Repository: datamol-io/datamol (language: python)

Issue:
Fix deprecation warning for `dm.to_fp`
https://github.com/rdkit/rdkit/pull/7004 added a deprecation warning for several functions related to Morgan Fingerprints.

Running 
```
dm.to_fp("CCCCC")
```

Gives: 
```
"please use MorganGenerator"
```


Discussion / hints:

Fix patch:
diff --git a/datamol/__init__.py b/datamol/__init__.py
index 79efda46..769cf80c 100644
--- a/datamol/__init__.py
+++ b/datamol/__init__.py
@@ -29,7 +29,7 @@
     "parallelized_with_batches": "datamol.utils",
     "JobRunner": "datamol.utils",
     "fs": "datamol.utils",
-    # log
+    # data
     "freesolv": "datamol.data",
     "cdk2": "datamol.data",
     "solubility": "datamol.data",
@@ -39,6 +39,7 @@
     "enable_rdkit_log": "datamol.log",
     "disable_rdkit_log": "datamol.log",
     "without_rdkit_log": "datamol.log",
+    "no_rdkit_log": "datamol.log",
     # mol
     "PERIODIC_TABLE": "datamol.mol",
     "TRIPLE_BOND": "datamol.mol",
@@ -233,6 +234,7 @@ def __dir__():
     from .log import enable_rdkit_log
     from .log import disable_rdkit_log
     from .log import without_rdkit_log
+    from .log import no_rdkit_log
 
     from .mol import PERIODIC_TABLE
     from .mol import TRIPLE_BOND
diff --git a/datamol/descriptors/descriptors.py b/datamol/descriptors/descriptors.py
index 6a26279e..ef6eb0e8 100644
--- a/datamol/descriptors/descriptors.py
+++ b/datamol/descriptors/descriptors.py
@@ -11,9 +11,11 @@
 
 from .. import Mol
 from ..convert import from_smarts
+from ..log import no_rdkit_log
 from .._version import is_lower_than_current_rdkit_version
 
 
+@no_rdkit_log
 def _sasscorer(mol: Mol):
     sys.path.append(os.path.join(RDConfig.RDContribDir, "SA_Score"))
     try:
diff --git a/datamol/fp.py b/datamol/fp.py
index cfb533a5..3cb863c8 100644
--- a/datamol/fp.py
+++ b/datamol/fp.py
@@ -12,6 +12,7 @@
 from rdkit.Chem import rdReducedGraphs
 from rdkit.Chem.EState import Fingerprinter as EStateFingerprinter
 from rdkit.Avalon import pyAvalonTools
+from rdkit.Chem import rdFingerprintGenerator
 
 from rdkit.DataStructs.cDataStructs import SparseBitVect
 from rdkit.DataStructs.cDataStructs import UIntSparseIntVect
@@ -20,25 +21,28 @@
 from rdkit.DataStructs.cDataStructs import ExplicitBitVect
 from rdkit.DataStructs.cDataStructs import ULongSparseIntVect
 
+_FP_GENERATORS = {
+    "ecfp": rdFingerprintGenerator.GetMorganGenerator,
+    "fcfp": rdFingerprintGenerator.GetMorganGenerator,
+    "topological": rdFingerprintGenerator.GetTopologicalTorsionGenerator,
+    "atompair": rdFingerprintGenerator.GetAtomPairGenerator,
+    "rdkit": rdFingerprintGenerator.GetRDKitFPGenerator,
+    "ecfp-count": rdFingerprintGenerator.GetMorganGenerator,
+    "fcfp-count": rdFingerprintGenerator.GetMorganGenerator,
+    "topological-count": rdFingerprintGenerator.GetTopologicalTorsionGenerator,
+    "atompair-count": rdFingerprintGenerator.GetAtomPairGenerator,
+    "rdkit-count": rdFingerprintGenerator.GetRDKitFPGenerator,
+}
 
