molecule-generation VS GRAN

Compare molecule-generation vs GRAN and see what are their differences.

molecule-generation

Implementation of MoLeR: a generative model of molecular graphs which supports scaffold-constrained generation (by microsoft)

GRAN

Efficient Graph Generation with Graph Recurrent Attention Networks, Deep Generative Model of Graphs, Graph Neural Networks, NeurIPS 2019 (by lrjconan)
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molecule-generation GRAN
1 1
240 451
-0.4% -
5.6 0.0
4 months ago 9 months ago
Python C++
MIT License MIT License
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molecule-generation

Posts with mentions or reviews of molecule-generation. We have used some of these posts to build our list of alternatives and similar projects.

GRAN

Posts with mentions or reviews of GRAN. We have used some of these posts to build our list of alternatives and similar projects.
  • Software Engineering or AI or Data Science?
    1 project | /r/cscareerquestionsEU | 2 May 2022
    In your case, I would really avoid AI ML DS altogether unless you believe you have the theoretical prerequisites. The coding part in AI ML DS is not like your typical software. It is scientific code and you must understand what's going on in your program with respect to trainable parameters. Here is an example.

What are some alternatives?

When comparing molecule-generation and GRAN you can also consider the following projects:

denoising-diffusion-pytorch - Implementation of Denoising Diffusion Probabilistic Model in Pytorch

euler - A distributed graph deep learning framework.

MidiTok - MIDI / symbolic music tokenizers for Deep Learning models 🎶

Generalizing-Lottery-Tickets - This repository contains code to replicate the experiments given in NeurIPS 2019 paper "One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers"

pytorch_geometric - Graph Neural Network Library for PyTorch [Moved to: https://github.com/pyg-team/pytorch_geometric]

ProGraML - A Graph-based Program Representation for Data Flow Analysis and Compiler Optimizations

dgl - Python package built to ease deep learning on graph, on top of existing DL frameworks.