Reproducing_DeepDTA.ipynb

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      "source": [
        "# **Reproducing DeepDTA**\n",
        "    * In this notebook I have tried to replicate the results of DeepDTA paper.\n",
        "    * I have taken small dataset of size (18461 rows × 3 columns)\n",
        "\n",
        "> ## **DOI** : https://doi.org/10.1093/bioinformatics/bty593\n"
      ],
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      "execution_count": 1,
      "metadata": {
        "id": "WTMKQY8RshL0"
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      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import csv\n",
        "import random\n",
        "import pandas as pd\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras import regularizers\n",
        "from tensorflow.keras import optimizers"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.random.seed(42)\n",
        "tf.random.set_seed(42)\n",
        "random.seed(42)"
      ],
      "metadata": {
        "id": "V1DhUlMrtyOq"
      },
      "execution_count": 2,
      "outputs": []
    },
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      "source": [
        "# load data\n",
        "df = pd.read_csv(\"/content/drive/MyDrive/Colab_Notebooks/dta.csv\")\n",
        "df.head()"
      ],
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          "height": 206
        },
        "id": "QFo_SmAau6ce",
        "outputId": "f84436c8-f4fe-480b-eabd-c80a9862d463"
      },
      "execution_count": 3,
      "outputs": [
        {
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              "   ligand_id                                             smiles prot_id  \\\n",
              "0   25182616  CNC(=O)c1c(F)cccc1Nc1nc(Nc2cc3N(CCc3cc2OC)C(=O...  P54762   \n",
              "1    5329102  CCN(CC)CCNC(=O)c1c(C)[nH]c(\\C=C2/C(=O)Nc3ccc(F...  P30530   \n",
              "2     447077  CSc1cccc(Nc2ncc3cc(-c4c(Cl)cccc4Cl)c(=O)n(C)c3...  O14965   \n",
              "3   25182616  CNC(=O)c1c(F)cccc1Nc1nc(Nc2cc3N(CCc3cc2OC)C(=O...  P22455   \n",
              "4   10107559           NC[C@H]([C@@H](O)c1ccccc1)c1ccc2ccccc2c1  Q01959   \n",
              "\n",
              "                                         aa_sequence  affinity_score  \n",
              "0  MALDYLLLLLLASAVAAMEETLMDTRTATAELGWTANPASGWEEVS...        5.000000  \n",
              "1  MAWRCPRMGRVPLAWCLALCGWACMAPRGTQAEESPFVGNPGNITG...        8.045757  \n",
              "2  MDRSKENCISGPVKATAPVGGPKRVLVTQQFPCQNPLPVNSGQAQR...        5.000000  \n",
              "3  MRLLLALLGVLLSVPGPPVLSLEASEEVELEPCLAPSLEQQEQELT...        5.000000  \n",
              "4  MSKSKCSVGLMSSVVAPAKEPNAVGPKEVELILVKEQNGVQLTSST...        6.853872  "
