5 rows × 34 columns
\n", + "5 rows × 35 columns
\n", "" ], "text/plain": [ @@ -204,35 +204,35 @@ "3 FNKQAGMHNFFSEI-DTTPTBRMSA-N 1528.363 104 \n", "4 PXVFFBGSTYQHRO-REQIQPEASA-N 1542.390 105 \n", "\n", - " Ring Count Rotatable Bond Count ... PDB Name Assay (DC50/Dmax) \\\n", - "0 10 24 ... NaN NaN NaN \n", - "1 10 25 ... NaN NaN NaN \n", - "2 10 26 ... NaN NaN NaN \n", - "3 10 27 ... NaN NaN NaN \n", - "4 10 28 ... NaN NaN NaN \n", + " Ring Count Rotatable Bond Count ... Name Assay (DC50/Dmax) Exact Mass \\\n", + "0 10 24 ... NaN NaN NaN \n", + "1 10 25 ... NaN NaN NaN \n", + "2 10 26 ... NaN NaN NaN \n", + "3 10 27 ... NaN NaN NaN \n", + "4 10 28 ... NaN NaN NaN \n", "\n", - " Exact Mass XLogP3 Target (Parsed) \\\n", - "0 NaN NaN NaN \n", - "1 NaN NaN NaN \n", - "2 NaN NaN NaN \n", - "3 NaN NaN NaN \n", - "4 NaN NaN NaN \n", + " XLogP3 Target (Parsed) POI Sequence \\\n", + "0 NaN NaN MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME... \n", + "1 NaN NaN MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME... \n", + "2 NaN NaN MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME... \n", + "3 NaN NaN MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME... \n", + "4 NaN NaN MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME... \n", "\n", - " POI Sequence E3 Ligase Uniprot \\\n", - "0 MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME... P40337 \n", - "1 MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME... P40337 \n", - "2 MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME... P40337 \n", - "3 MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME... P40337 \n", - "4 MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME... P40337 \n", + " E3 Ligase Uniprot E3 Ligase Sequence \\\n", + "0 P40337 MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE... \n", + "1 P40337 MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE... \n", + "2 P40337 MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE... \n", + "3 P40337 MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE... \n", + "4 P40337 MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE... \n", "\n", - " E3 Ligase Sequence Cell Line Identifier \n", - "0 MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE... MOLT-4 \n", - "1 MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE... MOLT-4 \n", - "2 MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE... MOLT-4 \n", - "3 MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE... MOLT-4 \n", - "4 MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE... MOLT-4 \n", + " Cell Line Identifier Active - OR \n", + "0 MOLT-4 NaN \n", + "1 MOLT-4 NaN \n", + "2 MOLT-4 NaN \n", + "3 MOLT-4 NaN \n", + "4 MOLT-4 True \n", "\n", - "[5 rows x 34 columns]" + "[5 rows x 35 columns]" ] }, "execution_count": 1, @@ -249,7 +249,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -334,7 +334,7 @@ " '10.1200/JCO.2019.37.7_suppl.259'], dtype=object)" ] }, - "execution_count": 88, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -347,7 +347,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -357,7 +357,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -371,11 +371,11 @@ " 'Article DOI', 'Comments', 'Database', 'Molecular Formula', 'cLogP',\n", " 'Target', 'PDB', 'Name', 'Assay (DC50/Dmax)', 'Exact Mass', 'XLogP3',\n", " 'Target (Parsed)', 'POI Sequence', 'E3 Ligase Uniprot',\n", - " 'E3 Ligase Sequence', 'Cell Line Identifier'],\n", + " 'E3 Ligase Sequence', 'Cell Line Identifier', 'Active - OR'],\n", " dtype='object')" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -386,7 +386,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -404,7 +404,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -422,7 +422,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -456,7 +456,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -473,7 +473,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -486,7 +486,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f1b180873a324a8cabaa1c79f1c9852e", + "model_id": "29e1fa4a91504c32a0c3634ece8253ec", "version_major": 2, "version_minor": 0 }, @@ -551,7 +551,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -573,7 +573,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -593,13 +593,13 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "464d252d34af4183923c13ac873b332f", + "model_id": "747b30bbe4304e19bc58d3ba7662e5c4", "version_major": 2, "version_minor": 0 }, @@ -632,13 +632,13 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "73fc24213f574597b74a3d239f09d17d", + "model_id": "296eec57cabc4c5ab8f92c6173c79598", "version_major": 2, "version_minor": 0 }, @@ -663,7 +663,7 @@ "Name: Avg Tanimoto, dtype: float64" ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -690,7 +690,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -719,7 +719,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -735,28 +735,73 @@ }, { "cell_type": "code", - "execution_count": 167, + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "RUN_DIMENSIONALITY_REDUCTION = False" + ] + }, + { + "cell_type": "code", + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ - "# import defaultdict from collections\n", "from collections import defaultdict\n", "\n", - "protac_data = []\n", - "protac_labels = defaultdict(list)\n", - "for _, row in protac_df.iterrows():\n", - " protac_data.append(\n", - " np.concatenate([\n", - " protein_embeddings[row['Uniprot']],\n", - " protein_embeddings[row['E3 Ligase Uniprot']],\n", - " cell2embedding[row['Cell Line Identifier']],\n", - " smiles2fp[row['Smiles']],\n", - " # [row['Treatment Time (h)']] if not pd.isna(row['Treatment Time (h)']) else [0],\n", - " ])\n", - " )\n", - " for col in row.keys():\n", - " protac_labels[col].append(row[col])\n", - "protac_data = np.array(protac_data)" + "def get_data_for_dim_reduction(\n", + " protac_df,\n", + " lookup_embeddings=True,\n", + " protein_embeddings=protein_embeddings,\n", + " cell2embedding=cell2embedding,\n", + " smiles2fp=smiles2fp,\n", + "):\n", + " \"\"\" Get the data and labels for dimensionality reduction.\n", + "\n", + " Args:\n", + " protac_df (pd.DataFrame): The PROTAC dataframe.\n", + " lookup_embeddings (bool): Whether to look up the embeddings or use the provided ones.\n", + " protein_embeddings (dict): The protein embeddings.\n", + " cell2embedding (dict): The cell line embeddings.\n", + " smiles2fp (dict): The SMILES to fingerprint dictionary.\n", + "\n", + " Returns:\n", + " np.ndarray: The data for dimensionality reduction.\n", + " dict: The labels for dimensionality reduction.\n", + " \"\"\"\n", + " protac_data = []\n", + " protac_labels = defaultdict(list)\n", + " for _, row in protac_df.iterrows():\n", + " if lookup_embeddings:\n", + " protac_data.append(\n", + " np.concatenate([\n", + " protein_embeddings[row['Uniprot']],\n", + " protein_embeddings[row['E3 Ligase Uniprot']],\n", + " cell2embedding[row['Cell Line Identifier']],\n", + " smiles2fp[row['Smiles']],\n", + " # [row['Treatment Time (h)']] if not pd.isna(row['Treatment Time (h)']) else [0],\n", + " ])\n", + " )\n", + " for col in row.keys():\n", + " protac_labels[col].append(row[col])\n", + " else:\n", + " protac_data.append(\n", + " np.concatenate([\n", + " row['Uniprot'],\n", + " row['E3 Ligase Uniprot'],\n", + " row['Cell Line Identifier'],\n", + " row['Smiles'],\n", + " ])\n", + " )\n", + " for col in ['Active', 'Active - OR']:\n", + " if col in row:\n", + " protac_labels[col].append(row[col])\n", + " protac_data = np.array(protac_data)\n", + " return protac_data, protac_labels\n", + "\n", + "if RUN_DIMENSIONALITY_REDUCTION:\n", + " protac_data, protac_labels = get_data_for_dim_reduction(protac_df)" ] }, { @@ -768,17 +813,18 @@ }, { "cell_type": "code", - "execution_count": 142, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "import umap\n", - "from sklearn.preprocessing import StandardScaler" + "if RUN_DIMENSIONALITY_REDUCTION:\n", + " import umap\n", + " from sklearn.preprocessing import StandardScaler" ] }, { "cell_type": "code", - "execution_count": 168, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -793,30 +839,31 @@ } ], "source": [ - "# Define UMAP and Scaler\n", - "umap_reducer = umap.UMAP(\n", - " n_neighbors=50, # Good value: 50\n", - " min_dist=0.5, # Good value: 0.5\n", - " # spread=1.0, # Good value: (not set, default)\n", - " metric='euclidean',\n", - " random_state=42,\n", - " unique=True,\n", - " # n_epochs=100,\n", - " init='spectral', # Default: 'spectral'\n", - " learning_rate=0.8, # Default: 1.0\n", - " verbose=False,\n", - ")\n", - "scaler = StandardScaler()\n", + "if RUN_DIMENSIONALITY_REDUCTION:\n", + " # Define UMAP and Scaler\n", + " umap_reducer = umap.UMAP(\n", + " n_neighbors=50, # Good value: 50\n", + " min_dist=0.5, # Good value: 0.5\n", + " # spread=1.0, # Good value: (not set, default)\n", + " metric='euclidean',\n", + " random_state=42,\n", + " unique=True,\n", + " # n_epochs=100,\n", + " init='spectral', # Default: 'spectral'\n", + " learning_rate=0.8, # Default: 1.0\n", + " verbose=False,\n", + " )\n", + " scaler = StandardScaler()\n", "\n", - "# Get the embeddings as a numpy array\n", - "umap_data = umap_reducer.fit_transform(scaler.fit_transform(protac_data))\n", + " # Get the embeddings as a numpy array\n", + " umap_data = umap_reducer.fit_transform(scaler.fit_transform(protac_data))\n", "\n", - "umap_data.shape" + " umap_data.shape" ] }, { "cell_type": "code", - "execution_count": 169, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -891,41 +938,42 @@ } ], "source": [ - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "import warnings\n", + "if RUN_DIMENSIONALITY_REDUCTION:\n", + " import seaborn as sns\n", + " import matplotlib.pyplot as plt\n", + " import warnings\n", "\n", - "labels_to_plot = [\n", - " 'Active',\n", - " 'Cell Line Identifier',\n", - " 'E3 Ligase',\n", - " 'Uniprot',\n", - " # 'Smiles',\n", - " 'Treatment Time (h)',\n", - " 'DC50 (nM)',\n", - " 'Dmax (%)',\n", - "]\n", - "for col in labels_to_plot:\n", - " # print(f'Plotting UMAP for {col}')\n", - " umap_embeddings = {\n", - " 'UMAP 1': umap_data[:, 0],\n", - " 'UMAP 2': umap_data[:, 1],\n", - " col: protac_labels[col],\n", - " }\n", - " umap_embeddings = pd.DataFrame(umap_embeddings).drop_duplicates()\n", + " labels_to_plot = [\n", + " 'Active',\n", + " 'Cell Line Identifier',\n", + " 'E3 Ligase',\n", + " 'Uniprot',\n", + " # 'Smiles',\n", + " 'Treatment Time (h)',\n", + " 'DC50 (nM)',\n", + " 'Dmax (%)',\n", + " ]\n", + " for col in labels_to_plot:\n", + " # print(f'Plotting UMAP for {col}')\n", + " umap_embeddings = {\n", + " 'UMAP 1': umap_data[:, 0],\n", + " 'UMAP 2': umap_data[:, 1],\n", + " col: protac_labels[col],\n", + " }\n", + " umap_embeddings = pd.DataFrame(umap_embeddings).drop_duplicates()\n", "\n", - " with warnings.catch_warnings():\n", - " warnings.simplefilter(\"ignore\")\n", - " sns.scatterplot(data=umap_embeddings, x='UMAP 1', y='UMAP 2',\n", - " hue=col) #, palette=sns.color_palette('tab10'))\n", - " # Make the legend external and plot it for active and e3 ligase only\n", - " if col in ['Active', 'E3 Ligase']:\n", - " plt.legend(title=f'{col}:')\n", - " else:\n", - " plt.legend().remove()\n", - " plt.title(f'UMAP