Create molecular fingerprint features of Ligands and predicting Bioactivities Acting with G Protein-coupled Receptors
Project description
wdl_rf
A two-stage algorithm WDL-RF allows end-to-end learning of prediction pipelines whose inputs are of arbitrary size, which contains the molecular fingerprint generation stage through a new weighted deep learning method and the bioactivity prediction stage by the random forest model.
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
wdl_rf-0.0.15.tar.gz
(132.6 kB
view details)
Built Distribution
wdl_rf-0.0.15-py3-none-any.whl
(151.9 kB
view details)
File details
Details for the file wdl_rf-0.0.15.tar.gz.
File metadata
- Download URL: wdl_rf-0.0.15.tar.gz
- Upload date:
- Size: 132.6 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.21.0 setuptools/40.8.0 requests-toolbelt/0.9.1 tqdm/4.31.1 CPython/3.7.3
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 5dca27729ba6774f806e29f03de0da623bbcdaf0793945b254fabd4929a6478a |
|
| MD5 | ecaaf5b6df6bc41b48917e5520967488 |
|
| BLAKE2b-256 | 03c7ca613d9096d6b39722cfd092ff2ec3d57fcde78e6357b7d288c6ab00c82e |
File details
Details for the file wdl_rf-0.0.15-py3-none-any.whl.
File metadata
- Download URL: wdl_rf-0.0.15-py3-none-any.whl
- Upload date:
- Size: 151.9 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.21.0 setuptools/40.8.0 requests-toolbelt/0.9.1 tqdm/4.31.1 CPython/3.7.3
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 8fdf00fb6d4eed0539088ae07305ca8731b37e175243223c2bdb55b4a945d145 |
|
| MD5 | e96457029e8ecd05693a5ae7dd5d093d |
|
| BLAKE2b-256 | 5f1dec92eeed1f3f55e36958a2205d24a87ea00a5c5761533684e00fa2bb2d3c |
