Revert "Revert "Abhik update""

.gitignore

@@ -14,11 +14,12 @@ node_modules
 dist
 build
 *.egg-info
-
-examples/.*
-
 # Playwright
 test-results
 playwright-report
 /blob-report/
 /playwright/.cache/
+/examples/.joblib-cache
+/lynxkite-app/src
+/lynxkite-app/src
+/lynxkite-app/web

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+                  "[0 1 0 ... 0 0 0]",
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+                [
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+                  "[0 0 0 ... 0 0 0]",
+                  9.52
+                ],
+                [
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+                  "[0 1 0 ... 0 0 0]",
+                  8.29
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+                  "[0 0 0 ... 0 0 0]",
+                  9.62
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+                  "[0 0 0 ... 0 0 0]",
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+                  "[0 1 0 ... 0 0 0]",
+                  7.51
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+                [
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+                  "[0 0 0 ... 0 0 0]",
+                  9.02
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+                [
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+                  9.46
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+                [
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+                  "[0 1 0 ... 0 0 0]",
+                  8.95
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+                [
+                  101,
+                  "[0 1 0 ... 0 0 0]",
+                  8.78
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+                [
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+                  "[0 1 0 ... 0 0 0]",
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+                  "[0 0 0 ... 0 0 0]",
+                  8.29
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+                  "[0 1 0 ... 0 0 0]",
+                  7.95
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+                [
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+                  "[0 1 0 ... 0 0 0]",
+                  7.48
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+                [
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+                  "[0 1 0 ... 0 0 0]",
+                  6.79
+                ],
+                [
+                  107,
+                  "[0 1 0 ... 0 0 0]",
+                  8.39
+                ]
+              ]
+            },
+            "training": {
+              "columns": [
+                "training_loss"
+              ],
+              "data": [
+                [
+                  67.413818359375
+                ],
+                [
+                  67.11187744140625
+                ],
+                [
+                  66.81317901611328
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+                ],
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+                ],
+                [
+                  64.06808471679688
+                ],
+                [
+                  63.73294448852539
+                ],
+                [
+                  63.3873405456543
+                ],
+                [
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+                ],
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+                ],
+                [
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+                ],
+                [
+                  61.87138366699219
+                ],
+                [
+                  61.452850341796875
+                ],
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+                ],
+                [
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+                ],
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+                ],
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+                ],
+                [
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+                ],
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+                ],
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+                ],
+                [
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+                ],
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+                ],
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+                ],
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+                ],
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+                ],
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+                ],
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+                ],
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+                ],
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+                ],
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+                ],
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+                ],
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+                ],
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+                ],
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+                ],
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+                ],
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+                ],
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+                ],
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+                ],
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+                ],
+                [
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+                ],
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+                ],
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+                ],
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+                  2.176875591278076
+                ],
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+                  2.102105140686035
+                ],
+                [
+                  2.066572666168213
+                ],
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+                ],
+                [
+                  2.082454204559326
+                ],
+                [
+                  2.1185786724090576
+                ],
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+                  2.163982629776001
+                ],
+                [
+                  2.212440252304077
+                ],
+                [
+                  2.2587029933929443
+                ],
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+                  2.298664093017578
+                ],
+                [
+                  2.3294262886047363
+                ],
+                [
+                  2.349210023880005
+                ],
+                [
+                  2.3572983741760254
+                ],
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+                ],
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+                ],
+                [
+                  2.3165128231048584
+                ],
+                [
+                  2.285783290863037
+                ],
+                [
+                  2.2495362758636475
+                ]
+              ]
+            }
+          },
+          "other": {
+            "model": "ModelConfig(model=Sequential(\n  (0) - Linear(1024, 512, bias=True): Input__tensor_1_output -> Linear_1_output\n  (1) - <function relu at 0x7f2e936854e0>: Linear_1_output -> Activation_1_output\n  (2) - Linear(512, 256, bias=True): Activation_1_output -> Linear_2_output\n  (3) - <function relu at 0x7f2e936854e0>: Linear_2_output -> Activation_2_output\n  (4) - Linear(256, 1, bias=True): Activation_2_output -> Linear_3_output\n  (5) - Identity(): Linear_3_output -> Output_1_x\n  (6) - Identity(): Output_1_x -> Output_1_x\n), model_inputs=['Input__tensor_1_output'], model_outputs=['Output_1_x'], loss_inputs=['Input__tensor_3_output', 'Output_1_x'], loss=Sequential(\n  (0) - <function mse_loss at 0x7f2e93687420>: Output_1_x, Input__tensor_3_output -> MSE_loss_2_output\n  (1) - Identity(): MSE_loss_2_output -> loss\n), optimizer_parameters={'lr': 0.0001, 'type': <OptionsFor_type.AdamW: 1>}, optimizer=AdamW (\nParameter Group 0\n    amsgrad: False\n    betas: (0.9, 0.999)\n    capturable: False\n    decoupled_weight_decay: True\n    differentiable: False\n    eps: 1e-08\n    foreach: None\n    fused: None\n    lr: 0.0001\n    maximize: False\n    weight_decay: 0.01\n), source_workspace='Cheminformatics/Model_NN_SAR', trained=True)"
+          },
+          "relations": []
+        },
+        "error": null,
+        "input_metadata": [
+          {
+            "dataframes": {
+              "df": {
+                "columns": [
+                  "x",
+                  "y"
+                ]
+              },
+              "df_test": {
+                "columns": [
+                  "index",
+                  "pred",
+                  "x",
+                  "y"
+                ]
+              },
+              "df_train": {
+                "columns": [
+                  "index",
+                  "x",
+                  "y"
+                ]
+              },
+              "training": {
+                "columns": [
+                  "training_loss"
+                ]
+              }
+            },
+            "other": {
+              "model": {
+                "model": {
+                  "inputs": [
+                    "Input__tensor_1_output"
+                  ],
+                  "loss_inputs": [
+                    "Input__tensor_3_output",
+                    "Output_1_x"
+                  ],
+                  "outputs": [
+                    "Output_1_x"
+                  ],
+                  "trained": true
+                },
+                "type": "model"
+              }
+            },
+            "relations": []
+          }
+        ],
+        "meta": {
+          "color": "orange",
+          "doc": null,
+          "inputs": [
+            {
+              "name": "bundle",
+              "position": "left",
+              "type": {
+                "type": "<class 'lynxkite_graph_analytics.core.Bundle'>"
+              }
+            }
+          ],
+          "name": "View tables",
+          "outputs": [],
+          "params": [
+            {
+              "default": 100,
+              "name": "limit",
+              "type": {
+                "type": "<class 'int'>"
+              }
+            }
+          ],
+          "type": "table_view"
+        },
+        "params": {
+          "_tables_open": {
+            "df_test": true,
+            "df_train": false
+          },
+          "limit": 100.0
+        },
+        "status": "done",
+        "title": "View tables"
+      },
+      "dragHandle": ".bg-primary",
+      "height": 523.0,
+      "id": "View tables 1",
+      "position": {
+        "x": 2614.6287203681786,
+        "y": -389.3147778373818
+      },
+      "type": "table_view",
+      "width": 940.0
+    }
+  ]
+}

