/tools

RFDesign is a tool for protein hallucination and inpainting using the RoseTTAFold framework. It allows for the generation of protein structures, which is a key aspect of molecular design and computational biology.

The mmpdb package facilitates the identification of matched molecular pairs to predict changes in molecular properties and generate new molecular structures. It supports fragmentation of molecules and indexing for analysis, making it a valuable tool in computational chemistry and drug discovery.

GeoLDM is a tool for generating 3D molecular structures using geometric latent diffusion models. It supports conditional generation based on molecular properties and includes capabilities for evaluating the quality of generated molecules, making it useful for applications in drug discovery.

La-Proteina is a tool for the joint generation of protein amino acid sequences and their full atomistic structures. It utilizes a partially latent flow matching model to effectively design proteins, achieving state-of-the-art performance in various generation benchmarks.

MoleculeSTM is a multi-modal model designed for text-based editing and retrieval of molecular structures. It provides tools for molecular property prediction and includes datasets for training and evaluation, making it a valuable resource in drug discovery and molecular design.

Awesome-Biomolecule-Language-Cross-Modeling is a curated list of resources that focuses on leveraging biomolecule data and natural language processing through multi-modal learning. It includes various models and datasets that facilitate tasks related to molecular properties and interactions.

GEOM is a dataset containing 37 million molecular conformations annotated by energy and statistical weight for over 450,000 molecules. It is designed for use in property prediction and molecular generation, providing essential data for researchers in computational chemistry.

LiGAN is a deep generative model designed for structure-based drug discovery, specifically generating 3D molecular structures that are predicted to bind to target proteins. It utilizes atomic density grids and is built on frameworks like PyTorch and MolGrid, making it a powerful tool for molecular design and optimization.

The 'py4chemoinformatics' repository provides resources and tools for chemoinformatics, including methods for predicting molecular properties, generating chemical structures, and utilizing machine learning techniques in molecular design and analysis. It serves as a comprehensive guide for researchers in the field.

Bio-Diffusion is a geometry-complete diffusion generative model designed for generating and optimizing 3D molecular structures. It allows for both unconditional and property-conditional generation of small molecules, making it a valuable tool in molecular design and optimization.

PLACER is a graph neural network that predicts protein-ligand conformational ensembles by generating structures of small molecules in the context of proteins. It excels in tasks such as docking and modeling conformational heterogeneity, providing a rapid and stochastic approach to molecular design.

MolScore is an automated scoring function that facilitates and standardizes the evaluation of generative models for de novo molecular design. It allows users to implement multi-parameter objectives for drug design, benchmark generative models, and evaluate generated molecules using various metrics.

DrugEx is a deep learning toolkit designed for scaffold-constrained drug design using graph transformer-based reinforcement learning. It allows users to generate novel drug molecules by optimizing multiple objectives, making it a valuable tool in the field of drug discovery.

ProLLaMA is a multitask protein language model that facilitates protein generation and understanding. It employs an Evolutionary Protein Generation Framework to ensure generated proteins are biologically viable while also predicting properties such as solubility and superfamily classification.

PocketGen is a tool that generates full-atom ligand-binding protein pockets using generative models. It benchmarks its performance against established datasets like CrossDocked and Binding MOAD, providing processed datasets for training and evaluation of pocket generation methods.

Fragmenstein is a tool that merges and links compounds by stitching them together based on atomic overlap, allowing for the generation of new molecular conformers. It also places follow-up molecules in relation to parent compounds, facilitating molecular design and optimization in drug discovery.

ScaffoldGraph is an open-source cheminformatics library that utilizes RDKit and NetworkX to generate and analyze scaffold networks and trees. It allows users to explore scaffold-space by generating sub-scaffolds from input molecules, making it a valuable tool for molecular design and analysis.

MolT5 is a tool that facilitates the translation between molecular representations (like SMILES) and natural language descriptions. It includes pretrained models for tasks such as molecule captioning and generation, along with datasets for training and evaluation.

Deep-Drug-Coder is a generative neural network designed for de novo drug design, utilizing a conditional recurrent neural network to generate SMILES strings based on specified molecular properties. It aims to facilitate the generation of molecules that meet desired bioactivity criteria, making it a valuable tool in the field of drug discovery.

Auto3D is a tool that automatically generates low-energy 3D molecular conformers from SMILES or SDF input using neural network potentials. It includes features for tautomer enumeration, stereoisomer generation, and geometry optimization, making it useful for molecular design and optimization tasks.

Genie 2 is a protein structure diffusion model designed for unconditional protein generation and motif scaffolding. It includes training and inference code, allowing users to generate diverse protein structures and evaluate their designability and novelty.

MolGen is a tool for domain-agnostic molecular generation that incorporates chemical feedback to optimize molecular properties. It supports de novo molecule generation and fine-tuning for specific properties like QED and logP, making it valuable for molecular design and optimization tasks.

Genie is a tool for de novo protein design that utilizes equivariant diffusion models to generate protein structures. It includes functionalities for training models, sampling generated domains, and evaluating the results against established benchmarks.

IgLM is a tool for generative language modeling aimed at antibody design. It allows users to generate unique antibody sequences and evaluate their likelihood based on specified parameters, facilitating the design and optimization of antibodies.