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

A tensorflow.keras generative neural network for de novo drug design, first-authored in Nature Machine Intelligence while working at AstraZeneca.

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

The official codebase of the paper "Chemical language modeling with structured state space sequence models"

PSP is a python toolkit for predicting atomic-level structural models for a range of polymer geometries.

Get chemical SMILES strings (structures) based on the CAS numbers or the names of the chemicals.

tools to perform group contribution (GC) identification, given the SMILES of a compound

Python interface for Enhanced Monte Carlo (EMC)

Variational Autoencoder (VAE)-based molecular SMILES string generator

De novo Drug Design via Binary Representations of SMILES for avoiding the Posterior Collapse Problem (BIBM 2021)

deep learning based prediction of structures and functional groups from MS/MS spectra

Taffi component increment theory used to predict enthalpy of formation, standard entropy and heat capacity.

11th place solution of NeurIPS 2024 - Predict New Medicines with BELKA competition on Kaggle: https://www.kaggle.com/competitions/leash-BELKA

Smilez is a simple compression library for SMILES strings.

SMILES, SELFIES and Reaction SMILES augmentation using RDKit

Encoder-decoders for translating different chemical formats.

A web application to track the kinase research done by the SGC.

Training pre-trained BERT language model on molecular SMILES from the Molecule Net benchmark by leveraging mixup and enumeration augmentations.

A command-line tool for simple, single-step retrosynthetic reaction prediction using graph partitioning.

Script developed to transform the amino acid smiles to one letter code for later analysis