examples/Cheminformatics/chem_utils.py

@@ -6,18 +6,20 @@ from operator import itemgetter
 from typing import List
 from typing import Tuple
 import itertools
-import matplotlib.pyplot as plt
 import numpy as np
+import matplotlib.pyplot as plt
 import seaborn as sns
 from rdkit import Chem, DataStructs, RDLogger
 from rdkit.Chem.Draw import rdMolDraw2D
 from rdkit.Chem.rdchem import Mol
 from rdkit.ML.Cluster import Butina
 from rdkit.rdBase import BlockLogs
-
 import pandas as pd
 from rdkit.Chem.rdMMPA import FragmentMol
 from rdkit.Chem.rdRGroupDecomposition import RGroupDecompose
+import matplotlib
+
+matplotlib.use("agg")
 
 
 def smi2mol_with_errors(smi: str) -> Tuple[Mol, str]:

examples/Cheminformatics/cheminfo_tools.py

@@ -13,10 +13,15 @@ import math
 import io
 from rdkit.Chem import AllChem
 from sklearn.ensemble import RandomForestRegressor
+from rdkit.Chem.FilterCatalog import FilterCatalog, FilterCatalogParams
+from rdkit.Chem import rdMolDescriptors
 from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error
 from sklearn.model_selection import train_test_split
 import numpy as np
 from rdkit.Chem import MACCSkeys
+import matplotlib
+
+matplotlib.use("agg")
 
 
 @op("LynxKite Graph Analytics", "View mol filter", view="matplotlib", slow=True)
@@ -104,34 +109,195 @@ def mol_filter(
     plot_gallery(images, num_cols=mols_per_row)
 
 
-@op("LynxKite Graph Analytics", "Lipinski filter")
+@op("LynxKite Graph Analytics", "Lipinski filter", outputs=["pass", "fail"])
 def lipinski_filter(bundle, *, table_name: str, column_name: str, strict_lipinski: bool = True):
-    # copy bundle and get DataFrame
+    """
+    Apply the lipinski rule (MW <= 500,HBA <= 10 , HBD <=5,logP <= 5 ).
+    """
     bundle = bundle.copy()
     df = bundle.dfs[table_name].copy()
+    if df is None:
+        raise KeyError("Table not found")
+
+    # Parse SMILES into molecules, drop unparsable
     df["mol"] = df[column_name].apply(Chem.MolFromSmiles)
     df = df[df["mol"].notnull()].reset_index(drop=True)
 
-    # compute properties
+    # Compute Lipinski properties
     df["MW"] = df["mol"].apply(Descriptors.MolWt)
     df["logP"] = df["mol"].apply(Crippen.MolLogP)
     df["HBD"] = df["mol"].apply(Lipinski.NumHDonors)
     df["HBA"] = df["mol"].apply(Lipinski.NumHAcceptors)
 
-    # compute a boolean pass/fail for Lipinski
+    # Determine pass/fail
     df["pass_lipinski"] = (
         (df["MW"] <= 500) & (df["logP"] <= 5) & (df["HBD"] <= 5) & (df["HBA"] <= 10)
     )
-    df = df.drop("mol", axis=1)
 
