/tools

DeePMD-kit is a deep learning package designed to create models for interatomic potential energy and perform molecular dynamics simulations. It interfaces with various deep learning frameworks and classical molecular dynamics packages, making it suitable for a wide range of molecular systems.

Graphein is a protein and interactomic graph library that enables the creation of geometric representations of protein and RNA structures, as well as biological interaction networks. It supports various molecular types and provides functionalities for graph construction, visualization, and analysis, making it a valuable resource for molecular design and drug discovery.

Ketcher is an open-source web-based chemical structure editor designed for chemists and laboratory scientists. It provides features for drawing, editing, and visualizing molecular structures in various formats, supporting both small molecules and macromolecules.

DScribe is a Python package that transforms atomic structures into fixed-size numerical fingerprints, known as descriptors. These descriptors can be utilized in various machine learning tasks related to atomistic systems, including property prediction and similarity analysis.

The SMILES Transformer is a tool that extracts molecular fingerprints from SMILES representations of chemical molecules. It utilizes a transformer architecture to learn latent representations useful for various downstream tasks in drug discovery.

GearNet is a geometry-aware relational graph neural network designed for protein structure representation learning. It employs various self-supervised learning methods to enhance the encoding of protein spatial information, making it suitable for downstream tasks in molecular biology.

MolCLR is an implementation of a contrastive learning framework for molecular representation learning using graph neural networks. It enhances the performance of models on various molecular property prediction tasks and provides datasets for pre-training and fine-tuning.

The DPLM repository provides implementations of diffusion protein language models that excel in generating and predicting protein sequences and structures. It includes features for unconditional and conditional protein generation, as well as representation learning for various protein-related tasks.

ProstT5 is a bilingual language model designed for translating between protein sequences and their corresponding 3D structures. It utilizes advanced machine learning techniques to derive embeddings and facilitate the understanding of protein structures from sequences.

xyz2mol is a Python tool that converts Cartesian coordinates from xyz files into RDKit molecular objects, allowing for the generation of molecular graphs. It supports the handling of resonance forms and can output various molecular formats, making it useful for computational chemistry applications.

Mol2vec is an unsupervised machine learning tool that generates vector representations of molecular substructures, facilitating the analysis and prediction of molecular properties. It allows users to prepare molecular data, train models, and featurize new samples using learned embeddings.

MolScribe is an image-to-graph model that converts molecular images into their corresponding chemical structures. It employs deep learning techniques to facilitate molecular structure recognition, making it a valuable tool for researchers in computational chemistry and related fields.

Transformer-M is a molecular modeling tool that utilizes a transformer architecture to process and generate representations of molecular data in both 2D and 3D formats. It has demonstrated strong performance in tasks related to molecular property prediction and has been used in competitive benchmarks.

ATOMICA is a geometric AI model that learns universal representations of intermolecular interactions at an atomic scale. It is pretrained on a large dataset of molecular interaction interfaces and can be used for various downstream tasks, including binding site prediction and embedding biomolecular complexes.

DrugCLIP is a tool designed for contrastive protein-molecule representation learning aimed at enhancing virtual screening processes in drug discovery. It includes datasets and methodologies for training models that predict interactions between proteins and small molecules.

MolVS is a Python-based tool that focuses on the validation and standardization of chemical structures. It utilizes the RDKit framework to enhance data quality by standardizing representations, helping with de-duplication, and identifying relationships between molecules.

3DInfomax enhances graph neural networks for predicting molecular properties by leveraging 3D molecular geometry. It provides tools for pre-training and fine-tuning models on various molecular datasets, enabling better predictions and molecular fingerprint generation.

Pysmiles is a lightweight Python library designed for reading and writing SMILES strings, which are used to represent molecular structures. It allows users to create and manipulate molecular graphs, making it a useful tool in cheminformatics.

The CHEAP repository provides a framework for the joint embedding of protein sequences and structures using compressed representations. It allows users to obtain embeddings that can be utilized in various applications related to protein design and molecular simulations.

MolMapNet is a deep learning framework that utilizes knowledge-based molecular representations to predict molecular properties. It provides tools for feature extraction, distance calculation, and visualization, making it suitable for tasks in drug discovery and molecular property prediction.

DeepSMILES is a Python module that converts standard SMILES notation to a modified version suitable for machine learning applications. It simplifies the representation of molecular structures, making it easier to work with in generative models and other computational chemistry tasks.

E3FP is a tool for generating extended 3-dimensional molecular fingerprints, which are useful for representing molecular structures in cheminformatics. It integrates with RDKit and can be applied in various molecular property prediction tasks.

SLICES is an innovative tool for encoding and decoding crystal structures, enabling the inverse design of solid-state materials with specific properties. It utilizes generative deep learning techniques to facilitate the creation of new materials, making it a valuable resource in computational chemistry and materials science.

CIRpy is a Python interface that simplifies interaction with the NCI Chemical Identifier Resolver (CIR). It allows users to resolve chemical identifiers, such as names to SMILES strings, enhancing the accessibility of chemical data.