Multi instance | BindingDB from TDC

data/bindingdb_kd/meta.yaml

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+name: bindingdb_kd
+description: |-
+  BindingDB is a public, web-accessible database of
+              measured binding affinities, focusing chiefly on the interactions of
+              protein considered to be drug-targets with small, drug-like molecules.
+targets:
+- id: BindingDB_Kd
+  description: binding affinity of the given compound for a given target or protein
+  units: KD
+  type: continuous
+  names:
+  - noun: The strength of binding of a single molecule to its ligand
+  - noun: Drug potency for certain protein target
+  - verb: Inhibit certain protein
+  - verb: Change the functionality and protein conformation
+  - adjective: Inhibition of certain protein target
+  - adjective: Inhibition of certain protein target to change its function
+  uris:
+  - http://purl.obolibrary.org/obo/MI_0646
+benchmarks:
+- name: TDC
+  link: https://tdcommons.ai/
+  split_column: split
+identifiers:
+- id: SMILES
+  type: SMILES
+  description: SMILES
+- id: Target_ID
+  type: Other
+  names:
+  - noun: protein target id
+  - noun: protein id
+  description: protein target id
+- id: Target
+  type: Other
+  names:
+  - noun: protein sequence
+  - noun: protein fastq
+  description: protein sequence in fastq
+license: CC BY 3.0 US.
+links:
+- url: https://doi.org/10.1093/nar/gkl999
+  description: corresponding publication
+- url: https://arxiv.org/abs/2004.08919
+  description: corresponding publication
+- url: https://tdcommons.ai/single_pred_tasks/adme/#bbb-blood-brain-barrier-martins-et-al
+  description: data source
+num_points: 47941
+bibtex:
+- |-
+  @article{https://doi.org/10.48550/arxiv.2004.08919,
+      doi = {10.48550/ARXIV.2004.08919},
+      url = {https://arxiv.org/abs/2004.08919},
+      author = {Huang,  Kexin and Fu,  Tianfan and Glass,  Lucas and Zitnik,  Marinka and Xiao,  Cao and Sun,  Jimeng},
+      keywords = {Machine Learning (cs.LG),  Quantitative Methods (q-bio.QM),  Machine Learning (stat.ML),  FOS: Computer and information sciences,  FOS: Computer and information sciences,  FOS: Biological sciences,  FOS: Biological sciences},
+      title = {DeepPurpose: a Deep Learning Library for Drug-Target Interaction Prediction},
+      publisher = {arXiv},
+      year = {2020},
+      copyright = {Creative Commons Attribution 4.0 International}
+- |-
+  @article{Liu2007,
+      doi = {10.1093/nar/gkl999},
+      url = {https://doi.org/10.1093/nar/gkl999},
+      year = {2007},
+      month = jan,
+      publisher = {Oxford University Press ({OUP})},
+      volume = {35},
+      number = {Database},
+      pages = {D198--D201},
+      author = {T. Liu and Y. Lin and X. Wen and R. N. Jorissen and M. K. Gilson},
+      title = {BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities},
+      journal = {Nucleic Acids Research}