-    # if strict_lipinski, drop those that fail
-    if strict_lipinski:
-        failed = df.loc[~df["pass_lipinski"], column_name].tolist()
-        df = df[df["pass_lipinski"]].reset_index(drop=True)
-        if failed:
-            print(f"Dropped {len(failed)} molecules that failed Lipinski: {failed}")
+    # Split out the two sets
+    pass_df = df[df["pass_lipinski"]].drop("mol", axis=1).reset_index(drop=True)
+    fail_df = df[~df["pass_lipinski"]].drop("mol", axis=1).reset_index(drop=True)
+
+    # return pass_df,fail_df
+    return {
+        "pass": pass_df,
+        "fail": fail_df,
+    }
+
+
+@op("LynxKite Graph Analytics", "Veber filter", outputs=["pass", "fail"])
+def veber_filter(bundle, *, table_name: str, column_name: str):
+    """
+    Apply the Veber rule (TPSA <= 140, rotatable bonds <= 10).
+    """
+    bundle = bundle.copy()
+    df = bundle.dfs[table_name].copy()
+    df["mol"] = df[column_name].apply(Chem.MolFromSmiles)
+    df = df[df["mol"].notnull()].reset_index(drop=True)
+    df["TPSA"] = df["mol"].apply(rdMolDescriptors.CalcTPSA)
+    df["RotB"] = df["mol"].apply(Lipinski.NumRotatableBonds)
+    df["pass_veber"] = (df["TPSA"] <= 140) & (df["RotB"] <= 10)
+
+    pass_df = df[df["pass_veber"]].drop("mol", axis=1).reset_index(drop=True)
+    fail_df = df[~df["pass_veber"]].drop("mol", axis=1).reset_index(drop=True)
+
+    # return df.drop(columns=['mol'])
+    return {
+        "pass": pass_df,
+        "fail": fail_df,
+    }
+
+
+@op("LynxKite Graph Analytics", "Ghose filter", outputs=["pass", "fail"])
+def ghose_filter(bundle, *, table_name: str, column_name: str, strict_ghose: bool = True):
+    """
+    Apply the Ghose filter:
+      - 160 <= MW <= 480
+      - -0.4 <= logP <= 5.6
+      - 40 <= MR <= 130
+      - 20 <= atom count <= 70
+    """
+    bundle = bundle.copy()
+    df = bundle.dfs[table_name].copy()
+    df["mol"] = df[column_name].apply(Chem.MolFromSmiles)
+    df = df[df["mol"].notnull()].reset_index(drop=True)
+    df["MW"] = df["mol"].apply(Descriptors.MolWt)
+    df["logP"] = df["mol"].apply(Crippen.MolLogP)
+    df["MR"] = df["mol"].apply(Descriptors.MolMR)
+    df["Atoms"] = df["mol"].apply(lambda m: m.GetNumAtoms())
+    df["pass_ghose"] = (
+        (df["MW"] >= 160)
+        & (df["MW"] <= 480)
+        & (df["logP"] >= -0.4)
+        & (df["logP"] <= 5.6)
+        & (df["MR"] >= 40)
+        & (df["MR"] <= 130)
+        & (df["Atoms"] >= 20)
+        & (df["Atoms"] <= 70)
+    )
+
+    pass_df = df[df["pass_ghose"]].drop("mol", axis=1).reset_index(drop=True)
+    fail_df = df[~df["pass_ghose"]].drop("mol", axis=1).reset_index(drop=True)
+
+    return {
+        "pass": pass_df,
+        "fail": fail_df,
+    }
+
+
+@op("LynxKite Graph Analytics", "Egan filter", outputs=["pass", "fail"])
+def egan_filter(bundle, *, table_name: str, column_name: str, strict_egan: bool = True):
+    """
+    Apply the Egan filter (logP <= 5.88, TPSA <= 131.6).
+    """
+    bundle = bundle.copy()
+    df = bundle.dfs[table_name].copy()
+    df["mol"] = df[column_name].apply(Chem.MolFromSmiles)
+    df = df[df["mol"].notnull()].reset_index(drop=True)
+    df["logP"] = df["mol"].apply(Crippen.MolLogP)
+    df["TPSA"] = df["mol"].apply(rdMolDescriptors.CalcTPSA)
+    df["pass_egan"] = (df["logP"] <= 5.88) & (df["TPSA"] <= 131.6)
+
+    pass_df = df[df["pass_egan"]].drop("mol", axis=1).reset_index(drop=True)
+    fail_df = df[~df["pass_egan"]].drop("mol", axis=1).reset_index(drop=True)
+
+    return {
+        "pass": pass_df,
+        "fail": fail_df,
+    }
+
+
+@op("LynxKite Graph Analytics", "Muegge filter", outputs=["pass", "fail"])
+def muegge_filter(bundle, *, table_name: str, column_name: str, strict_muegge: bool = True):
+    """
+    Apply the Muegge filter:
+      - 200 <= MW <= 600
+      - -2 <= logP <= 5
+      - TPSA <= 150
+      - rotatable bonds <= 15
+      - 3 <= ring count <= 7
+      - atom count <= 50
+    """
+    bundle = bundle.copy()
+    df = bundle.dfs[table_name].copy()
+    df["mol"] = df[column_name].apply(Chem.MolFromSmiles)
+    df = df[df["mol"].notnull()].reset_index(drop=True)
+    df["MW"] = df["mol"].apply(Descriptors.MolWt)
+    df["logP"] = df["mol"].apply(Crippen.MolLogP)
+    df["TPSA"] = df["mol"].apply(rdMolDescriptors.CalcTPSA)
+    df["RotB"] = df["mol"].apply(Lipinski.NumRotatableBonds)
+    df["Rings"] = df["mol"].apply(lambda m: len(m.GetRingInfo().AtomRings()))
+    df["Atoms"] = df["mol"].apply(lambda m: m.GetNumAtoms())
+    df["pass_muegge"] = (
+        (df["MW"] >= 200)
+        & (df["MW"] <= 600)
+        & (df["logP"] >= -2)
+        & (df["logP"] <= 5)
+        & (df["TPSA"] <= 150)
+        & (df["RotB"] <= 15)
+        & (df["Rings"] >= 3)
+        & (df["Rings"] <= 7)
+        & (df["Atoms"] <= 50)
+    )
+
+    pass_df = df[df["pass_muegge"]].drop("mol", axis=1).reset_index(drop=True)
+    fail_df = df[~df["pass_muegge"]].drop("mol", axis=1).reset_index(drop=True)
+
+    return {
+        "pass": pass_df,
+        "fail": fail_df,
+    }
+
+
+@op("LynxKite Graph Analytics", "Brenk Aggregator filter", outputs=["pass", "fail"])
+def brenk_aggregator_filter(
+    bundle, *, table_name: str, column_name: str, strict_brenk: bool = True
+):
+    """
+    Apply Brenk aggregator filters for known nuisance substructures.
+    """
+    bundle = bundle.copy()
+    df = bundle.dfs[table_name].copy()
+    df["mol"] = df[column_name].apply(Chem.MolFromSmiles)
+    df = df[df["mol"].notnull()].reset_index(drop=True)
+    params = FilterCatalogParams()
+    params.AddCatalog(FilterCatalogParams.FilterCatalogs.BRENK)
+    catalog = FilterCatalog(params)
+
+    def _get_brenk_alerts(mol):
+        entries = catalog.GetMatches(mol)
+        return [e.GetDescription() for e in entries] if entries else []
+
+    df["brenk_alerts"] = df["mol"].apply(_get_brenk_alerts)
+    df["pass_brenk"] = df["brenk_alerts"].apply(lambda x: len(x) == 0)
+
+    pass_df = df[df["pass_brenk"]].drop("mol", axis=1).reset_index(drop=True)
+    fail_df = df[~df["pass_brenk"]].drop("mol", axis=1).reset_index(drop=True)
 
