ebjerrum Goto Github PK

aizynthfinder

A tool for retrosynthetic planning

chemcharts

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

deep-chemometrics

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

deep-learning-models

Keras code and weights files for popular deep learning models.

deepiep

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

heteroencoders

Molecular Heteroencoders example code

levenshtein

Levenshtein SMILES augmentation for reaction datasets

lib-invent-dataset

lstm_music

DUB music in the making. Teaching computers to improvise and play with neural networks

matplotlib_styles

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

metis-1

Python-based GUI to collect Feedback of Chemist in Molecules

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

molll

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

molvecgen

Molecular vectorization and batch generation

molvs

Molecule Validation and Standardization

picalculax

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

pir

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

protwis

protwis_vagrant

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

rdeditor

Simple RDKit molecule editor GUI using PySide

rdkit

The official sources for the RDKit library

rdkit_jupyter_notebooks

RDKit Jupyter Notebooks

reaction_utils

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

reinvent

Molecular De Novo design using Recurrent Neural Networks and Reinforcement Learning

scikit-mol

scikit-learn classes for molecular vectorization using RDKit

smiles-enumeration

SMILES enumeration for QSAR modelling using LSTM recurrent neural networks

smilesnet

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

ugm_2016

Materials from the 2016 RDKit UGM