Update README.md

README.md

@@ -4,7 +4,60 @@ tags:
 - biology
 - chemistry
 configs:
-  - config_name: bace
+  - config_name: bace_new
+    data_files:
+      - split: test
+        path: data2/bace/test.csv
+      - split: val
+        path: data2/bace/valid.csv
+  - config_name: bbbp_new
+    data_files:
+      - split: test
+        path: data2/bbbp/test.csv
+      - split: val
+        path: data2/bbbp/valid.csv
+  - config_name: clintox_new
+    data_files:
+      - split: test
+        path: data2/clintox/test.csv
+      - split: val
+        path: data2/clintox/valid.csv
+  - config_name: hiv_new
+    data_files:
+      - split: test
+        path: data2/hiv/test.csv
+      - split: val
+        path: data2/hiv/valid.csv
+  - config_name: sider_new
+    data_files:
+      - split: test
+        path: data2/sider/test.csv
+      - split: val
+        path: data2/sider/valid.csv
+  - config_name: tox21_new
+    data_files:
+      - split: test
+        path: data2/tox21/test.csv
+      - split: val
+        path: data2/tox21/valid.csv
+---
+
+# MoleculeNet Benchmark ([website](https://moleculenet.org/))
+
+MoleculeNet is a benchmark specially designed for testing machine learning methods of molecular properties. As we aim to facilitate the development of molecular machine learning method, this work curates a number of dataset collections, creates a suite of software that implements many known featurizations and previously proposed algorithms. All methods and datasets are integrated as parts of the open source DeepChem package(MIT license).
+
+MoleculeNet is built upon multiple public databases. The full collection currently includes over 700,000 compounds tested on a range of different properties. We test the performances of various machine learning models with different featurizations on the datasets(detailed descriptions here), with all results reported in AUC-ROC, AUC-PRC, RMSE and MAE scores.
+
+For users, please cite:
+Zhenqin Wu, Bharath Ramsundar, Evan N. Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S. Pappu, Karl Leswing, Vijay Pande, MoleculeNet: A Benchmark for Molecular Machine Learning, arXiv preprint, arXiv: 1703.00564, 2017.
+
+
+
+
+
+
+
+  <!-- - config_name: bace
     data_files:
       - split: train
         path: bace/train.csv
@@ -83,51 +136,4 @@ configs:
       - split: test
         path: tox21/test.csv
       - split: val
-        path: tox21/valid.csv
-  - config_name: bace_new
-    data_files:
-      - split: test
-        path: data2/bace/test.csv
-      - split: val
-        path: data2/bace/valid.csv
-  - config_name: bbbp_new
-    data_files:
-      - split: test
-        path: data2/bbbp/test.csv
-      - split: val
-        path: data2/bbbp/valid.csv
-  - config_name: clintox_new
-    data_files:
-      - split: test
-        path: data2/clintox/test.csv
-      - split: val
-        path: data2/clintox/valid.csv
-  - config_name: hiv_new
-    data_files:
-      - split: test
-        path: data2/hiv/test.csv
-      - split: val
-        path: data2/hiv/valid.csv
-  - config_name: sider_new
-    data_files:
-      - split: test
-        path: data2/sider/test.csv
-      - split: val
-        path: data2/sider/valid.csv
-  - config_name: tox21_new
-    data_files:
-      - split: test
-        path: data2/tox21/test.csv
-      - split: val
-        path: data2/tox21/valid.csv
----
-
-# MoleculeNet Benchmark ([website](https://moleculenet.org/))
-
-MoleculeNet is a benchmark specially designed for testing machine learning methods of molecular properties. As we aim to facilitate the development of molecular machine learning method, this work curates a number of dataset collections, creates a suite of software that implements many known featurizations and previously proposed algorithms. All methods and datasets are integrated as parts of the open source DeepChem package(MIT license).
-
-MoleculeNet is built upon multiple public databases. The full collection currently includes over 700,000 compounds tested on a range of different properties. We test the performances of various machine learning models with different featurizations on the datasets(detailed descriptions here), with all results reported in AUC-ROC, AUC-PRC, RMSE and MAE scores.
-
-For users, please cite:
-Zhenqin Wu, Bharath Ramsundar, Evan N. Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S. Pappu, Karl Leswing, Vijay Pande, MoleculeNet: A Benchmark for Molecular Machine Learning, arXiv preprint, arXiv: 1703.00564, 2017.
-
+        path: tox21/valid.csv -->