-    return df
+    return {
+        "pass": pass_df,
+        "fail": fail_df,
+    }
 
 
 @op("LynxKite Graph Analytics", "View mol image", view="matplotlib", slow=True)

examples/Cheminformatics/qsar_example.lynxkite.json

@@ -1,12 +1,5 @@
 {
   "edges": [
-    {
-      "id": "Import CSV 1 output Train QSAR2 1 df",
-      "source": "Import CSV 1",
-      "sourceHandle": "output",
-      "target": "Train QSAR2 1",
-      "targetHandle": "df"
-    },
     {
       "id": "Train QSAR2 1 output plot qsar2 1 results_df",
       "source": "Train QSAR2 1",
@@ -20,6 +13,34 @@
       "sourceHandle": "output",
       "target": "plot qsar 1",
       "targetHandle": "results_df"
+    },
+    {
+      "id": "Lipinski filter 1 pass View tables 1 bundle",
+      "source": "Lipinski filter 1",
+      "sourceHandle": "pass",
+      "target": "View tables 1",
+      "targetHandle": "bundle"
+    },
+    {
+      "id": "Import file 1 output Train QSAR2 1 df",
+      "source": "Import file 1",
+      "sourceHandle": "output",
+      "target": "Train QSAR2 1",
+      "targetHandle": "df"
+    },
+    {
+      "id": "Import file 1 output Lipinski filter 1 bundle",
+      "source": "Import file 1",
+      "sourceHandle": "output",
+      "target": "Lipinski filter 1",
+      "targetHandle": "bundle"
+    },
+    {
+      "id": "Lipinski filter 1 fail View tables 2 bundle",
+      "source": "Lipinski filter 1",
+      "sourceHandle": "fail",
+      "target": "View tables 2",
+      "targetHandle": "bundle"
     }
   ],
   "env": "LynxKite Graph Analytics",
@@ -30,84 +51,90 @@
         "collapsed": null,
         "display": null,
         "error": null,
-        "input_metadata": [],
+        "input_metadata": [
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+        ],
         "meta": {
           "color": "orange",
           "doc": [
             {
               "kind": "text",
-              "value": "Imports a CSV file."
-            }
-          ],
-          "inputs": [],
-          "name": "Import CSV",
-          "outputs": [
-            {
-              "name": "output",
-              "position": "right",
-              "type": {
-                "type": "None"
-              }
-            }
-          ],
-          "params": [
-            {
-              "default": null,
-              "name": "filename",
-              "type": {
-                "type": "<class 'str'>"
-              }
+              "value": "Train/save RandomForest QSAR model, returning the model and a results DataFrame.\n\nThe results DataFrame contains per-point data ('actual', 'predicted',\n'lower_ci', 'upper_ci', 'split') AND repeated summary metrics for each\nsplit ('split_R2', 'split_MAE', 'split_RMSE')."
             },
             {
-              "default": "<from file>",
-              "name": "columns",
-              "type": {
-                "type": "<class 'str'>"
-              }
+              "kind": "parameters",
+              "value": [
+                {
+                  "annotation": "any",
+                  "description": "",
+                  "name": "bundle"
+                },
+                {
+                  "annotation": "str",
+                  "description": "",
+                  "name": "table_name"
+                },
+                {
+                  "annotation": "str",
+                  "description": "",
+                  "name": "smiles_col"
+                },
+                {
+                  "annotation": "str",
+                  "description": "",
+                  "name": "target_col"
+                },
+                {
+                  "annotation": "str",
+                  "description": "",
+                  "name": "fp_type"
+                },
+                {
+                  "annotation": "int",
+                  "description": "",
+                  "name": "radius"
+                },
+                {
+                  "annotation": "int",
+                  "description": "",
+                  "name": "n_bits"
+                },
+                {
+                  "annotation": "float",
+                  "description": "",
+                  "name": "test_size"
+                },
+                {
+                  "annotation": "int",
+                  "description": "",
+                  "name": "random_state"
+                },
+                {
+                  "annotation": "str",
+                  "description": "",
+                  "name": "out_dir"
+                },
+                {
+                  "annotation": "float",
+                  "description": "",
+                  "name": "confidence"
+                }
+              ]
             },
             {
-              "default": "<auto>",
-              "name": "separator",
-              "type": {
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-            }
-          ],
-          "type": "basic"
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-        "params": {
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-          "filename": "~/Github/lynxkite-2024/examples/uploads/CHEMBL313_sel.csv",
-          "separator": "<auto>"
-        },
-        "status": "done",
-        "title": "Import CSV"
-      },
-      "dragHandle": ".bg-primary",
-      "height": 334.0,
-      "id": "Import CSV 1",
-      "position": {
-        "x": 58.0,
-        "y": 59.0
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-      "type": "basic",
-      "width": 457.0
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-    {
-      "data": {
-        "__execution_delay": 0.0,
-        "collapsed": null,
-        "display": null,
-        "error": null,
-        "input_metadata": [
-          {}
-        ],
