling biological structures through discrete tokenization of proteins. It utilizes flow autoencoders to efficiently encode and decode protein structures, facilitating further applications in protein design and molecular representation.

The RDKit-Neo4j project provides an extension for the Neo4j graph database that allows for efficient querying of chemical structures and properties. It supports exact, substructure, and similarity searches, making it a valuable tool for managing and analyzing molecular data.

AF2_peptide_hallucination is a tool for generating high-affinity binders to flexible peptides using the AlphaFold2 Hallucination method. It allows users to design and optimize peptide binders by predicting their structures and properties based on input sequences.

Aizynthtrain provides routines and pipelines for training models that predict chemical synthesis pathways. It is designed to work with the AiZynthFinder software, facilitating the generation and optimization of synthetic routes in molecular chemistry.

QMCTorch is a PyTorch implementation designed for real space quantum Monte Carlo simulations of molecular systems. It provides tools for simulating molecular behavior at a quantum level, making it useful for researchers in computational chemistry.

DIMOS (Differentiable Molecular Simulator) is a PyTorch-based framework that enhances molecular dynamics and Monte Carlo simulations through machine learning. It allows for the integration of classical force fields and machine learning interatomic potentials, facilitating advanced research in computational chemistry and biology.

This repository provides a pipeline for evaluating protein structure diffusion models, assessing designability, diversity, and novelty of generated protein structures. It supports various evaluation metrics and models, making it a useful tool for protein design and analysis.

The Open Targets Tractability Pipeline assesses the tractability of potential drug targets based on Ensembl Gene IDs. It categorizes targets into various buckets for small molecules, antibodies, and PROTACs, providing valuable data for drug discovery efforts.

The GSD repository allows users to read and write GSD files, which are used to store trajectories of molecular dynamics simulations in HOOMD-blue. It provides an efficient way to access and analyze simulation data, making it a useful tool for researchers in computational chemistry and molecular biology.

The 'route-distances' repository contains tools for calculating distances between synthesis routes and clustering them, primarily aimed at developers and researchers in cheminformatics. It also incorporates a machine learning model for fast predictions of distances between synthetic routes, enhancing its utility in molecular design and retrosynthesis.

This repository serves as the source of truth for drugdesign.org, providing tools and resources for drug discovery, including molecular dynamics and modeling. It supports various aspects of cheminformatics and drug design, making it a valuable resource for researchers in the field.

DockStreamCommunity is a repository that offers Jupyter Notebook tutorials for molecular docking and generative design using reinforcement learning. It supports various docking backends and ligand embedders, making it a useful resource for researchers in molecular design and drug discovery.

The 'absolv' framework provides a simple API for calculating the change in free energy when transferring a solute between solvents or to vacuum. It supports both standard equilibrium and non-equilibrium switching calculations, making it useful for molecular simulations and property predictions.