embedding coloring for \"{col}\"')\n", - " plt.grid(axis='both', alpha=0.5)\n", - " plt.show()" + " with warnings.catch_warnings():\n", + " warnings.simplefilter(\"ignore\")\n", + " sns.scatterplot(data=umap_embeddings, x='UMAP 1', y='UMAP 2',\n", + " hue=col) #, palette=sns.color_palette('tab10'))\n", + " # Make the legend external and plot it for active and e3 ligase only\n", + " if col in ['Active', 'E3 Ligase', 'Active - OR']:\n", + " plt.legend(title=f'{col}:')\n", + " else:\n", + " plt.legend().remove()\n", + " plt.title(f'UMAP embedding coloring for \"{col}\"')\n", + " plt.grid(axis='both', alpha=0.5)\n", + " plt.show()" ] }, { @@ -935,183 +983,87 @@ "### PCA-ing" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run PCA analysis on `protac_data` and `protac_labels`:" + ] + }, { "cell_type": "code", - "execution_count": 170, + "execution_count": 49, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "13 rows × 35 columns
\n", + "13 rows × 36 columns
\n", "" ], "text/plain": [ @@ -2173,50 +2282,50 @@ "2088 UWYLVQJEOCYCEW-UHFFFAOYSA-N 609.108 42 \n", "2096 CRZGRNBMUQNJFY-UHFFFAOYSA-N 794.935 57 \n", "\n", - " Ring Count Rotatable Bond Count ... Name \\\n", - "173 7 18 ... NaN \n", - "174 6 13 ... NaN \n", - "793 3 28 ... NaN \n", - "924 4 26 ... NaN \n", - "1420 6 14 ... QC-01-175 \n", - "1604 5 28 ... MS910 \n", - "1606 7 29 ... MS4322 \n", - "1668 7 21 ... CPS2 \n", - "1956 4 11 ... NaN \n", - "1980 6 7 ... BP3 \n", - "1983 8 27 ... NaN \n", - "2088 5 12 ... NaN \n", - "2096 6 20 ... NaN \n", + " Ring Count Rotatable Bond Count ... \\\n", + "173 7 18 ... \n", + "174 6 13 ... \n", + "793 3 28 ... \n", + "924 4 26 ... \n", + "1420 6 14 ... \n", + "1604 5 28 ... \n", + "1606 7 29 ... \n", + "1668 7 21 ... \n", + "1956 4 11 ... \n", + "1980 6 7 ... \n", + "1983 8 27 ... \n", + "2088 5 12 ... \n", + "2096 6 20 ... \n", "\n", - " Assay (DC50/Dmax) Exact Mass XLogP3 \\\n", - "173 NaN NaN NaN \n", - "174 NaN NaN NaN \n", - "793 NaN NaN NaN \n", - "924 NaN NaN NaN \n", - "1420 Degradation of total tau/P-tau in A152T neuron... 626.248897 1.96 \n", - "1604 Degradation of MEK1 in HT-29/SK-MEL-28 cells a... 984.237789 3.36 \n", - "1606 Degradation of PRMT5 in MCF-7 cells after 6 d ... 1100.536489 2.22 \n", - "1668 Degradation of CDK2 in Pfeiffer/DOHH2/K562 cel... 890.258828 2.19 \n", - "1956 Degradation of TYR in A375 cells after 24 h tr... 537.222348 1.42 \n", - "1980 Degradation of HSP90 in MCF-7 cells after 6 h ... 640.194944 3.58 \n", - "1983 Degradation of GSK3B in SH-SY5Y cells after 24... 1037.417771 0.96 \n", - "2088 Degradation of SF3B1 in K562 cells after 24 h ... 608.160867 4.93 \n", - "2096 Degradation of HDAC8 in Jurkat cells after 24 ... 794.321017 6.02 \n", + " Assay (DC50/Dmax) Exact Mass XLogP3 \\\n", + "173 NaN NaN NaN \n", + "174 NaN NaN NaN \n", + "793 NaN NaN NaN \n", + "924 NaN NaN NaN \n", + "1420 Degradation of total tau/P-tau in A152T neuron... 626.248897 1.96 \n", + "1604 Degradation of MEK1 in HT-29/SK-MEL-28 cells a... 984.237789 3.36 \n", + "1606 Degradation of PRMT5 in MCF-7 cells after 6 d ... 1100.536489 2.22 \n", + "1668 Degradation of CDK2 in Pfeiffer/DOHH2/K562 cel... 890.258828 2.19 \n", + "1956 Degradation of TYR in A375 cells after 24 h tr... 537.222348 1.42 \n", + "1980 Degradation of HSP90 in MCF-7 cells after 6 h ... 640.194944 3.58 \n", + "1983 Degradation of GSK3B in SH-SY5Y cells after 24... 1037.417771 0.96 \n", + "2088 Degradation of SF3B1 in K562 cells after 24 h ... 608.160867 4.93 \n", + "2096 Degradation of HDAC8 in Jurkat cells after 24 ... 