-        "meta": {
-          "color": "orange",
-          "doc": [
-            {
-              "kind": "text",
-              "value": "Train/save RandomForest QSAR model, returning the model and a results DataFrame.\n\nThe results DataFrame contains per-point data ('actual', 'predicted',\n'lower_ci', 'upper_ci', 'split') AND repeated summary metrics for each\nsplit ('split_R2', 'split_MAE', 'split_RMSE').\n\nParameters\n----------\n(Parameters are the same as before)\nbundle : any\ntable_name : str\nsmiles_col : str\ntarget_col : str\nfp_type : str\nradius : int\nn_bits : int\ntest_size : float\nrandom_state : int\nout_dir : str\nconfidence : float, optional\n\nReturns\n-------\nmodel : RandomForestRegressor\n    The trained QSAR model.\nresults_df : pandas.DataFrame\n    DataFrame containing columns: 'actual', 'predicted', 'lower_ci',\n    'upper_ci', 'split', 'split_R2', 'split_MAE', 'split_RMSE'.\n    The metric columns repeat the overall metric for the corresponding split."
+              "kind": "returns",
+              "value": [
+                {
+                  "annotation": "RandomForestRegressor",
+                  "description": "The trained QSAR model.",
+                  "name": "model"
+                },
+                {
+                  "annotation": "pandas.DataFrame",
+                  "description": "DataFrame containing columns: 'actual', 'predicted', 'lower_ci',\n'upper_ci', 'split', 'split_R2', 'split_MAE', 'split_RMSE'.\nThe metric columns repeat the overall metric for the corresponding split.",
+                  "name": "results_df"
+                }
+              ]
             }
           ],
           "inputs": [
@@ -201,13 +228,13 @@
           "fp_type": "maccs",
           "n_bits": 2048.0,
           "out_dir": "Models",
-          "radius": 2.0,
+          "radius": "2",
           "random_state": 42.0,
           "smiles_col": "SMILES",
           "target_col": "pIC50",
           "test_size": 0.2
         },
-        "status": "done",
+        "status": "planned",
         "title": "Train QSAR2"
       },
       "dragHandle": ".bg-primary",
@@ -232,7 +259,56 @@
           "doc": [
             {
               "kind": "text",
-              "value": "Plots actual vs. predicted values resembling the example image.\n\nIncludes separate markers for train/test, y=x line, and parallel dashed\nerror bands based on test set RMSE (optional). Does NOT use per-point CIs.\n\nHandles displaying the plot via plt.show() or saving it to a file\nbased on the `save_path` parameter. THIS FUNCTION DOES NOT RETURN ANY VALUE.\n\nParameters\n----------\nresults_df : pd.DataFrame\n    Mandatory input DataFrame. Must contain: 'actual', 'predicted', 'split'.\n    Should also contain 'split_RMSE' column for error bands and metrics display.\ntitle : str, optional\nxlabel : str, optional\nylabel : str, optional\nrmse_multiplier_for_bands : float or None, optional\n    Determines the width of the dashed error bands (multiplier * test_RMSE).\n    Set to None to disable bands. Default is 1.0.\nshow_metrics : bool, optional\n    Whether to display R2/MAE/RMSE text (requires metric columns). Default is True.\nsave_path : str, optional\n    If provided, saves plot to this path. If None (default), displays plot.\n\nRaises\n------\nValueError / TypeError : For invalid inputs."
+              "value": "Plots actual vs. predicted values resembling the example image.\n\nIncludes separate markers for train/test, y=x line, and parallel dashed\nerror bands based on test set RMSE (optional). Does NOT use per-point CIs.\n\nHandles displaying the plot via plt.show() or saving it to a file\nbased on the `save_path` parameter. THIS FUNCTION DOES NOT RETURN ANY VALUE."
+            },
+            {
+              "kind": "parameters",
+              "value": [
+                {
+                  "annotation": "pd.DataFrame",
+                  "description": "Mandatory input DataFrame. Must contain: 'actual', 'predicted', 'split'.\nShould also contain 'split_RMSE' column for error bands and metrics display.",
+                  "name": "results_df"
+                },
+                {
+                  "annotation": "str",
+                  "description": "",
+                  "name": "title"
+                },
+                {
+                  "annotation": "str",
+                  "description": "",
+                  "name": "xlabel"
+                },
+                {
+                  "annotation": "str",
+                  "description": "",
+                  "name": "ylabel"
+                },
+                {
+                  "annotation": "float or None",
+                  "description": "Determines the width of the dashed error bands (multiplier * test_RMSE).\nSet to None to disable bands. Default is 1.0.",
+                  "name": "rmse_multiplier_for_bands"
+                },
+                {
+                  "annotation": "bool",
+                  "description": "Whether to display R2/MAE/RMSE text (requires metric columns). Default is True.",
+                  "name": "show_metrics"
+                },
+                {
+                  "annotation": "str",
+                  "description": "If provided, saves plot to this path. If None (default), displays plot.",
+                  "name": "save_path"
+                }
+              ]
+            },
+            {
+              "kind": "raises",
+              "value": [
+                {
+                  "annotation": "ValueError / TypeError : For invalid inputs.",
+                  "description": ""
+                }
+              ]
             }
           ],
           "inputs": [
@@ -250,18 +326,18 @@
           "type": "image"
