haneulpark commited on
Commit
68da5c5
·
verified ·
1 Parent(s): ec60e4b

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +35 -11
README.md CHANGED
@@ -11,16 +11,24 @@ size_categories:
11
  tags:
12
  - drug discovery
13
  - bioassay
14
- dataset_summary: A comprehensive disease and target-based dataset with 1.4 million
15
- molecules, collected from PubChem to accelerate molecular machine learning for better
 
16
  drug discovery.
17
- citation: "@article{KeshavarziArshadi2022,\n title = {MolData, a molecular benchmark\
18
- \ for disease and target based machine learning},\n volume = {14},\n ISSN = {1758-2946},\n\
19
- \ url = {http://dx.doi.org/10.1186/s13321-022-00590-y},\n DOI = {10.1186/s13321-022-00590-y},\n\
20
- \ number = {1},\n journal = {Journal of Cheminformatics},\n publisher = {Springer\
21
- \ Science and Business Media LLC},\n author = {Keshavarzi Arshadi, Arash and Salem,\
22
- \ Milad and Firouzbakht, Arash and Yuan, Jiann Shiun},\n year = {2022},\n month\
23
- \ = mar \n}"
 
 
 
 
 
 
 
24
  dataset_info:
25
  config_name: MolData
26
  features:
@@ -32,7 +40,7 @@ dataset_info:
32
  dtype: string
33
  - name: AID
34
  dtype: string
35
- - name: Y
36
  dtype: int64
37
  splits:
38
  - name: train
@@ -151,4 +159,20 @@ Split and evaluate the catboost model
151
 
152
  scores = regression_suite.compute(
153
  references=split_featurised_dataset["test"]['Y'],
154
- predictions=preds["cat_boost_regressor::Y"])
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
11
  tags:
12
  - drug discovery
13
  - bioassay
14
+ dataset_summary: >-
15
+ A comprehensive disease and target-based dataset with 1.4 million molecules,
16
+ collected from PubChem to accelerate molecular machine learning for better
17
  drug discovery.
18
+ citation: |-
19
+ @article{KeshavarziArshadi2022,
20
+ title = {MolData, a molecular benchmark for disease and target based machine learning},
21
+ volume = {14},
22
+ ISSN = {1758-2946},
23
+ url = {http://dx.doi.org/10.1186/s13321-022-00590-y},
24
+ DOI = {10.1186/s13321-022-00590-y},
25
+ number = {1},
26
+ journal = {Journal of Cheminformatics},
27
+ publisher = {Springer Science and Business Media LLC},
28
+ author = {Keshavarzi Arshadi, Arash and Salem, Milad and Firouzbakht, Arash and Yuan, Jiann Shiun},
29
+ year = {2022},
30
+ month = mar
31
+ }
32
  dataset_info:
33
  config_name: MolData
34
  features:
 
40
  dtype: string
41
  - name: AID
42
  dtype: string
43
+ - name: 'Y'
44
  dtype: int64
45
  splits:
46
  - name: train
 
159
 
160
  scores = regression_suite.compute(
161
  references=split_featurised_dataset["test"]['Y'],
162
+ predictions=preds["cat_boost_regressor::Y"])
163
+
164
+
165
+ ### Citation
166
+ @article{KeshavarziArshadi2022,
167
+ title = {MolData, a molecular benchmark for disease and target based machine learning},
168
+ volume = {14},
169
+ ISSN = {1758-2946},
170
+ url = {http://dx.doi.org/10.1186/s13321-022-00590-y},
171
+ DOI = {10.1186/s13321-022-00590-y},
172
+ number = {1},
173
+ journal = {Journal of Cheminformatics},
174
+ publisher = {Springer Science and Business Media LLC},
175
+ author = {Keshavarzi Arshadi, Arash and Salem, Milad and Firouzbakht, Arash and Yuan, Jiann Shiun},
176
+ year = {2022},
177
+ month = mar
178
+ }