| maxhodak/keras-molecules |
448 |
|
0 |
0 |
over 8 years ago |
0 |
|
37 |
mit |
Python |
| Autoencoder network for learning a continuous representation of molecular structures. |
| john-bradshaw/molecule-chef |
72 |
|
0 |
0 |
over 2 years ago |
0 |
|
11 |
gpl-3.0 |
Python |
| Code for our paper "A Model to Search for Synthesizable Molecules" (https://arxiv.org/abs/1906.05221) |
| marcopodda/fragment-based-dgm |
46 |
|
0 |
0 |
over 3 years ago |
0 |
|
2 |
|
Python |
| Code for the paper "A Deep Generative Model for Fragment-Based Molecule Generation" (AISTATS 2020) |
| aspuru-guzik-group/GA |
41 |
|
0 |
0 |
almost 5 years ago |
0 |
|
0 |
|
Python |
| Code for the paper: Augmenting genetic algorithms with deep neural networks for exploring the chemical space |
| cieplinski-tobiasz/smina-docking-benchmark |
40 |
|
0 |
0 |
over 3 years ago |
0 |
|
3 |
mit |
Python |
| stan-his/DeepFMPO |
21 |
|
0 |
0 |
over 6 years ago |
0 |
|
2 |
mit |
Python |
| Code accompanying the paper "Deep reinforcement learning for multiparameter optimization in de novo drug design" |
| simonmb/fragmentation_algorithm_paper |
20 |
|
0 |
0 |
about 4 years ago |
0 |
|
0 |
mit |
Python |
| Two algorithms to fragment molecules into specified molecular subunits (e.g. functional groups) |
| aksub99/molecular-vae |
19 |
|
0 |
0 |
about 5 years ago |
0 |
|
1 |
mit |
Jupyter Notebook |
| Pytorch implementation of the paper "Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules" |
| fhooton/FoodMine |
11 |
|
0 |
0 |
almost 5 years ago |
0 |
|
0 |
mit |
Jupyter Notebook |
| chaoyan1037/Re-balanced-VAE |
10 |
|
0 |
0 |
over 3 years ago |
0 |
|
0 |
apache-2.0 |
Python |
| Code for our paper Re-balancing Variational Autoencoder Loss for Molecule Sequence Generation. |