         },
         "params": {},
-        "status": "done",
+        "status": "planned",
         "title": "plot qsar2"
       },
       "dragHandle": ".bg-primary",
-      "height": 379.0,
+      "height": 283.0,
       "id": "plot qsar2 1",
       "position": {
-        "x": 1150.5,
-        "y": 57.5
+        "x": 1225.5,
+        "y": -35.5
       },
       "type": "image",
-      "width": 347.0
+      "width": 216.0
     },
     {
       "data": {
@@ -293,18 +369,1383 @@
           "type": "image"
         },
         "params": {},
-        "status": "done",
+        "status": "planned",
         "title": "plot qsar"
       },
       "dragHandle": ".bg-primary",
-      "height": 200.0,
+      "height": 228.0,
       "id": "plot qsar 1",
       "position": {
-        "x": 1284.5,
-        "y": 497.5
+        "x": 1295.3704288854306,
+        "y": 280.3626299614863
       },
       "type": "image",
+      "width": 255.0
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+    {
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+        "__execution_delay": 0.0,
+        "collapsed": false,
+        "display": null,
+        "error": "Missing input: bundle",
+        "input_metadata": null,
+        "meta": {
+          "color": "orange",
+          "doc": [
+            {
+              "kind": "text",
+              "value": "Apply the lipinski rule (MW <= 500,HBA <= 10 , HBD <=5,logP <= 5 )."
+            }
+          ],
+          "inputs": [
+            {
+              "name": "bundle",
+              "position": "left",
+              "type": {
+                "type": "<class 'inspect._empty'>"
+              }
+            }
+          ],
+          "name": "Lipinski filter",
+          "outputs": [
+            {
+              "name": "pass",
+              "position": "right",
+              "type": {
+                "type": "None"
+              }
+            },
+            {
+              "name": "fail",
+              "position": "right",
+              "type": {
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+          "params": [
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+              "name": "strict_lipinski",
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+          "type": "basic"
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+                  "description": "Format of the file. Has to be one of the values in the `FileFormat` enum.",
+                  "name": "file_format"
+                }
+              ]
+            },
+            {
+              "kind": "returns",
+              "value": [
+                {
+                  "annotation": null,
+                  "description": "Bundle with a single table with the contents of the file.",
+                  "name": "Bundle"
+                }
+              ]
+            }
+          ],
+          "inputs": [],
+          "name": "Import file",
+          "outputs": [
+            {
+              "name": "output",
+              "position": "right",
+              "type": {
+                "type": "None"
+              }
+            }
+          ],
+          "params": [
+            {
+              "default": null,
+              "name": "file_path",
+              "type": {
+                "type": "<class 'str'>"
+              }
+            },
+            {
+              "default": null,
+              "name": "table_name",
+              "type": {
+                "type": "<class 'str'>"
+              }
+            },
+            {
+              "default": null,
+              "name": "file_format",
+              "type": {
+                "enum": [
+                  "csv",
+                  "parquet",
+                  "json",
+                  "excel"
+                ]
+              }
+            },
+            {
+              "default": "csv",
+              "groups": {
+                "csv": [
+                  {
+                    "default": "<from file>",
+                    "name": "columns",
+                    "type": {
+                      "type": "<class 'str'>"
+                    }
+                  },
+                  {
+                    "default": "<auto>",
+                    "name": "separator",
+                    "type": {
+                      "type": "<class 'str'>"
+                    }
+                  }
+                ],
+                "excel": [
+                  {
+                    "default": "Sheet1",
+                    "name": "sheet_name",
+                    "type": {
+                      "type": "<class 'str'>"
+                    }
+                  }
+                ],
+                "json": [],
+                "parquet": []
+              },
+              "name": "file_format_group",
+              "selector": {
+                "default": "csv",
+                "name": "file_format",
+                "type": {
+                  "enum": [
+                    "csv",
+                    "parquet",
+                    "json",
+                    "excel"
+                  ]
+                }
+              },
+              "type": "group"
+            }
+          ],
+          "type": "basic"
+        },
+        "params": {
+          "file_format": null,
+          "file_format_group": "csv",
+          "file_path": "~/Github/lynxkite-2024/examples/uploads/CHEMBL313_sel.csv",
+          "table_name": "data"
+        },
+        "status": "done",
+        "title": "Import file"
+      },
+      "dragHandle": ".bg-primary",
+      "height": 496.0,
+      "id": "Import file 1",
+      "position": {
+        "x": 21.185214442715278,
+        "y": 163.93842286555287
+      },
+      "type": "basic",
+      "width": 434.0
     }
   ]
 }

