/tools

The 'diffusion-conformer' repository provides a Python implementation for generating multiple conformers of drug-like molecules using a physics-informed generative model. It allows users to generate conformers for single or multiple molecules and includes training capabilities for custom models.

LLM4Mol is a repository that explores the application of large language models in molecular design and protein research. It serves as a hub for studies and techniques that leverage AI to advance understanding in molecular properties and material science.

Modof is a tool designed for molecule optimization through fragment-based generative models. It allows users to train models on pairs of molecules to generate new molecules with improved properties, making it useful for applications in drug discovery and molecular design.

ADMET_XGBoost is a tool designed for accurate prediction of ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties of small molecules using the XGBoost machine learning algorithm. It provides functionalities for feature extraction from SMILES representations and model training for various ADMET tasks.

SolTranNet is a machine learning tool that predicts the aqueous solubility of molecules based on their SMILES representations. It utilizes a molecular transformer architecture to provide fast and accurate predictions, making it useful for applications in drug discovery and cheminformatics.

MindlessGen is a Python package designed for the semi-automated generation of small molecules using a rule-based algorithm. It allows users to specify element compositions and generates molecules by placing atoms randomly in coordinate space, making it useful for molecular design and exploration.

The RDKit Tutorials repository offers a collection of Jupyter notebooks that guide users through various cheminformatics tasks using RDKit. It covers fundamental and advanced topics such as property calculations, QSAR modeling, and molecular structure manipulation, making it a valuable resource for those interested in molecular design and analysis.

This repository contains modular components designed for cheminformatics and computational chemistry, allowing users to create data processing pipelines. It leverages RDKit for various cheminformatics functionalities, making it suitable for tasks related to molecular property prediction and simulations.

This repository contains Dockerfiles for building lightweight RDKit images, which are used for cheminformatics tasks such as molecular property prediction and manipulation. It facilitates the use of RDKit in cloud environments, making it easier to deploy molecular modeling applications.

ReTReK is a data-driven application designed for retrosynthesis planning, leveraging knowledge from US patent datasets to predict synthetic routes for target molecules. It aims to assist chemists in designing efficient synthesis pathways using various scoring metrics to evaluate potential reactions.

This repository contains algorithms for automatically fragmenting molecules into specified molecular subunits, such as functional groups. It aims to enhance the understanding and manipulation of molecular structures in computational chemistry.

SEEKR2 is a tool designed for multiscale milestoning to compute molecular thermodynamics and kinetics. It enables users to prepare and run simulations using various molecular dynamics engines to analyze processes such as ligand binding and membrane permeability.

MolFrames is a Python module designed for handling chemical structures in Pandas DataFrames, allowing for efficient operations on large molecular datasets. It includes a pipelining workflow for processing molecular data and integrates with RDKit for chemical functionality.

The 'rxn-ebm' repository implements energy-based modeling to enhance the performance of retrosynthesis models by re-ranking reactant predictions. It leverages machine learning to analyze reaction databases and improve synthesis planning for organic chemistry.

The PaccMann^RL repository provides a pipeline for predicting drug sensitivity and generating hit-like anticancer molecules using reinforcement learning. It includes tools for training multimodal predictors and generative models for both omic profiles and molecular structures.

BACPI is a bi-directional attention neural network designed for predicting compound-protein interactions and binding affinities. It allows users to classify interactions and predict continuous binding affinity values based on molecular data.

This repository provides a variational autoencoder for generating and optimizing molecular structures using SMILES data. It includes preprocessing steps, training procedures, and notebooks for visualizing and analyzing the learned latent space of molecular representations.

Modof is a software implementation for optimizing molecules through fragment-based generative models. It allows users to train models on pairs of molecules to generate optimized structures based on specific properties, making it a valuable tool in molecular design and optimization.

MetaScreener is a collection of scripts that integrates various molecular modeling and docking programs to facilitate virtual screening and analysis of molecular interactions. It automates the processing of data and results, making it a valuable tool for computational chemistry and drug discovery.

AutoMolDesigner is an open-source Python application that facilitates automated design and screening of drug-like molecules using a combination of chemical language models and automated machine learning. It includes modules for deep molecular generation and molecular property prediction, enabling researchers to efficiently conceptualize and evaluate new molecular candidates.

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 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.

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.

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.