EBjerrum

SMILES-enumeration

SMILES enumeration for QSAR modelling using LSTM recurrent neural networks

rdeditor

Simple RDKit molecule editor GUI using PySide

scikit-mol

scikit-learn classes for molecular vectorization using RDKit

Deep-Chemometrics

Using deep learning approaches and convolutional neural networks (CNN) for spectroscopical data (deep chemometrics)

molvecgen

Molecular vectorization and batch generation

pICalculax

Isoelectric point (pI) predictor for chemically modified peptides and proteins.

heteroencoders

Molecular Heteroencoders example code

molll

Data Driven Estimation of Molecular Log-Likelihood using Fingerprint Key Counting

DeepIEP

DeepIEP: Isoelectric Point (IEP/pI) of peptides using Recurrent Neural Networks (RNNs)

matplotlib_styles

Matplotlib style sheets to nicely format figures for scientific papers, thesis and presentations while keeping them fully editable in Adobe Illustrator.

RDKit_Jupyter_Notebooks

RDKit Jupyter Notebooks

REINVENT

Molecular De Novo design using Recurrent Neural Networks and Reinforcement Learning

aizynthfinder

A tool for retrosynthetic planning

ChemCharts

ChemCharts is a module that allows you to plot chemical space in various figure types

deep-learning-models

Keras code and weights files for popular deep learning models.

Levenshtein

Levenshtein SMILES augmentation for reaction datasets

Lib-INVENT-dataset

missing-data-multitask-methods

This repository contains the data sets, the code and the results that were reported on the article "Effect of missing data on multitask prediction methods", found at

MolVS

Molecule Validation and Standardization

pIR

Estimation of isoelectric point of peptides and proteins using Support vector machines

pysmilesutils

Utilities for working with SMILES based encodings of molecules for deep learning (PyTorch oriented)

RAscore

Retrosynthetic Accessibility (RA) score learned from computer aided synthesis planning

rdkit

The official sources for the RDKit library

reaction_utils

Utilities for working with datasets of chemical reactions, reaction templates and template extraction.

SmilesNet

Deep learning using simplified molecular-input line-entry system (SMILES) of molecules

UGM_2017

2017 RDKit UGM

UGM_2018

2018 RDKit UGM

UGM_2019

UGM_2020

Materials from the (virtual) 2020 RDKit UGM

wcc_rDock

Try rDock