794.321017 6.02 \n", "\n", - " Target (Parsed) POI Sequence \\\n", - "173 NaN MAEPDPSHPLETQAGKVQEAQDSDSDSEGGAAGGEADMDFLRNLFS... \n", - "174 NaN MAKQYDSVECPFCDEVSKYEKLAKIGQGTFGEVFKARHRKTGQKVA... \n", - "793 NaN MASNSSSCPTPGGGHLNGYPVPPYAFFFPPMLGGLSPPGALTTLQH... \n", - "924 NaN MVNPTVFFDIAVDGEPLGRVSFELFADKVPKTAENFRALSTGEKGF... \n", - "1420 tau/P-tau MAEPRQEFEVMEDHAGTYGLGDRKDQGGYTMHQDQEGDTDAGLKES... \n", - "1604 MEK1 MPKKKPTPIQLNPAPDGSAVNGTSSAETNLEALQKKLEELELDEQQ... \n", - "1606 PRMT5 MAAMAVGGAGGSRVSSGRDLNCVPEIADTLGAVAKQGFDFLCMPVF... \n", - "1668 CDK2 MENFQKVEKIGEGTYGVVYKARNKLTGEVVALKKIRLDTETEGVPS... \n", - "1956 TYR MLLAVLYCLLWSFQTSAGHFPRACVSSKNLMEKECCPPWSGDRSPC... \n", - "1980 HSP90 MPEETQTQDQPMEEEEVETFAFQAEIAQLMSLIINTFYSNKEIFLR... \n", - "1983 GSK3B MSGRPRTTSFAESCKPVQQPSAFGSMKVSRDKDGSKVTTVVATPGQ... \n", - "2088 SF3B1 MAKIAKTHEDIEAQIREIQGKKAALDEAQGVGLDSTGYYDQEIYGG... \n", - "2096 HDAC8 MEEPEEPADSGQSLVPVYIYSPEYVSMCDSLAKIPKRASMVHSLIE... \n", + " Target (Parsed) POI Sequence \\\n", + "173 NaN MAEPDPSHPLETQAGKVQEAQDSDSDSEGGAAGGEADMDFLRNLFS... \n", + "174 NaN MAKQYDSVECPFCDEVSKYEKLAKIGQGTFGEVFKARHRKTGQKVA... \n", + "793 NaN MASNSSSCPTPGGGHLNGYPVPPYAFFFPPMLGGLSPPGALTTLQH... \n", + "924 NaN MVNPTVFFDIAVDGEPLGRVSFELFADKVPKTAENFRALSTGEKGF... \n", + "1420 tau/P-tau MAEPRQEFEVMEDHAGTYGLGDRKDQGGYTMHQDQEGDTDAGLKES... \n", + "1604 MEK1 MPKKKPTPIQLNPAPDGSAVNGTSSAETNLEALQKKLEELELDEQQ... \n", + "1606 PRMT5 MAAMAVGGAGGSRVSSGRDLNCVPEIADTLGAVAKQGFDFLCMPVF... \n", + "1668 CDK2 MENFQKVEKIGEGTYGVVYKARNKLTGEVVALKKIRLDTETEGVPS... \n", + "1956 TYR MLLAVLYCLLWSFQTSAGHFPRACVSSKNLMEKECCPPWSGDRSPC... \n", + "1980 HSP90 MPEETQTQDQPMEEEEVETFAFQAEIAQLMSLIINTFYSNKEIFLR... \n", + "1983 GSK3B MSGRPRTTSFAESCKPVQQPSAFGSMKVSRDKDGSKVTTVVATPGQ... \n", + "2088 SF3B1 MAKIAKTHEDIEAQIREIQGKKAALDEAQGVGLDSTGYYDQEIYGG... \n", + "2096 HDAC8 MEEPEEPADSGQSLVPVYIYSPEYVSMCDSLAKIPKRASMVHSLIE... \n", "\n", " E3 Ligase Uniprot E3 Ligase Sequence \\\n", "173 Q96SW2 MAGEGDQQDAAHNMGNHLPLLPAESEEEDEMEVEDQDSKEAKKPNI... \n", @@ -2233,22 +2342,22 @@ "2088 Q96SW2 MAGEGDQQDAAHNMGNHLPLLPAESEEEDEMEVEDQDSKEAKKPNI... \n", "2096 Q96SW2 MAGEGDQQDAAHNMGNHLPLLPAESEEEDEMEVEDQDSKEAKKPNI... \n", "\n", - " Cell Line Identifier Avg Tanimoto \n", - "173 HeLa 0.229740 \n", - "174 HCT116-53BPI(+/-) 0.204367 \n", - "793 HT1080 EGFP 0.194916 \n", - "924 Huh7 IFITM2-/- 0.243808 \n", - "1420 NaN 0.214824 \n", - "1604 HT-29 0.213945 \n", - "1606 MCF-7 0.237421 \n", - "1668 Pfeiffer 0.224538 \n", - "1956 A375-C5 0.197189 \n", - "1980 MCF-7 0.200476 \n", - "1983 SH-SY5Y 0.275566 \n", - "2088 EGFP-K562 0.237667 \n", - "2096 Jurkat 0.248761 \n", + " Cell Line Identifier Active - OR Avg Tanimoto \n", + "173 HeLa False 0.229740 \n", + "174 HCT116-53BPI(+/-) False 0.204367 \n", + "793 HT1080 EGFP False 0.194916 \n", + "924 Huh7 IFITM2-/- True 0.243808 \n", + "1420 NaN False 0.214824 \n", + "1604 HT-29 False 0.213945 \n", + "1606 MCF-7 False 0.237421 \n", + "1668 Pfeiffer False 0.224538 \n", + "1956 A375-C5 False 0.197189 \n", + "1980 MCF-7 False 0.200476 \n", + "1983 SH-SY5Y False 0.275566 \n", + "2088 EGFP-K562 False 0.237667 \n", + "2096 Jurkat False 0.248761 \n", "\n", - "[13 rows x 35 columns]" + "[13 rows x 36 columns]" ] }, "metadata": {}, @@ -2282,7 +2391,8 @@ "unique_smiles_uniprot_idx = active_df.set_index(['Smiles', 'Uniprot']).index.map(unique_smiles_uniprot)\n", "\n", "# Cross the indices to get the unique samples\n", - "unique_samples = active_df[unique_smiles_idx & unique_uniprot_idx & unique_smiles_uniprot_idx].index\n", + "# unique_samples = active_df[unique_smiles_idx & unique_uniprot_idx & unique_smiles_uniprot_idx].index\n", + "unique_samples = active_df[unique_smiles_idx & unique_uniprot_idx].index\n", "test_df = active_df.loc[unique_samples]\n", "\n", "# Reporting\n", @@ -2307,34 +2417,44 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "# active_col = 'Active'\n", + "active_col = 'Active - OR'" + ] + }, + { + "cell_type": "code", + "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "protac_df.shape: (2141, 35)\n", - "active_df.shape: (1087, 35)\n", - "train_df.shape: (878, 36)\n", - "val_df.shape: (196, 36)\n", - "test_df.shape: (13, 35)\n", + "protac_df.shape: (2141, 36)\n", + "active_df.shape: (1637, 36)\n", + "train_df.shape: (1302, 37)\n", + "val_df.shape: (322, 37)\n", + "test_df.shape: (13, 36)\n", "Number of leaking Uniprots: 0\n", "\n", "Active/inactive PROTACs in the training set:\n", - "False 0.74\n", - "True 0.26\n", - "Name: Active, dtype: float64\n", + "False 0.5\n", + "True 0.5\n", + "Name: Active - OR, dtype: float64\n", "\n", "Active/inactive PROTACs in the validation set:\n", - "False 0.76\n", - "True 0.24\n", - "Name: Active, dtype: float64\n", + "True 0.56\n", + "False 0.44\n", + "Name: Active - OR, dtype: float64\n", "\n", "Active/inactive PROTACs in the test set:\n", "False 0.92\n", "True 0.08\n", - "Name: Active, dtype: float64\n" + "Name: Active - OR, dtype: float64\n" ] } ], @@ -2343,8 +2463,8 @@ "from sklearn.model_selection import train_test_split\n", "\n", "# The train and validation sets will be created from the active PROTACs only,\n", - "# i.e., the ones with 'Active' column not NaN, and that are NOT in the test set\n", - "active_df = protac_df[protac_df['Active'].notna()]\n", + "# i.e., the ones with active_col column not NaN, and that are NOT in the test set\n", + "active_df = protac_df[protac_df[active_col].notna()]\n", "train_val_df = active_df[~active_df.index.isin(unique_samples)].copy()\n", "\n", "# Get the 20% amount of the train_val_df\n", @@ -2373,16 +2493,16 @@ "leaking_uniprots = val_df['Uniprot'].isin(train_df['Uniprot'])\n", "print(f'Number of leaking Uniprots: {leaking_uniprots.sum()}')\n", "print('')\n", - "print(f'Active/inactive PROTACs in the training set:\\n{train_df[\"Active\"].value_counts(normalize=True).round(2)}')\n", + "print(f'Active/inactive PROTACs in the training set:\\n{train_df[active_col].value_counts(normalize=True).round(2)}')\n", "print('')\n", - "print(f'Active/inactive PROTACs in the validation set:\\n{val_df[\"Active\"].value_counts(normalize=True).round(2)}')\n", + "print(f'Active/inactive PROTACs in the validation set:\\n{val_df[active_col].value_counts(normalize=True).round(2)}')\n", "print('')\n", - "print(f'Active/inactive PROTACs in the test set:\\n{test_df[\"Active\"].value_counts(normalize=True).round(2)}')" + "print(f'Active/inactive PROTACs in the test set:\\n{test_df[active_col].value_counts(normalize=True).round(2)}')" ] }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -2392,24 +2512,27 @@ " cell2embedding,\n", " smiles2fp,\n", " use_smote=True,\n", + " use_ored_activity=True if active_col == 'Active - OR' else False,\n", ")\n", "val_ds = PROTAC_Dataset(\n", " val_df,\n", " protein_embeddings,\n", " cell2embedding,\n", " smiles2fp,\n", + " use_ored_activity=True if active_col == 'Active - OR' else False,\n", ")\n", "test_ds = PROTAC_Dataset(\n", " test_df,\n", " protein_embeddings,\n", " cell2embedding,\n", " smiles2fp,\n", + " use_ored_activity=True if active_col == 'Active - OR' else False,\n", ")" ] }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -2464,27 +2587,27 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Number of parameters: 4,198,401\n" + "Number of parameters: 6,494,209\n" ] } ], "source": [ "model = PROTAC_Model(\n", - " hidden_dim=512,\n", + " hidden_dim=768,\n", " smiles_emb_dim=1024,\n", " poi_emb_dim=1024,\n", " e3_emb_dim=1024,\n", " cell_emb_dim=768,\n", " batch_size=2,\n", - " learning_rate=1e-5, # 2e-5,\n", - " dropout=0.5, # 0.2,\n", + " learning_rate=2e-5, # 2e-5,\n", + " dropout=0.2, # 0.2,\n", " train_dataset=train_ds,\n", " val_dataset=val_ds,\n", " test_dataset=test_ds,\n", @@ -2495,7 +2618,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -2506,26 +2629,26 @@ "\n", " | Name | Type | Params\n", "------------------------------------------\n", - "0 | poi_emb | Sequential | 787 K \n", - "1 | e3_emb | Sequential | 524 K \n", - "2 | cell_emb | Sequential | 393 K \n", - "3 | smiles_emb | Sequential | 524 K \n", - "4 | fc1 | Linear | 1.7 M \n", - "5 | fc2 | Linear | 262 K \n", - "6 | fc3 | Linear | 513 \n", + "0 | poi_emb | Sequential | 1.4 M \n", + "1 | e3_emb | Sequential | 787 K \n", + "2 | cell_emb | Sequential | 590 K \n", + "3 | smiles_emb | Sequential | 787 K \n", + "4 | fc1 | Linear | 2.4 M \n", + "5 | fc2 | Linear | 590 K \n", + "6 | fc3 | Linear | 769 \n", "7 | dropout | Dropout | 0 \n", "8 | metrics | ModuleDict | 0 \n", "------------------------------------------\n", - "4.2 M Trainable params\n", + "6.5 M Trainable params\n", "0 Non-trainable params\n", - "4.2 M Total params\n", - "16.794 Total estimated model params size (MB)\n" + "6.5 M Total params\n", + "25.977 Total estimated model params size (MB)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cc5d2b836ce3410ba7f1948caa0e84e6", + "model_id": "0c739e27c5aa4285aad146e8352f2170", "version_major": 2, "version_minor": 0 }, @@ -2539,7 +2662,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e0ee43ce2a8f4e8b831445013a1fe8d7", + "model_id": "333a1cc6f7594301990779a4a3cb6774", "version_major": 2, "version_minor": 0 }, @@ -2553,7 +2676,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "22e8dbc288ff4adbb83ddc8dd38beb7f", + "model_id": "e7d7c9a11b4f4e36baec39ecc61ebec8", "version_major": 2, "version_minor": 0 }, @@ -2568,16 +2691,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "Metric train_loss improved. New best score: 0.690\n", - "Metric val_loss improved. New best score: 0.698\n", - "Metric val_acc improved. New best score: 0.730\n", - "Epoch 0, global step 647: 'val_acc' reached 0.72959 (best 0.72959), saving model to '../logs\\\\protac_playground_model\\\\version_17\\\\checkpoints\\\\epoch=0-val_metrics_opt_score=0.0000.ckpt' as top 1\n" + "Metric train_loss improved. New best score: 0.655\n", + "Metric val_loss improved. New best score: 0.695\n", + "Metric val_acc improved. New best score: 0.475\n", + "Epoch 0, global step 654: 'val_acc' reached 0.47516 (best 0.47516), saving model to '../logs\\\\protac_playground_model\\\\version_19\\\\checkpoints\\\\epoch=0-val_metrics_opt_score=0.0000.ckpt' as top 1\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f7bcf5f239e7464fb22e22c6c669e706", + "model_id": "0c0d204488444db29312e59593b49c52", "version_major": 2, "version_minor": 0 }, @@ -2592,16 +2715,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "Metric train_loss improved by 0.014 >= min_delta = 0.0. New best score: 0.676\n", - "Metric val_loss improved by 0.002 >= min_delta = 0.0. New best score: 0.696\n", - "Metric val_acc improved by 0.010 >= min_delta = 0.0. New best score: 0.740\n", - "Epoch 1, global step 1294: 'val_acc' reached 0.73980 (best 0.73980), saving model to '../logs\\\\protac_playground_model\\\\version_17\\\\checkpoints\\\\epoch=1-val_metrics_opt_score=0.0000.ckpt' as top 1\n" + "Metric train_loss improved by 0.174 >= min_delta = 0.0. New best score: 0.480\n", + "Metric val_acc improved by 0.009 >= min_delta = 0.0. New best score: 0.484\n", + "Epoch 1, global step 1308: 'val_acc' reached 0.48447 (best 0.48447), saving model to '../logs\\\\protac_playground_model\\\\version_19\\\\checkpoints\\\\epoch=1-val_metrics_opt_score=0.0000.ckpt' as top 1\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3b354188f4044b87a106452f10be8ab8", + "model_id": "3ca8dc81df234fa492eabf74bdd6ab90", "version_major": 