 _FP_FUNCS = {
     "maccs": rdMolDescriptors.GetMACCSKeysFingerprint,
-    "ecfp": rdMolDescriptors.GetMorganFingerprintAsBitVect,
-    "fcfp": rdMolDescriptors.GetMorganFingerprintAsBitVect,
-    "topological": rdMolDescriptors.GetHashedTopologicalTorsionFingerprintAsBitVect,
-    "atompair": rdMolDescriptors.GetHashedAtomPairFingerprintAsBitVect,
-    "rdkit": rdmolops.RDKFingerprint,
     "pattern": rdmolops.PatternFingerprint,
     "layered": rdmolops.LayeredFingerprint,
     "erg": rdReducedGraphs.GetErGFingerprint,
     # NOTE(hadim): bad for pickling?
     "estate": lambda x, **args: EStateFingerprinter.FingerprintMol(x)[0],
     "avalon-count": pyAvalonTools.GetAvalonCountFP,
-    "rdkit-count": rdmolops.UnfoldedRDKFingerprintCountBased,
-    "ecfp-count": rdMolDescriptors.GetHashedMorganFingerprint,
-    "fcfp-count": rdMolDescriptors.GetHashedMorganFingerprint,
-    "topological-count": rdMolDescriptors.GetHashedTopologicalTorsionFingerprint,
-    "atompair-count": rdMolDescriptors.GetHashedAtomPairFingerprint,
+    **_FP_GENERATORS,
 }
 
