/tools

Lo-Hi Splitter is a tool designed for partitioning molecular datasets to facilitate drug discovery tasks such as lead optimization and hit identification. It employs methods to ensure that training and test sets are distinct, improving the predictive performance of models in drug discovery applications.

ChemFlow is a series of computational chemistry workflows designed to automate and simplify the drug discovery pipeline. It includes functionalities for docking, rescoring, and benchmarking, allowing users to focus on analysis and decision-making.

EasyDock is a Python module designed to automate the molecular docking process, from molecule preparation to result analysis. It supports various docking programs and offers features like distributed computing and detailed protein-ligand interaction analysis.

GrASP is a tool that utilizes graph neural networks to predict druggable binding sites in proteins. It provides datasets and a framework for evaluating binding site predictions, making it useful for drug discovery applications.

MolSnapper is a software tool that utilizes a conditioned diffusion model to generate 3D drug-like molecules. It is built on the MolDiff framework and is aimed at aiding in structure-based drug design.

GLaDOS is a web interface for ChEMBL, a comprehensive database that provides information on drug discovery, including molecular structures and drug-target interactions. It facilitates access to cheminformatics data, supporting research in molecular biology and chemistry.

Metis is a Python-based GUI designed to collect expert feedback on small molecules, facilitating the training of generative models for drug discovery. It supports de novo design and integrates with existing generative chemistry frameworks to enhance molecular design processes.

HiGNN is a hierarchical graph neural network framework designed for predicting molecular properties by leveraging molecular graphs and BRICS fragments. It includes datasets for training and demonstrates its effectiveness on various drug discovery-related tasks.

The PAR-NeurIPS21 repository provides a PyTorch implementation of Property-Aware Relation Networks for predicting molecular properties in a few-shot learning context. It includes datasets like Tox21 and SIDER, making it a valuable tool for drug discovery and molecular property prediction.

LABODOCK is a collection of Jupyter Notebooks designed for molecular docking using Google Colab. It simplifies the docking process by automating pre- and post-docking tasks and integrates various cheminformatics tools for effective in-silico experimentation.

Indigo ELN is an open-source electronic lab notebook tailored for chemistry applications. It supports drug discovery processes and provides a platform for managing chemical data and experiments.

DockM8 is an all-in-one structure-based virtual screening workflow that utilizes consensus docking to prepare libraries and proteins, perform docking, and rank poses. It is designed to facilitate drug discovery by streamlining the virtual screening process.

eMolFrag is a Python-based tool designed for molecular fragmentation using the BRICS algorithm. It aids in the decomposition of small molecules for fragment-based drug design, making it useful for predicting molecular properties and generating new molecular structures.

Uni-FEP-Benchmarks is a benchmark dataset aimed at systematically evaluating the Uni-FEP method for binding free energy calculations. It compiles diverse protein-ligand systems and chemical transformations to facilitate the validation and optimization of the Uni-FEP methodology, contributing to advancements in drug discovery.

Uni-Dock2 is a GPU-accelerated molecular docking software that enhances docking accuracy through advanced algorithms. It supports various docking tasks, including virtual screening and covalent docking, making it a valuable tool for researchers in drug discovery and molecular design.

DiffPhore is a tool that implements a knowledge-guided diffusion model for mapping ligands to pharmacophores in 3D space. It enhances virtual screening capabilities and provides datasets for pharmacophore-ligand pairs, making it useful for drug discovery applications.

Scopy is an integrated Python library that facilitates the screening of undesirable compounds in drug discovery. It includes modules for data preparation, screening filters, and the calculation of molecular properties, making it a valuable tool for designing high-quality chemical libraries.

3D-MCTS is a framework for structure-based de novo drug design that utilizes a fragment-based molecular editing strategy. It efficiently generates molecules with improved binding affinity and synthesizability, making it a valuable tool in drug discovery.

This repository provides code and datasets for analyzing IC50 and Ki values from various sources, highlighting the noise in these measurements. It includes curated datasets from ChEMBL32 and tools for generating results related to molecular property prediction.

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.

Medea is an AI agent that accelerates therapeutic discovery by integrating diverse data modalities and computational resources to identify therapeutic targets and predict drug responses. It includes modules for research planning, analysis, and literature reasoning, making it a comprehensive tool for molecular biology applications.

AliDiff implements a method for aligning target-aware molecule diffusion models with exact energy optimization. It provides tools for data generation, training, and evaluation, including molecular docking capabilities, making it useful for molecular design and drug discovery applications.

PROTACFold is a toolkit that predicts and analyzes PROTAC-mediated ternary complexes using AlphaFold3 and Boltz-1. It provides methods for structure prediction, evaluation, and analysis, facilitating advancements in PROTAC drug discovery.

moldrug is a Python package that focuses on drug-oriented optimization within the chemical space. It employs a Genetic Algorithm as a search engine and integrates with the CReM library for generating chemical structures.