2, "version_minor": 0 }, @@ -2616,15 +2738,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "Metric train_loss improved by 0.051 >= min_delta = 0.0. New best score: 0.626\n", - "Metric val_loss improved by 0.031 >= min_delta = 0.0. New best score: 0.665\n", - "Epoch 2, global step 1941: 'val_acc' was not in top 1\n" + "Metric train_loss improved by 0.095 >= min_delta = 0.0. New best score: 0.386\n", + "Metric val_acc improved by 0.102 >= min_delta = 0.0. New best score: 0.587\n", + "Epoch 2, global step 1962: 'val_acc' reached 0.58696 (best 0.58696), saving model to '../logs\\\\protac_playground_model\\\\version_19\\\\checkpoints\\\\epoch=2-val_metrics_opt_score=0.0000.ckpt' as top 1\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f1ab97f26126496eaeee5272f14c896c", + "model_id": "9166a5c4522d4afd9e09e26d12bcda73", "version_major": 2, "version_minor": 0 }, @@ -2639,15 +2761,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "Metric train_loss improved by 0.124 >= min_delta = 0.0. New best score: 0.502\n", - "Metric val_loss improved by 0.010 >= min_delta = 0.0. New best score: 0.655\n", - "Epoch 3, global step 2588: 'val_acc' was not in top 1\n" + "Metric train_loss improved by 0.051 >= min_delta = 0.0. New best score: 0.335\n", + "Epoch 3, global step 2616: 'val_acc' was not in top 1\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9018278acca948b28b2950f98770ae8e", + "model_id": "02ff0aa346d64ad484ecb744e24476dd", "version_major": 2, "version_minor": 0 }, @@ -2662,14 +2783,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "Metric train_loss improved by 0.112 >= min_delta = 0.0. New best score: 0.390\n", - "Epoch 4, global step 3235: 'val_acc' was not in top 1\n" + "Metric train_loss improved by 0.036 >= min_delta = 0.0. New best score: 0.299\n", + "Metric val_acc improved by 0.009 >= min_delta = 0.0. New best score: 0.596\n", + "Epoch 4, global step 3270: 'val_acc' reached 0.59627 (best 0.59627), saving model to '../logs\\\\protac_playground_model\\\\version_19\\\\checkpoints\\\\epoch=4-val_metrics_opt_score=0.0000.ckpt' as top 1\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3663c320b18a4e60842481d19f2d4e59", + "model_id": "35a915c058f74353bc54da5183715db6", "version_major": 2, "version_minor": 0 }, @@ -2684,31 +2806,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "Metric train_loss improved by 0.058 >= min_delta = 0.0. New best score: 0.332\n", - "Epoch 5, global step 3882: 'val_acc' was not in top 1\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "b28edd5f02f04641be8630b43942bd3b", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Metric train_loss improved by 0.042 >= min_delta = 0.0. New best score: 0.290\n", - "Monitored metric val_acc did not improve in the last 5 records. Best score: 0.740. Signaling Trainer to stop.\n", - "Epoch 6, global step 4529: 'val_acc' was not in top 1\n" + "Metric train_loss improved by 0.026 >= min_delta = 0.0. New best score: 0.273\n", + "Monitored metric val_loss did not improve in the last 5 records. Best score: 0.695. Signaling Trainer to stop.\n", + "Epoch 5, global step 3924: 'val_acc' was not in top 1\n" ] } ], @@ -2735,7 +2835,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -2748,7 +2848,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5f5f1db72d5649df85ae1beae2ab9282", + "model_id": "34e221e2cece4c6eaeb02d8a5837c62f", "version_major": 2, "version_minor": 0 }, @@ -2769,7 +2869,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "409795cc9d9c437b8c84f0acc6876016", + "model_id": "1222b2c994e74bac991b22d5bb64e26d", "version_major": 2, "version_minor": 0 }, @@ -2782,7 +2882,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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