 _FP_DEFAULT_ARGS = {
@@ -51,59 +55,60 @@
     },
     "ecfp": {
         "radius": 3,  # ECFP6 - not the RDKit default (ECFP4)
-        "nBits": 2048,
-        "invariants": [],
-        "fromAtoms": [],
-        "useChirality": False,
+        "fpSize": 2048,
+        "includeChirality": False,
         "useBondTypes": True,
-        "useFeatures": False,
+        "countSimulation": False,
+        "countBounds": None,
+        "atomInvariantsGenerator": None,
+        "bondInvariantsGenerator": None,
     },
     "fcfp": {
-        "radius": 2,  # FCFP4
-        "nBits": 2048,
-        "invariants": [],  # you may want to provide features invariance
-        "fromAtoms": [],
-        "useChirality": False,
+        "radius": 2,
+        "fpSize": 2048,
+        "includeChirality": False,
         "useBondTypes": True,
-        "useFeatures": True,
+        "countSimulation": False,
+        "countBounds": None,
+        "atomInvariantsGenerator": rdFingerprintGenerator.GetMorganFeatureAtomInvGen(),
+        "bondInvariantsGenerator": None,
     },
     "topological": {
-        "nBits": 2048,
-        "targetSize": 4,
-        "fromAtoms": 0,
-        "ignoreAtoms": 0,
-        "atomInvariants": 0,
-        "nBitsPerEntry": 4,
         "includeChirality": False,
+        "torsionAtomCount": 4,
+        "countSimulation": True,
+        "countBounds": None,
+        "fpSize": 2048,
+        "atomInvariantsGenerator": None,
     },
     "atompair": {
-        "nBits": 2048,
-        "minLength": 1,
-        "maxLength": 30,
-        "fromAtoms": 0,
-        "ignoreAtoms": 0,
-        "atomInvariants": 0,
-        "nBitsPerEntry": 4,
+        "minDistance": 1,
+        "maxDistance": 30,
         "includeChirality": False,
         "use2D": True,
-        "confId": -1,
+        "countSimulation": True,
+        "countBounds": None,
+        "fpSize": 2048,
+        "atomInvariantsGenerator": None,
     },
     "rdkit": {
         "minPath": 1,
         "maxPath": 7,
-        "fpSize": 2048,
-        "nBitsPerHash": 2,
         "useHs": True,
-        "tgtDensity": 0.0,
-        "minSize": 128,
         "branchedPaths": True,
         "useBondOrder": True,
-        "atomInvariants": 0,
-        "fromAtoms": 0,
-        "atomBits": None,
-        "bitInfo": None,
+        "countSimulation": False,
+        "countBounds": None,
+        "fpSize": 2048,
+        "numBitsPerFeature": 2,
+        "atomInvariantsGenerator": None,
+    },
+    "pattern": {
+        "fpSize": 2048,
+        "atomCounts": [],
+        "setOnlyBits": None,
+        "tautomerFingerprints": False,
     },
-    "pattern": {"fpSize": 2048, "atomCounts": [], "setOnlyBits": None},
     "layered": {
         "fpSize": 2048,
         "minPath": 1,
@@ -126,38 +131,48 @@
     "erg": {"atomTypes": 0, "fuzzIncrement": 0.3, "minPath": 1, "maxPath": 15},
     "estate": {},
     # COUNTING FP
+    "avalon-count": {
+        "nBits": 512,
+        "isQuery": False,
+        "bitFlags": pyAvalonTools.avalonSimilarityBits,
+    },
     "ecfp-count": {
-        "radius": 2,  # ECFP4
-        "nBits": 2048,
-        "invariants": [],
-        "fromAtoms": [],
-        "useChirality": False,
+        "radius": 3,  # ECFP6 - not the RDKit default (ECFP4)
+        "fpSize": 2048,
+        "includeChirality": False,
         "useBondTypes": True,
-        "useFeatures": False,
-        "includeRedundantEnvironments": False,
+        "countSimulation": False,
+        "countBounds": None,
+        "atomInvariantsGenerator": None,
+        "bondInvariantsGenerator": None,
     },
     "fcfp-count": {
-        "radius": 2,  # FCFP4
-        "nBits": 2048,
-        "invariants": [],  # you may want to provide features invariance
-        "fromAtoms": [],
-        "useChirality": False,
+        "radius": 2,
+        "fpSize": 2048,
+        "includeChirality": False,
         "useBondTypes": True,
-        "useFeatures": True,
-        "includeRedundantEnvironments": False,
+        "countSimulation": False,
+        "countBounds": None,
+        "atomInvariantsGenerator": rdFingerprintGenerator.GetMorganFeatureAtomInvGen(),
+        "bondInvariantsGenerator": None,
     },
     "topological-count": {
-        "nBits": 2048,
-        "targetSize": 4,
-        "fromAtoms": 0,
-        "ignoreAtoms": 0,
-        "atomInvariants": 0,
         "includeChirality": False,
+        "torsionAtomCount": 4,
+        "countSimulation": True,
+        "countBounds": None,
+        "fpSize": 2048,
+        "atomInvariantsGenerator": None,
     },
-    "avalon-count": {
-        "nBits": 512,
-        "isQuery": False,
-        "bitFlags": pyAvalonTools.avalonSimilarityBits,
+    "atompair-count": {
+        "minDistance": 1,
+        "maxDistance": 30,
+        "includeChirality": False,
+        "use2D": True,
+        "countSimulation": True,
+        "countBounds": None,
+        "fpSize": 2048,
+        "atomInvariantsGenerator": None,
     },
     "rdkit-count": {
         "minPath": 1,
@@ -165,21 +180,11 @@
         "useHs": True,
         "branchedPaths": True,
         "useBondOrder": True,
-        "atomInvariants": 0,
-        "fromAtoms": 0,
-        "atomBits": None,
-        "bitInfo": None,
-    },
-    "atompair-count": {
-        "nBits": 2048,
-        "minLength": 1,
-        "maxLength": 30,
-        "fromAtoms": 0,
-        "ignoreAtoms": 0,
-        "atomInvariants": 0,
-        "includeChirality": False,
-        "use2D": True,
-        "confId": -1,
+        "countSimulation": False,
+        "countBounds": None,
+        "fpSize": 2048,
+        "numBitsPerFeature": 1,
+        "atomInvariantsGenerator": None,
     },
 }
 
@@ -279,7 +284,14 @@ def to_fp(
         fp_args.setdefault(key, value)
 
     # Compute the fingerprint
-    fp = fp_func(mol, **fp_args)
+    if fp_type in _FP_GENERATORS:
+        fp_func = fp_func(**fp_args)
+        if fp_type.endswith("-count"):
+            fp = fp_func.GetCountFingerprint(mol)
+        else:
+            fp = fp_func.GetFingerprint(mol)
+    else:
+        fp = fp_func(mol, **fp_args)
 