examples/Cheminformatics/rcsb_api.py

@@ -0,0 +1,240 @@
+import matplotlib
+import pandas as pd
+import matplotlib.pyplot as plt
+import pypdb
+import biotite.database.rcsb as rcsb
+from MDAnalysis.analysis import rms
+from opencadd.structure.superposition.engines.mda import MDAnalysisAligner
+from lynxkite.core.ops import op
+import os
+import numpy as np
+from Bio.PDB import PDBList, PDBParser, Superimposer
+
+matplotlib.use("agg")
+
+
+def calc_rmsd(A, B):
+    """
+    Calculate RMSD between two structures.
+
+    Parameters
+    ----------
+    A : opencadd.structure.core.Structure
+        Structure A.
+    B : opencadd.structure.core.Structure
+        Structure B.
+
+    Returns
+    -------
+    float
+        RMSD value.
+    """
+    aligner = MDAnalysisAligner()
+    selection, _ = aligner.matching_selection(A, B)
+    A = A.select_atoms(selection["reference"])
+    B = B.select_atoms(selection["mobile"])
+    return rms.rmsd(A.positions, B.positions, superposition=False)
+
+
+def calc_rmsd_matrix(structures, names):
+    """
+    Calculate RMSD matrix between a list of structures.
+
+    Parameters
+    ----------
+    structures : list of opencadd.structure.core.Structure
+        List of structures.
+    names : list of str
+        List of structure names.
+
+    Returns
+    -------
+    pandas.DataFrame
+        RMSD matrix.
+    """
+    values = {name: {} for name in names}
+    for i, (A, name_i) in enumerate(zip(structures, names)):
+        for j, (B, name_j) in enumerate(zip(structures, names)):
+            if i == j:
+                values[name_i][name_j] = 0.0
+                continue
+            if i < j:
+                rmsd = calc_rmsd(A, B)
+                values[name_i][name_j] = rmsd
+                values[name_j][name_i] = rmsd
+                continue
+    df = pd.DataFrame.from_dict(values)
+    return df
+
+
+@op("LynxKite Graph Analytics", "pdb composite search")
+def get_pdb_count(
+    *, ligand_id: str, experimental_method: str, max_resolution: float, polymer_count: int
+):
+    """
+    Query the RCSB PDB for structures matching specified criteria and
+    return the list of matching PDB IDs.
+
+    Parameters
+    ----------
+    bundle : LynxKiteBundle
+        The workflow bundle (unused here, included for op compatibility).
+    ligand_id : str
+        Non-polymer component ID to filter on (e.g., 'STI').
+    experimental_method : str
+        Experimental method to filter by (e.g., 'X-RAY DIFFRACTION').
+    max_resolution : float
+        Maximum resolution (Å) to include (<= this value).
+    polymer_count : int
+        Exact number of polymer chains in the structure.
+
+    Returns
+    -------
+    List[str]
+        A list of PDB IDs matching all criteria.
+    """
+
+    # 1) Query by ligand ID
+    q_ligand = rcsb.FieldQuery(
+        "rcsb_nonpolymer_entity_container_identifiers.nonpolymer_comp_id", exact_match=ligand_id
+    )
+    count_ligand = rcsb.count(q_ligand)
+    print(f"Number of matches for ligand '{ligand_id}': {count_ligand}")
+
+    # 2) Query by experimental method
+    q_method = rcsb.FieldQuery("exptl.method", exact_match=experimental_method)
+    count_method = rcsb.count(q_method)
+    print(f"Number of matches for experimental method '{experimental_method}': {count_method}")
+
+    # 3) Query by resolution
+    q_resolution = rcsb.FieldQuery(
+        "rcsb_entry_info.resolution_combined", less_or_equal=max_resolution
+    )
+    count_resolution = rcsb.count(q_resolution)
+    print(f"Number of matches with resolution ≤ {max_resolution}: {count_resolution}")
+
+    # 4) Query by polymer chain count
+    q_polymer = rcsb.FieldQuery(
+        "rcsb_entry_info.deposited_polymer_entity_instance_count", equals=polymer_count
+    )
+    count_polymer = rcsb.count(q_polymer)
+    print(f"Number of matches with polymer count == {polymer_count}: {count_polymer}")
+
+    # 5) Composite query (AND all criteria)
+    composite_q = rcsb.CompositeQuery([q_ligand, q_method, q_resolution, q_polymer], "and")
