/tools

Drug_Design_Models is a reimplementation of various models for de novo drug design, utilizing techniques such as recurrent neural networks and graph convolutional networks. It aims to generate and optimize molecular structures based on established research in the field.

The 'Viral Protein Function prediction using Protein Language Model (VPF-PLM)' repository provides a method for predicting the functional categories of viral proteins based on their sequences. It utilizes a classifier built on protein language models to enhance the annotation of prokaryotic viral proteins.

PPLM is a protein-protein language model designed to predict interactions, binding affinities, and contact residues between proteins. It utilizes a novel attention mechanism to enhance the accuracy of these predictions, making it a valuable tool in molecular biology and computational chemistry.

BIND is a tool that leverages protein-language models for virtual screening of ligand-protein interactions without requiring 3D structural information. It utilizes graph neural networks to enhance the identification of true binders from non-binders, making it useful in computer-aided drug design.

The mlx-esm repository provides an implementation of Meta's ESM-1 protein language model using the MLX library. It allows users to generate new protein sequences and predict masked amino acids, contributing to protein design and structure prediction in molecular biology.

EvoMIL is a deep learning method designed to predict virus-host associations at the species level using protein sequences. It leverages a pre-trained protein language model and attention-based multiple instance learning to enhance prediction accuracy for both prokaryotic and eukaryotic hosts.

MBP is a PyTorch implementation designed for multi-task bioassay pre-training aimed at predicting protein-ligand binding affinities. It utilizes the ChEMBL-Dock dataset, which contains extensive protein-ligand binding data, to train models that can predict binding affinities effectively.

PMGen is a comprehensive pipeline designed for predicting peptide-MHC complex structures and optimizing peptide sequences. It utilizes advanced techniques like AlphaFold for structure prediction and includes features for iterative peptide optimization and mutation screening.

iPPIGAN is a tool for de novo molecular design that utilizes deep molecular generative models to create inhibitors targeting protein-protein interactions. It includes functionalities for training models and generating novel compounds, contributing to drug discovery efforts.

The homebrew-cheminformatics repository offers a collection of cheminformatics formulae for the Homebrew package manager, allowing users to easily install various molecular modeling tools like RDKit and Open Babel. These tools are essential for tasks such as predicting molecular properties and performing simulations.

MutaPLM is a tool for protein language modeling that aids in understanding and engineering mutations in proteins. It utilizes pre-training and fine-tuning on specific datasets to enhance its capabilities in mutation explanation and engineering tasks.

BC-Design is a framework designed for high-precision inverse protein folding, integrating structural and biochemical features to enhance protein design accuracy. It utilizes a dual-encoder architecture to generate amino acid sequences that correspond to specific 3D protein structures, making it valuable for protein engineering and drug development.

PoET-2 is a multimodal protein language model designed for generating protein sequences and predicting the effects of protein variants. It utilizes retrieval-augmented techniques to enhance its predictive capabilities, making it a valuable tool in protein design and bioinformatics.

DyScore is an open-source tool that implements a scoring method for identifying true binders and non-binders in drug discovery. It utilizes molecular docking and dynamic feature generation to predict the binding likelihood of compounds to target proteins.

HoloBench is a benchmarking tool for discrete sequence optimization algorithms, specifically designed for biophysical applications. It allows users to optimize sequences using evolutionary strategies, making it relevant for protein design and optimization tasks.

This repository provides tutorials and pipelines for the computational design of metallohydrolases using the RFdiffusion2 model. It includes both dry lab data and wet lab data, facilitating the design and testing of enzyme sequences.

PySmash is a Python package designed for the automatic generation and application of structural alerts in molecular screening. It provides algorithms for deriving representative substructures, which can be used to evaluate molecular potency and ADMET properties in drug discovery.

This repository provides a Python code for simulating the Brownian motion of colloidal particles under the influence of a time-varying force field. It is particularly useful for applications in microfluidics and lab-on-a-chip technologies, allowing for the analysis of particle trajectories and interactions in various force environments.

DrugPilot is an advanced LLM-based agent framework that automates and optimizes various aspects of drug discovery. It includes functionalities for predicting drug properties, generating new drug candidates, and classifying drug-target affinities, thereby streamlining the drug discovery process.

TTMD is a Python tool designed to automate the execution of Thermal Titration Molecular Dynamics simulations. It facilitates the analysis of protein-ligand complex stability and supports various molecular formats, making it useful for researchers in computational chemistry and molecular biology.

The SMARTS-RX project provides tools for generating and managing molecular representations using SMARTS notation. It facilitates the classification and identification of various molecular types, making it useful for cheminformatics applications.

This repository offers a Docker image for RDKit, a collection of cheminformatics and machine-learning software. It facilitates the installation and use of RDKit for various molecular modeling and analysis tasks.

QM9PACK is a Python package designed for data-mining the QM9 dataset, which contains quantum chemistry structures and properties of a large number of molecules. It facilitates the extraction and analysis of molecular properties, making it a useful tool in computational chemistry.