     # Fold the fp if needed.
     if fold_size is not None:
diff --git a/datamol/log.py b/datamol/log.py
index 0734741c..0d6697cd 100644
--- a/datamol/log.py
+++ b/datamol/log.py
@@ -1,5 +1,6 @@
 from rdkit import RDLogger
 from rdkit import rdBase
+from functools import wraps
 
 
 class without_rdkit_log:
@@ -71,3 +72,53 @@ def enable_rdkit_log():
     """Enable all rdkit logs."""
     for log_level in RDLogger._levels:
         rdBase.EnableLog(log_level)
+
+
+def no_rdkit_log(
+    func=None,
+    *,
+    mute_errors: bool = True,
+    mute_warning: bool = True,
+    mute_info: bool = True,
+    mute_debug: bool = True,
+    enable: bool = True,
+):
+    """Decorator to disable RDKit logs.
+
+    This decorator can be used to suppress RDKit logs when executing a specific function.
+    By default, all log levels (error, warning, info, and debug) are muted.
+
+    Args:
+        mute_errors : Whether to mute error logs (default is True).
+        mute_warning : Whether to mute warning logs (default is True).
+        mute_info : Whether to mute info logs (default is True).
+        mute_debug : Whether to mute debug logs (default is True).
+        enable: Whether to enable the log muting (default is True). If set to False, no logs will be muted.
+
+    Example:
+    ```python
+    @no_rdkit_log()
+    def example_function():
+        # Your function code here
+        pass
+
+    example_function()  # RDKit logs won't show during this function's execution
+    ```
+    """
+
+    if func is None:
+        return lambda f: no_rdkit_log(
+            f,
+            mute_errors=mute_errors,
+            mute_warning=mute_warning,
+            mute_info=mute_info,
+            mute_debug=mute_debug,
+            enable=enable,
+        )
+
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+        with without_rdkit_log(mute_errors, mute_warning, mute_info, mute_debug, enable):
+            return func(*args, **kwargs)
+
+    return wrapper
diff --git a/docs/tutorials/Descriptors.ipynb b/docs/tutorials/Descriptors.ipynb
index 8e7e54fd..3752e354 100644
--- a/docs/tutorials/Descriptors.ipynb
+++ b/docs/tutorials/Descriptors.ipynb
@@ -50,9 +50,380 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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      },
      "execution_count": 1,
@@ -90,9 +461,9 @@
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",
+      "image/png": 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",
       "text/plain": [
-       "<rdkit.Chem.rdchem.Mol at 0x7f69e7664280>"
+       "<rdkit.Chem.rdchem.Mol at 0x14e0dd700>"
       ]
      },
      "execution_count": 2,
@@ -127,7 +498,7 @@
        " 'n_rotatable_bonds': 1,\n",
        " 'n_radical_electrons': 0,\n",
        " 'tpsa': 71.66999999999999,\n",
-       " 'qed': 0.7539078378657419,\n",
+       " 'qed': 0.7539078378657418,\n",
        " 'clogp': 0.7626199999999999,\n",
        " 'sas': 2.5248498164613675,\n",
        " 'n_aliphatic_carbocycles': 0,\n",
@@ -987,7 +1358,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 6,
    "id": "d0dcddbb-c524-4215-b64e-031aba10b5e4",
