/tools

Turbomoleio is a Python package that serves as an interface to the TURBOMOLE quantum chemistry software, facilitating the generation of input files and the parsing of output data. It is designed to assist researchers in computational chemistry by streamlining the workflow associated with quantum chemical calculations.

SLIP is a sandbox environment designed for engineering protein sequences through synthetic fitness functions. It allows users to create and manipulate landscapes for protein design, making it a valuable tool in computational biology.

ChemicalIdentifiers.jl is a Julia package designed for searching chemical identifiers such as CAS, PubChemID, SMILES, and InChI from a text query. It facilitates access to a database of over 70,000 compounds, making it useful for researchers in the field of chemistry.

SUPERChem is a multimodal reasoning benchmark designed to evaluate the chemical reasoning capabilities of large language models. It includes a dataset of 500 expert-curated problems and provides tools for evaluation and analysis of model performance in chemistry.

The 'antibody-dl' repository is a collection of platforms, tools, and resources aimed at enhancing antibody engineering. It includes deep learning models for antibody design, structural prediction, and various databases that support antibody research and development.

LeanLJ is a project that aims to bring mathematical accuracy and reliability to molecular simulations by formalizing molecular dynamics and Monte Carlo simulations. It ensures that every calculation is grounded in proven mathematics, reducing the risk of errors and enhancing the trustworthiness of simulation results.

Syndirella is a tool designed for generating and scoring synthetically tractable elaborations of molecules derived from fragment screens. It utilizes retrosynthetic analysis and energy minimization to produce viable synthetic routes for small molecules.

The DrugHunting repository provides Python scripts for automating drug discovery processes, including the design and optimization of drug-like molecules. It utilizes stochastic methods and cheminformatics to explore novel chemical spaces, making it suitable for applications like docking and virtual screening.

The ANI-1xnr repository provides a machine learning model for interatomic potentials in condensed-phase reactive chemistry. It includes companion data and a model that can be utilized for molecular simulations and property predictions in chemistry.

PoseidonQ is a software tool designed for efficient QSAR modeling, allowing users to build, validate, and deploy predictive models for molecular properties. It supports data extraction from databases and offers various machine learning models for regression and classification tasks in drug discovery.

Deepdock is an experimental package that utilizes deep learning techniques specifically for molecular docking applications. It aims to enhance the accuracy and efficiency of predicting how molecules interact with each other at the molecular level.

The PTM Parameterization Prototype allows users to parameterize proteins with post-translational modifications and simulate them using the OpenFF Rosemary force field. It provides a Jupyter Notebook that demonstrates how to handle both canonical and non-canonical amino acids in protein simulations.

This repository contains code for automatically extracting a dataset of interacting compound-target pairs from the ChEMBL database. It enables researchers to analyze drug-target interactions and supports future analyses in drug discovery.

The rdkit-sys repository offers a Rust interface to the RDKit C++ API, facilitating the development of applications in cheminformatics and molecular modeling. It allows users to leverage RDKit's capabilities for various molecular tasks, including property prediction and molecular simulations.

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.

ChemXploreML is a desktop application that utilizes machine learning to predict various molecular properties of organic compounds. It supports advanced molecular representation techniques and automates data preprocessing, making it accessible for chemists to explore and analyze molecular data.

Torsionfit is a toolkit designed for Bayesian parameterization of torsion angles in molecular mechanics forcefields, utilizing quantum chemical data. It aims to improve the accuracy of molecular simulations by refining torsion parameters based on statistical inference methods.

Ichor is a Python package designed to simplify data management from computational chemistry programs and support machine learning force field development. It provides interfaces for various computational chemistry software, flexible data structures for managing large datasets, and tools for benchmarking molecular dynamics simulations.

OPUS-Fold is an open-source framework designed for protein folding based on torsion-angle sampling. It integrates various methods for protein structure prediction and allows for efficient modeling of protein side chains during the folding process.

Chemtools is a Python toolbox that facilitates advanced computations in quantum chemistry, enabling users to manipulate and optimize basis sets for various electronic structure programs. It supports computations with popular software like Dalton, Gamess-US, and MolPro, making it a valuable resource for computational chemists.

CHAP is a tool for the functional annotation of ion channel structures, enabling researchers to analyze and interpret the structural and functional aspects of these proteins. It utilizes molecular dynamics simulations to provide insights into ion channel behavior.

This repository provides a framework for predicting molecular properties using Gaussian Process regression with SOAP kernels. It includes scripts for data preprocessing, SOAP descriptor generation, and model training, making it suitable for QSAR modeling in drug discovery.