/tools

GPDL is a deep learning framework designed for generating novel protein backbones based on specified motifs and sequences. It utilizes a protein language model to optimize the design process, making it a valuable tool for molecular design in protein engineering.

RetroBridge is a Markov bridge model that facilitates retrosynthesis planning by predicting reactants for given product molecules. It utilizes a generative framework to learn distributions in a discrete state space, achieving state-of-the-art results in retrosynthesis tasks.

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.

Molecular_VAE_Pytorch is a PyTorch implementation of a Variational Autoencoder designed for automatic chemical design. It utilizes a continuous representation of molecules to generate new molecular structures based on the ChEMBL dataset.

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.

AlphaPPImd utilizes transformer-based generative neural networks to explore and generate new conformations of protein-protein complexes. It enhances molecular dynamics simulations by providing a method to sample unsampled conformations, thereby aiding in the analysis of protein interactions.

HELM-GPT is a tool designed for the de novo generation of macrocyclic peptides using a generative pre-trained transformer model. It allows users to train models and generate new peptide structures, contributing to drug discovery efforts.

LeMat-GenBench is a comprehensive benchmarking framework designed to evaluate crystal generative models across various metrics such as validity, diversity, and stability. It facilitates the assessment of material generation models, making it relevant for molecular design and optimization in computational chemistry.

CodonMPNN is a tool for organism-specific and codon optimal inverse folding, producing codon sequences based on trained models from the AlphaFold database. It facilitates the design of codon-optimized sequences for proteins, making it relevant in the field of molecular biology.

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.

AF2_peptide_hallucination is a tool for generating high-affinity binders to flexible peptides using the AlphaFold2 Hallucination method. It allows users to design and optimize peptide binders by predicting their structures and properties based on input sequences.

This repository provides a pipeline for evaluating protein structure diffusion models, assessing designability, diversity, and novelty of generated protein structures. It supports various evaluation metrics and models, making it a useful tool for protein design and analysis.

DockStreamCommunity is a repository that offers Jupyter Notebook tutorials for molecular docking and generative design using reinforcement learning. It supports various docking backends and ligand embedders, making it a useful resource for researchers in molecular design and drug discovery.

RDMC is a software package designed for handling reaction data and molecular conformers, primarily in 3D. It offers functionalities for generating resonance structures, visualizing conformers, and performing bond analysis, making it a valuable tool for molecular modeling and simulations.

FvHallucinator is a tool that designs antibody sequences to achieve desired folding structures using a deep learning model. It employs a sequence-to-structure prediction approach to optimize the design of specific regions within antibodies, particularly the complementarity-determining regions (CDRs).

FRAME is a tool for fragment-based molecular expansion that utilizes geometric deep learning techniques to aid in structure-based ligand design. It allows for the addition of molecular fragments to a seed ligand, optimizing the design process for drug discovery.

DeepBioisostere is a tool that utilizes deep learning to perform bioisosteric replacements, optimizing various molecular properties such as molecular weight and logP. It allows users to generate and optimize molecules based on specified criteria, making it relevant for molecular design and property prediction.

This repository provides a PyTorch implementation for generative protein design, allowing users to create and optimize protein structures. It includes scripts for dataset management and model training, making it a useful tool for researchers in molecular biology.

The GGM_LOG_Tutorial provides a tutorial on geometric generative models, which can be applied in the context of molecular design and optimization. It is aimed at researchers interested in generative modeling techniques for molecular applications.

p2smi is a Python toolkit designed for the generation and analysis of drug-like peptide SMILES strings. It allows users to create peptide sequences, convert them to SMILES representations, and evaluate various molecular properties, making it a valuable resource for computational peptide chemistry.

RAG-ESM is a framework that enhances pretrained protein language models by conditioning them on homologous sequences. It allows for the generation of novel protein sequences and improves predictive performance through a retrieval-augmented approach.

LEADD is a tool that employs a Lamarckian evolutionary algorithm for the design and optimization of molecules in drug discovery. It utilizes a population-based approach to evolve molecular structures by combining fragments and optimizing them based on user-defined scoring functions.

GraphRelax is a drop-in replacement for Rosetta Relax that utilizes graph neural networks for residue repacking and design. It combines LigandMPNN for sequence design with OpenMM for energy minimization, enabling efficient protein structure optimization.