    "metadata": {},
    "outputs": [
@@ -997,7 +1368,7 @@
        "0"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1008,23 +1379,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "dc9f95ed",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "<AxesSubplot: xlabel='clogp', ylabel='Count'>"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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0wQcfSJIGDhyo1atXO54/YcIENWnSREVFRZKkgoICWSwWHT9+3CvHAwAA4ElJSUn69a9/rXnz5qlt27aKjIxUWlqay+3cfpuDnJwcWSwW/eUvf1H//v3VvHlzDRkyREePHnU85/bbXZXfjuoPf/iDunTporCwMD300EMqLi527PPf//3fSkhIUHBwsNq1a6cxY8bo6tWrlcaUl5en9u3ba/ny5U7tu+Lll19WVFSU2rVrp5kzZ+r777932l5cXKyHH35YLVu2VHR0tF599VWn7WvWrFFCQoJatGihTp06acaMGbpy5YpLMQAAANyK/K167uZv+fn5GjlypJo3b64+ffooKyvLpd8PAABgFmVlZVq5cqW6d++uoKAgxcTEOPKs2+Xm5mrw4MEKCgpSVFSUFixYoOvXrzu2FxcX65FHHlGLFi0UFRWl3/zmN0pKSlJKSopjny5duujFF1+sNtcCgOpQ5AYAkjIzMxUeHq79+/dr9uzZevLJJzVp0iQNGzZMhw4d0o9//GNNmzZNJSUlSkpKUk5OjiTJMAzt3r1bbdq00Z49eyRJ2dnZioyMVK9evbx4RAAAAJ6TmZmpFi1a6JNPPtGqVau0dOlSj32YN3fuXL388ss6cOCA2rdvr5/85CcVPmi81alTp/Tuu+/qz3/+s/785z8rNzdXK1askCRdvHhRU6ZM0fTp02W1WpWTk6OJEyfKMIwK7eTk5Gj06NFasmSJFi1aVKfYs7OzderUKWVnZyszM1ObN2/W5s2bnfZ56aWX1K9fPx06dEgLFy7UU0895dR3AQEB+u1vf6vPP/9cmZmZ2rlzp+bNm1eneAAAAMqRv1XO3fytrKxMEydOVGBgoPbt26fXX39d8+fPr1MsAAAA3rZw4UKtXLlSzz//vL744gtt2bJFERERFfa7cOGCxo8fr7vuukuffvqpNmzYoDfeeEPLli1z7JOamqqPP/5Y27dvV1ZWlnbv3q1Dhw5VaKumuTIAqA63KwUASf3799dzzz0n6YeEbsWKFQoPD9cvf/lLSdILL7ygDRs26LPPPlNSUpLeeOMNlZWV6ejRowoMDNTUqVOVk5Oj8ePHKycnR6NGjfLm4QAAAHhUv379tHjxYklSjx49tG7dOu3YsUP33nuv220vXrzY0U5mZqY6duyobdu26ec//3ml+5eVlWnz5s0KDQ2VJE2bNk07duzQ8uXLdfHiRV2/fl0TJ05U586dJUkJCQkV2njvvfc0bdo0/e53v9OUKVPqHHubNm20bt06BQYGqnfv3rrvvvu0Y8cORw4pScOHD9eCBQskST179tTHH3+s3/zmN45jvvXbrLGxsXrxxRf15JNPav369XWOCwAAgPytcu7mb//7v/8rq9Wqs2fPqmPHjpKk9PR0jRs3rs4xAQAAeENxcbHWrl2rdevWKTk5WZLUrVs3jRgxQmfPnnXad/369erUqZPWrVsni8Wi3r176+uvv9b8+fP1wgsv6OrVq8rMzNSWLVs0evRoSdKmTZsUHR1d4ffWNFcGANXhSm4AoB8m/soFBgaqXbt2ThNq5d9aKCws1MiRI1VcXKzDhw8rNzdXo0aN0j333KPc3FxJosgNAAA0OrfmSpIUFRWlwsJCj7SdmJjo+Llt27bq1auXrFZrlft36dLF8QHp7bH0799fo0ePVkJCgiZNmqTf//73unTpktPzP/nkE/3sZz9TZmamWx+QSlLfvn0VGBhYaSyVHV/541uPLzs7W/fee686dOig0NBQPfroo/r222+rvEUXAABAbZC/Vc7d/M1qtSomJsZR4FbZ/gAAAL7AarWqtLTUUZRW076JiYmyWCyOdcOHD9eVK1d0/vx5nT59Wt9//70GDx7s2B4WFlbpXa9qmisDgOpQ5AYAkpo2ber02GKxOK0rT9rKysoUFhamO++8Uzk5OcrNzVVSUpLuvvtuHTlyRPn5+Tpx4oSSkpIaMnwAAIB6VVmuVFZWVm+/79YJM1diCQwMVFZWlj744AP16dNHr776qnr16qUzZ8449u/WrZt69+6t//iP/9C1a9fcirOu/VJ+fF999ZXGjx+v+Ph4/elPf1JeXp5ee+01Sar2ll8AAAA1IX9zPZbqlB9fZbdRre7YAQAAzCo4OLjW+xqGUSHnKc+LLBaL08+V7VMT8ikAtUWRGwDUQVJSkrKzs7Vr1y4lJSWpdevW6tOnj5YtW6b27dsrLi7O2yECAAD4hH379jl+vnTpkk6cOKHevXvXuT2LxaLhw4dryZIlOnz4sJo1a6Zt27Y5toeHh2vnzp06deqUJk+eXO/FZLceX/nj8uM7ePCgrl+/rtWrV2vo0KHq2bOnvv7663qNBwAAwF3+nL/16dNHNpvNKWf7v//7v3qNBwAAoD706NFDwcHB2rFjR4379unTR3v37nUqWtu7d69CQ0PVoUMHdevWTU2bNtX+/fsd24uKipSfn1+hrepyLQCoCUVuAFAHSUlJ+vDDD2WxWNSnTx/HurfeeotblQIAALhg6dKl2rFjhz7//HM99thjCg8P14QJE+rU1ieffKL09HQdPHhQNptN77zzjv72t79V+AJC+/bttXPnTn355ZeaMmWKrl+/7oEjqdzHH3+sVatW6cSJE3rttdf0X//1X5ozZ46kH65Kcv36db366qs6ffq0/vCHP+j111+vt1gAAAA8wZ/ztzFjxqhXr1569NFH9emnn2r37t1atGhRvcUCAABQX5o