/tools

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.

The Pharmacophore-Toolkit allows users to generate simple pharmacophore models using RDKit, which can be derived from crystal structures, docking poses, or SMILES strings. It provides functionalities for visualizing these models in both 2D and 3D formats, aiding in the understanding of molecular interactions.

dZiner is an AI framework designed for the rational inverse design of materials, allowing for the generation and assessment of new molecular candidates based on user-defined properties and constraints. It incorporates a human-in-the-loop approach to refine designs and improve chemical feasibility.

APEX (Alloy Property EXplorer) is a Python package that facilitates the calculation of various alloy properties through cloud-native workflows. It integrates multiple computational approaches, including molecular dynamics and first-principles calculations, to streamline the process of alloy property testing.

π-PrimeNovo is a deep learning model designed for accurate and efficient de novo peptide sequencing. It addresses challenges in peptide identification from mass spectrometry data, making it a valuable tool in molecular biology and protein design.

MolecuLarva is a molecular simulation tool that visualizes the behavior of particles in 2D and 3D space, simulating interactions such as collisions and forces between different particle types. It aims to study self-organizing systems and explore conditions for the emergence of artificial life.

LAMP is a deep learning-based surrogate model designed for controllable adaptive simulations in multi-resolution physics. It optimizes computational costs while improving prediction accuracy, making it applicable for molecular simulations and related computational chemistry tasks.

PIDGINv3 is a tool for predicting protein targets using random forests trained on bioactivity data. It employs molecular descriptors and provides models for various activity cut-offs, making it useful for drug discovery and cheminformatics applications.

SPECTRA is a Python toolkit that provides a spectral framework for evaluating the generalizability of biomedical AI models, particularly in the context of molecular datasets. It allows users to define spectral properties and generate train-test splits to assess model performance across varying degrees of data overlap.

The UGM 2016 repository provides educational materials from the RDKit User Group Meeting, featuring notebooks and presentations on various topics in cheminformatics, including molecular simulations and property prediction techniques. It serves as a resource for learning and applying RDKit tools in computational chemistry.

MOF-ChemUnity is a knowledge graph that unifies computational and experimental data for over 15,000 metal-organic frameworks. It allows users to query and retrieve information about MOFs, including their properties and applications, enhancing the understanding and research of these materials.

pyWannier90 is a Python interface for the Wannier90 package, enabling the construction of maximally-localized Wannier functions from wave functions obtained via PySCF or VASP. It facilitates analysis of electronic properties in solid-state materials, contributing to molecular simulations and quantum chemistry research.

This repository provides protocols and scripts for performing molecular dynamics simulations using Gromacs, along with trajectory analysis and various tips for effective simulation practices.

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.

AlphaFold3-Conda-Install is a step-by-step guide for installing and configuring AlphaFold 3, a state-of-the-art tool for predicting protein structures. It facilitates the setup of the necessary environment and dependencies to run AlphaFold 3 effectively on compatible hardware.

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.

proseLM is a protein language model that adapts to structure-conditioned sequence generation, facilitating the design of proteins based on their structural information. This tool aims to enhance the capabilities in protein design and engineering through advanced machine learning techniques.

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.

GenScore is a generalized scoring framework for protein-ligand interactions that extends RTMScore. It provides capabilities for scoring, ranking, and screening on multiple datasets, making it useful for molecular property prediction and docking tasks.

MolScript is a program that produces publication-quality images of macromolecular 3D structures, such as proteins, in both schematic and detailed representations. It has been a standard tool in the science of macromolecular structures for many years.

OMMProtocol is a command line application that facilitates the launch of molecular dynamics simulations using OpenMM. It supports various input file formats and provides features for monitoring simulation progress and managing output trajectories.

GLAM is a software tool that implements an adaptive graph learning method to automate the prediction of molecular interactions and properties. It aims to improve drug discovery efficiency by providing robust and interpretable predictions across various datasets.

The biomed-multi-view repository features the Multi-view Molecular Embedding with Late Fusion (MMELON) architecture, which aggregates molecular representations from images, graphs, and text to enhance predictions of molecular properties. It is applicable to various tasks including ligand-protein binding and molecular solubility, utilizing a large dataset of molecules for training and evaluation.