A PyTorch and TorchDrug based deep learning library for drug pair scoring. (KDD 2022)

RXNMapper: Unsupervised attention-guided atom-mapping. Code complementing our Science Advances publication on "Extraction of organic chemistry grammar from unsupervised learning of chemical reactions" (https://advances.sciencemag.org/content/7/15/eabe4166).

SLICES: An Invertible, Invariant, and String-based Crystal Representation [2023, Nature Communications] MatterGPT, SLICES-PLUS

DeepSMILES - A variant of SMILES for use in machine-learning

The repository contains the network and the related scripts for encoder-decoder based Chemical Image Recognition

A lightweight python-only library for reading and writing SMILES strings

3D diverse conformers generation using rdkit

PyTorch implementation of the paper "Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules"

A toolkit for working with coarse-grain systems

Implementation of the paper "Neuraldecipher - Reverse-engineering extended-connectivity fingerprints (ECFPs) to their molecular structures" by Tuan Le, Robin Winter, Frank Noé and Djork-Arné Clevert

pytoda - PaccMann PyTorch Dataset Classes. Read the docs: https://paccmann.github.io/paccmann_datasets/

A tool to convert IUPAC representations of glycans into SMILES strings.

A recurrent neural network (RNN) that generates drug-like molecules for drug discovery.

Draw molecules with plotly!

⚗️ An all-in-one solution for chemical property retrieval from PubChem.

The BERT-based embedding model for SMILES molecule representation

A collection of deep reinforcement learning-based & GFlowNet drug molecule generators focused on generation of molecules using Graphs/SELFIES guided by modern retrosynthesis tools to increase synthetic accessibility of de-novo designed drugs.

Low code molecular property prediction

Automated BigSMILES conversion workflow and dataset for homopolymeric macromolecules

Tool for creating deep learning model for predicting properties of chemical compounds based on SMILES.