3b6758+dr3rx5evPNN3Xq1Cnt27dPb7zxRoV9Z8yYoXPnzmn27Nn68ssv9d5772nx4sVKTU1VQECAQkNDlZycrLlz5yo7O1vHjh3T9OnTFRAQUOEqbdXlWgBQE4rcAKAORo4cKUkaNWqUIzkbNWqUbty4QZEbAACAC1asWKE5c+Zo4MCBunjxorZv365mzZrVqa1WrVpp165dGj9+vHr27KnnnntOq1ev1rhx4yrsGxkZqZ07d+ro0aN65JFHdOPGDXcPpVJPP/208vLyNGDAAL344otavXq1fvzjH0uS7rzzTq1Zs0YrV65UfHy83nrrLWVkZNRLHAAAAJ7iz/lbQECAtm3bptLSUg0ePFi/+MUvtHz58nqJAwAAoL49//zzevrpp/XCCy8oLi5OkydPVmFhYYX9OnTooPfff1/79+9X//799cQTT+jxxx/Xc88959hnzZo1SkxM1P33368xY8Zo+PDhiouLU/PmzZ3aqi7XAoCaWIza3ggZAAAAAAAPycnJ0T333KNLly6pdevW3g4HAAAANSB/AwAAQG1dvXpVHTp00OrVq/X4449Lkrp06aKUlBSlpKR4NzgAPquJtwMAAAAAAAAAAAAAAACAbzp8+LC+/PJLDR48WJcvX9bSpUslSQ8++KCXIwPQmHC7UgAAAACAy9566y21bNmy0qVv377eDs9tVR1by5YttXv3bm+HBwAA4DLyNwAAANSnl19+Wf3799eYMWN09epV7d69W+Hh4d4OC0Ajwu1KAQAAAAAuKy4u1jfffFPptqZNm6pz584NHJFnnTx5ssptHTp0UHBwcANGAwAA4D7yN/I3AAAAAPBlFLkBAAAAAAAAAAAAAAAAAEyL25UCAAAAAAAAAAAAAAAAAEyLIjcAAAAAAAAAAAAAAAAAgGlR5AYAAAAAAAAAAAAAAAAAMC2K3AAAAAAAAAAAAAAAAAAApkWRGwAAAAAAAAAAAAAAAADAtChyAwAAAAAAAAAAAAAAAACYFkVuAAAAAAAAAAAAAAAAAADTosgNAAAAAAAAAAAAAAAAAGBa/w9qHAkpVvHnxgAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 2500x400 with 4 Axes>"
       ]
@@ -1038,15 +1399,32 @@
     "# Plotting the RO5 descriptors\n",
     "\n",
     "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
     "\n",
     "fig, axs = plt.subplots(ncols=4, figsize=(25, 4))\n",
     "axs = axs.flatten()\n",
     "\n",
-    "sns.histplot(df, x=\"mw\", ax=axs[0])\n",
-    "sns.histplot(df, x=\"n_lipinski_hba\", ax=axs[1])\n",
-    "sns.histplot(df, x=\"n_lipinski_hbd\", ax=axs[2])\n",
-    "sns.histplot(df, x=\"clogp\", ax=axs[3])"
+    "axs[0].hist(df[\"mw\"], bins=30, color='lightblue', edgecolor='black')\n",
+    "axs[0].set_title('Molecular Weight')\n",
+    "axs[0].set_xlabel('mw')\n",
+    "axs[0].set_ylabel('Frequency')\n",
+    "\n",
+    "axs[1].hist(df[\"n_lipinski_hba\"], bins=30, color='lightblue', edgecolor='black')\n",
+    "axs[1].set_title('Number of Lipinski HBA')\n",
+    "axs[1].set_xlabel('n_lipinski_hba')\n",
+    "axs[1].set_ylabel('Frequency')\n",
+    "\n",
+    "axs[2].hist(df[\"n_lipinski_hbd\"], bins=30, color='lightblue', edgecolor='black')\n",
+    "axs[2].set_title('Number of Lipinski HBD')\n",
+    "axs[2].set_xlabel('n_lipinski_hbd')\n",
+    "axs[2].set_ylabel('Frequency')\n",
+    "\n",
+    "axs[3].hist(df[\"clogp\"], bins=30, color='lightblue', edgecolor='black')\n",
+    "axs[3].set_title('CLogP')\n",
+    "axs[3].set_xlabel('clogp')\n",
+    "axs[3].set_ylabel('Frequency')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
    ]
   },
   {
@@ -1100,9 +1478,9 @@
    "notebook_metadata_filter": "-all"
   },
   "kernelspec": {
-   "display_name": "Python [conda env:datamol]",
+   "display_name": "datamol",
    "language": "python",
-   "name": "conda-env-datamol-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1114,7 +1492,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.10.14"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
diff --git a/env.yml b/env.yml
index 4f935845..2e1bbffe 100644
--- a/env.yml
+++ b/env.yml
@@ -24,7 +24,7 @@ dependencies:
   - scikit-learn
 