data/bindingdb_kd/transform.py

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+import pandas as pd
+import yaml
+from tdc.multi_pred import DTI
+
+def get_and_transform_data():
+    target_subfolder ="BindingDB_Kd"
+    target_folder = str(target_subfolder).lower()
+    splits = DTI(name = target_subfolder).get_split()
+    df_train = splits["train"]
+    df_valid = splits["valid"]
+    df_test = splits["test"]
+    df_train["split"] = "train"
+    df_valid["split"] = "valid"
+    df_test["split"] = "test"
+    df = pd.concat([df_train, df_valid, df_test], axis=0)
+
+    fn_data_original = "data_original.csv"
+    df.to_csv(fn_data_original, index=False)
+    del df
+    # create dataframe
+    df = pd.read_csv(
+        fn_data_original,
+        delimiter=",",
+    )  # not necessary but ensure we can load the saved data
+    # check if fields are the same
+    fields_orig = df.columns.tolist()
+    assert fields_orig == ['Drug_ID', 'Drug', 'Target_ID', 'Target', 'Y', 'split']
+    # overwrite column names = fields
+    fields_clean = ['Drug_ID', 'SMILES', 'Target_ID', 'Target', 'BindingDB_Kd', 'split']
+    df.columns = fields_clean
+    # data cleaning
+    # remove leading and trailing white space characters
+    df = df.dropna()
+    assert not df.duplicated().sum()
+    # save to csv
+    fn_data_csv = "data_clean.csv"
+    df.to_csv(fn_data_csv, index=False)
+
+    meta = {
+            "name": "bindingdb_kd",  # unique identifier, we will also use this for directory names
+            "description": """BindingDB is a public, web-accessible database of
+            measured binding affinities, focusing chiefly on the interactions of
+            protein considered to be drug-targets with small, drug-like molecules.""",
+            "targets": [
+                {
+                    "id": "BindingDB_Kd",  # name of the column in a tabular dataset
+                    "description": "binding affinity of the given compound for a given target or protein",
+                    "units": "KD",  # units of the values in this column (leave empty if unitless)
+                    "type": "continuous",
+                    "names": [  # names for the property (to sample from for building the prompts)
+                        {"noun": "The strength of binding of a single molecule to its ligand"},
+                        {"noun": "Drug potency for certain protein target"},
+                        {"verb": "Inhibit certain protein"},
+                        {"verb": "Change the functionality and protein conformation"},
+                        {"adjective": "Inhibition of certain protein target"},
+                        {
+                            "adjective": "Inhibition of certain protein target to change its function"
+                        },
+                    ],
+                    "uris": [
+                        "http://purl.obolibrary.org/obo/MI_0646"
+
+                    ],
+                },
+            ],
+            "benchmarks": [
+                {
+                    "name": "TDC",  # unique benchmark name
+                    "link": "https://tdcommons.ai/",  # benchmark URL
+                    "split_column": "split",  # name of the column that contains the split information
+                },
+            ],
+            "identifiers": [
+                {
+                    "id": "SMILES",  # column name
+                    "type": "SMILES",
+                    "description": "SMILES",  # description (optional, except for "Other")
+                },
+                {
+                    "id": "Target_ID",  # column name
+                    "type": "Other",
+                    "names": [
+                        {"noun": "protein target id"},
+                        {"noun": "protein id"},
+                    ],
+                    "description": "protein target id",  # description (optional, except for "Other")
+                },
+                {
+                    "id": "Target",  # column name
+                    "type": "Other",
+                    "names": [
+                        {"noun": "protein sequence"},
+                        {"noun": "protein fastq"},
+                    ],
+                    "description": "protein sequence in fastq",  # description (optional, except for "Other")
+                },            
+            ],
+            "license": "CC BY 3.0 US.",  # license under which the original dataset was published
+            "links": [  # list of relevant links (original dataset, other uses, etc.)
+                {
+                    "url": "https://doi.org/10.1093/nar/gkl999",
+                    "description": "corresponding publication",
+                },
+                {
+                    "url": "https://arxiv.org/abs/2004.08919",
+                    "description": "corresponding publication",
+                },
+                {
+                    "url": "https://tdcommons.ai/single_pred_tasks/adme/#bbb-blood-brain-barrier-martins-et-al",
+                    "description": "data source",
+                },
+            ],
+            "num_points": len(df),  # number of datapoints in this dataset
+            "bibtex": [
+     """@article{https://doi.org/10.48550/arxiv.2004.08919,
+    doi = {10.48550/ARXIV.2004.08919},
+    url = {https://arxiv.org/abs/2004.08919},
+    author = {Huang,  Kexin and Fu,  Tianfan and Glass,  Lucas and Zitnik,  Marinka and Xiao,  Cao and Sun,  Jimeng},
+    keywords = {Machine Learning (cs.LG),  Quantitative Methods (q-bio.QM),  Machine Learning (stat.ML),  FOS: Computer and information sciences,  FOS: Computer and information sciences,  FOS: Biological sciences,  FOS: Biological sciences},
+    title = {DeepPurpose: a Deep Learning Library for Drug-Target Interaction Prediction},
+    publisher = {arXiv},
+    year = {2020},
+    copyright = {Creative Commons Attribution 4.0 International}""",
+                """@article{Liu2007,
+    doi = {10.1093/nar/gkl999},
+    url = {https://doi.org/10.1093/nar/gkl999},
+    year = {2007},
+    month = jan,
+    publisher = {Oxford University Press ({OUP})},
+    volume = {35},
+    number = {Database},
+    pages = {D198--D201},
+    author = {T. Liu and Y. Lin and X. Wen and R. N. Jorissen and M. K. Gilson},
+    title = {BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities},
+    journal = {Nucleic Acids Research}""",
+            ]
+        }
+
+
+    def str_presenter(dumper, data):
+        """configures yaml for dumping multiline strings
+        Ref: https://stackoverflow.com/questions/8640959/how-can-i-control-what-scalar-form-pyyaml-uses-for-my-data
+        """
+        if data.count("\n") > 0:  # check for multiline string
+            return dumper.represent_scalar("tag:yaml.org,2002:str", data, style="|")
+        return dumper.represent_scalar("tag:yaml.org,2002:str", data)
+
+    yaml.add_representer(str, str_presenter)
+    yaml.representer.SafeRepresenter.add_representer(
+        str, str_presenter
+    )  # to use with safe_dum
+    fn_meta = "meta.yaml"
+    with open(fn_meta, "w") as f:
+        yaml.dump(meta, f, sort_keys=False)
+
+    print(f"Finished processing {meta['name']} dataset!")
+
+
+if __name__ == "__main__":
+    get_and_transform_data()