Molecular AutoEncoder in PyTorch

Last update: Dec 05, 2022

Overview

MolEncoder

Molecular AutoEncoder in PyTorch

Install

$ git clone https://github.com/cxhernandez/molencoder.git && cd molencoder
$ python setup.py install

Download Dataset

$ molencoder download --dataset chembl22

Train

$ molencoder train --dataset data/chembl22.h5

Add --cuda flag to enable CUDA. Add --cont to continue training a model from a checkpoint file.

Pre-Trained Model

A pre-trained reference model is available in the ref/ directory. Currently, it performs with ~98% accuracy on the validation set after 100 epochs of training. However, if you succeed at training a better model, feel free to submit a pull request!

TODO

Shoutouts

Original paper by Gómez-Bombarelli, et al.
keras-molecules by Max Hodak
DeepChem
PyTorch

Owner

Carlos Hernández

Research Scientist @facebookresearch

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Molecular AutoEncoder in PyTorch

Related tags

Overview

MolEncoder

Install

Download Dataset

Train

Pre-Trained Model

TODO

Shoutouts

Owner

Carlos Hernández

KE-Dialogue: Injecting knowledge graph into a fully end-to-end dialogue system.

Inverse Optimal Control Adapted to the Noise Characteristics of the Human Sensorimotor System

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All course materials for the Zero to Mastery Machine Learning and Data Science course.

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Adversarial Texture Optimization from RGB-D Scans (CVPR 2020).

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CVPR 2021: "Generating Diverse Structure for Image Inpainting With Hierarchical VQ-VAE"

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This project is the PyTorch implementation of our CVPR 2022 paper:

Fashion Recommender System With Python

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Crowd-Kit is a powerful Python library that implements commonly-used aggregation methods for crowdsourced annotation and offers the relevant metrics and datasets

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