   # Chemistry
-  - rdkit >=2021.03
+  - rdkit
   - selfies
 
   # Optional deps
@@ -46,7 +46,7 @@ dependencies:
   - nbconvert
 
   # Doc
-  - mkdocs
+  - mkdocs <1.6
   - mkdocs-material >=7.1.1
   - mkdocs-material-extensions
   - mkdocstrings


Test patch:
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index 3628512b..4407e2ad 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -18,7 +18,7 @@ jobs:
       matrix:
         python-version: ["3.10", "3.11"]
         os: ["ubuntu-latest", "macos-latest", "windows-latest"]
-        rdkit-version: ["2023.03", "2023.09"]
+        rdkit-version: ["2023.09", "2024.03"]
 
     runs-on: ${{ matrix.os }}
     timeout-minutes: 30
diff --git a/tests/test_fp.py b/tests/test_fp.py
index cad12b1a..ebf54df6 100644
--- a/tests/test_fp.py
+++ b/tests/test_fp.py
@@ -66,7 +66,7 @@ def test_all_fps():
         "erg": {"size": 315, "bits_sum": 23.4},
         "estate": {"size": 79, "bits_sum": 13},
         "avalon-count": {"size": 512, "bits_sum": 168},
-        "ecfp-count": {"size": 2048, "bits_sum": 35},
+        "ecfp-count": {"size": 2048, "bits_sum": 42},
         "fcfp-count": {"size": 2048, "bits_sum": 35},
         "topological-count": {"size": 2048, "bits_sum": 19},
         "atompair-count": {"size": 2048, "bits_sum": 78},
diff --git a/tests/test_log.py b/tests/test_log.py
index a9988ca1..ca52ca0f 100644
--- a/tests/test_log.py
+++ b/tests/test_log.py
@@ -3,6 +3,11 @@
 import datamol as dm
 
 
+@dm.no_rdkit_log
+def no_log_to_mol(smiles):
+    return dm.to_mol(smiles)
+
+
 def check_logs_are_shown(capfd):
     smiles = "fake_smiles"
     dm.to_mol(smiles)
@@ -17,10 +22,20 @@ def check_logs_are_not_shown(capfd):
     assert err == ""
 
 
+def check_logs_are_not_shown_deco(capfd):
+    smiles = "fake_smiles"
+    no_log_to_mol(smiles)
+    _, err = capfd.readouterr()
+    assert err == ""
+
+
 @pytest.mark.skip_platform("win")
 def test_rdkit_log(capfd):
     """Test multiple rdkit log scenarios."""
 
+    check_logs_are_shown(capfd)
+    check_logs_are_not_shown_deco(capfd)
+
     check_logs_are_shown(capfd)
     with dm.without_rdkit_log():
         check_logs_are_not_shown(capfd)