+    pdb_ids = rcsb.search(composite_q)
+
+    # print(f"Number of composite matches: {len(pdb_ids)}")
+    # print("Selected PDB IDs:")
+    # print(*pdb_ids)
+    pdb_ids = rcsb.search(composite_q)
+    # Fetch PDBx descriptors
+    pdbs_info = [pypdb.get_all_info(pid) for pid in pdb_ids]
+    print(pdbs_info)
+    title = [pdb_info["struct"]["title"] for pdb_info in pdbs_info]
+
+    # Build DataFrame
+    return pd.DataFrame({"pdb_id": pdb_ids, "description": title})
+
+
+@op("LynxKite Graph Analytics", "PDB alignment rmsd")
+def compute_pdb_rmsd(df: pd.DataFrame, *, pdb_id_col: str = "pdb_id") -> pd.DataFrame:
+    """
+    Accepts a DataFrame with a column of PDB IDs, downloads PDB files,
+    selects Cα atoms from chain A (or first chain), superimposes using BioPython's
+    Superimposer, computes the RMSD matrix (only on common residues),
+    and returns it as a DataFrame.
+
+    Parameters
+    ----------
+    df : pd.DataFrame
+        Input DataFrame containing PDB IDs.
+    pdb_id_col : str
+        Name of the column in `df` with PDB IDs (default 'pdb_id').
+
+    Returns
+    -------
+    pd.DataFrame
+        Square DataFrame of pairwise RMSD values (Å), indexed and columned by PDB IDs.
+    """
+
+    # Prepare PDB directory
+    pdb_dir = "pdb_files"
+    os.makedirs(pdb_dir, exist_ok=True)
+
+    out = df.copy()
+    ids = out[pdb_id_col].tolist()
+    n = len(ids)
+
+    pdbl = PDBList()
+    parser = PDBParser(QUIET=True)
+    atom_dicts = []
+    for pid in ids:
+        pdbl.retrieve_pdb_file(pid, pdir=pdb_dir, file_format="pdb")
+        path = os.path.join(pdb_dir, f"pdb{pid.lower()}.ent")
+        struct = parser.get_structure(pid, path)
+        model = struct[0]
+        try:
+            chain = model["A"]
+        except KeyError:
+            chain = next(model.get_chains())
+        ca_atoms = {residue.id: residue["CA"] for residue in chain if residue.has_id("CA")}
+        atom_dicts.append(ca_atoms)
+
+    rmsd_mat = np.zeros((n, n))
+    sup = Superimposer()
+    for i in range(n):
+        for j in range(i + 1, n):
+            common = sorted(set(atom_dicts[i].keys()) & set(atom_dicts[j].keys()))
+            if not common:
+                rmsd = np.nan
+            else:
+                fixed_atoms = [atom_dicts[i][k] for k in common]
+                moving_atoms = [atom_dicts[j][k] for k in common]
+                sup.set_atoms(fixed_atoms, moving_atoms)
+                rmsd = sup.rms
+            rmsd_mat[i, j] = rmsd_mat[j, i] = round(rmsd, 1) if not np.isnan(rmsd) else np.nan
+
+    return pd.DataFrame(rmsd_mat, index=ids, columns=ids)
+
+
+@op("LynxKite Graph Analytics", "plot matrix", view="matplotlib")
+def plot_heatmap_from_df(
+    df: pd.DataFrame, *, value_label: str = "Value", title: str = None
+) -> plt.Figure:
+    """
+    Plot a heatmap of a square DataFrame using matplotlib.
+
+    Parameters
+    ----------
+    df : pd.DataFrame
+        Square DataFrame of values to plot.
+    value_label : str
+        Label for the color bar.
+    title : str, optional
+        Title for the plot.
+
+    Returns
+    -------
+    plt.Figure
+        The matplotlib Figure object containing the heatmap.
+    """
+    fig, ax = plt.subplots()
+    im = ax.imshow(df.values)
+    # create and label the colorbar
+    cbar = fig.colorbar(im, ax=ax)
+    cbar.set_label(value_label)
+
+    if title:
+        ax.set_title(title)
+    labels = df.index.tolist()
+    ax.set_xticks(range(len(labels)))
+    ax.set_xticklabels(labels, rotation=90)
+    ax.set_yticks(range(len(labels)))
+    ax.set_yticklabels(labels)
+    # Annotate each cell
+    for i in range(df.shape[0]):
+        for j in range(df.shape[1]):
+            ax.text(j, i, f"{df.iat[i, j]:.1f}", ha="center", va="center")
+    plt.tight_layout()
+    return fig

examples/Cheminformatics/requirements.txt

@@ -1,2 +1,10 @@
+biotite
+pypdb
+rdkit
+MDAnalysis
+redo
+https://github.com/volkamerlab/opencadd/archive/master.tar.gz
+seaborn
+=======
 chembl_webresource_client
 rcsb-api