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              "      <td>447077</td>\n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 18461,\n  \"fields\": [\n    {\n      \"column\": \"ligand_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20351289,\n        \"min\": 774,\n        \"max\": 132271625,\n        \"num_unique_values\": 750,\n        \"samples\": [\n          44280071,\n          57336560,\n          44294316\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"smiles\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 750,\n        \"samples\": [\n          \"C[C@@H](O)[C@H](N(C)C(=O)[C@@H]1CSSC[C@@H](NC(=O)[C@H](N)Cc2ccccc2)C(=O)N[C@@H](Cc2ccccc2)C(=O)N[C@@H](Cc2c[nH]c3ccccc23)C(=O)N[C@@H](CCCCN)C(=O)N[C@@H]([C@@H](C)O)C(=O)N1)C(N)=O\",\n          \"Cc1ccc(NC(=O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(=O)NO)cc1C\",\n          \"CC[C@H](C)[C@H]1NC(=O)[C@H](Cc2cc3ccccc3[nH]2)NC(=O)CC2(CCCCC2)SSC[C@@H](NC(=O)[C@@H](CC(N)=O)NC(=O)[C@H](CCC(N)=O)NC1=O)C(=O)N1CCC[C@H]1C(=O)N[C@@H](CCCNC(N)=N)C(=O)NCC(N)=O\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prot_id\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 398,\n        \"samples\": [\n          \"Q16512\",\n          \"P37231\",\n          \"O43318\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"aa_sequence\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 398,\n        \"samples\": [\n          \"MASDAVQSEPRSWSLLEQLGLAGADLAAPGVQQQLELERERLRREIRKELKLKEGAENLRRATTDLGRSLGPVELLLRGSSRRLDLLHQQLQELHAHVVLPDPAATHDGPQSPGAGGPTCSATNLSRVAGLEKQLAIELKVKQGAENMIQTYSNGSTKDRKLLLTAQQMLQDSKTKIDIIRMQLRRALQAGQLENQAAPDDTQGSPDLGAVELRIEELRHHFRVEHAVAEGAKNVLRLLSAAKAPDRKAVSEAQEKLTESNQKLGLLREALERRLGELPADHPKGRLLREELAAASSAAFSTRLAGPFPATHYSTLCKPAPLTGTLEVRVVGCRDLPETIPWNPTPSMGGPGTPDSRPPFLSRPARGLYSRSGSLSGRSSLKAEAENTSEVSTVLKLDNTVVGQTSWKPCGPNAWDQSFTLELERARELELAVFWRDQRGLCALKFLKLEDFLDNERHEVQLDMEPQGCLVAEVTFRNPVIERIPRLRRQKKIFSKQQGKAFQRARQMNIDVATWVRLLRRLIPNATGTGTFSPGASPGSEARTTGDISVEKLNLGTDSDSSPQKSSRDPPSSPSSLSSPIQESTAPELPSETQETPGPALCSPLRKSPLTLEDFKFLAVLGRGHFGKVLLSEFRPSGELFAIKALKKGDIVARDEVESLMCEKRILAAVTSAGHPFLVNLFGCFQTPEHVCFVMEYSAGGDLMLHIHSDVFSEPRAIFYSACVVLGLQFLHEHKIVYRDLKLDNLLLDTEGYVKIADFGLCKEGMGYGDRTSTFCGTPEFLAPEVLTDTSYTRAVDWWGLGVLLYEMLVGESPFPGDDEEEVFDSIVNDEVRYPRFLSAEAIGIMRRLLRRNPERRLGSSERDAEDVKKQPFFRTLGWEALLARRLPPPFVPTLSGRTDVSNFDEEFTGEAPTLSPPRDARPLTAAEQAAFLDFDFVAGGC\",\n          \"MGETLGDSPIDPESDSFTDTLSANISQEMTMVDTEMPFWPTNFGISSVDLSVMEDHSHSFDIKPFTTVDFSSISTPHYEDIPFTRTDPVVADYKYDLKLQEYQSAIKVEPASPPYYSEKTQLYNKPHEEPSNSLMAIECRVCGDKASGFHYGVHACEGCKGFFRRTIRLKLIYDRCDLNCRIHKKSRNKCQYCRFQKCLAVGMSHNAIRFGRMPQAEKEKLLAEISSDIDQLNPESADLRALAKHLYDSYIKSFPLTKAKARAILTGKTTDKSPFVIYDMNSLMMGEDKIKFKHITPLQEQSKEVAIRIFQGCQFRSVEAVQEITEYAKSIPGFVNLDLNDQVTLLKYGVHEIIYTMLASLMNKDGVLISEGQGFMTREFLKSLRKPFGDFMEPKFEFAVKFNALELDDSDLAIFIAVIILSGDRPGLLNVKPIEDIQDNLLQALELQLKLNHPESSQLFAKLLQKMTDLRQIVTEHVQLLQVIKKTETDMSLHPLLQEIYKDLY\",\n          \"MSTASAASSSSSSSAGEMIEAPSQVLNFEEIDYKEIEVEEVVGRGAFGVVCKAKWRAKDVAIKQIESESERKAFIVELRQLSRVNHPNIVKLYGACLNPVCLVMEYAEGGSLYNVLHGAEPLPYYTAAHAMSWCLQCSQGVAYLHSMQPKALIHRDLKPPNLLLVAGGTVLKICDFGTACDIQTHMTNNKGSAAWMAPEVFEGSNYSEKCDVFSWGIILWEVITRRKPFDEIGGPAFRIMWAVHNGTRPPLIKNLPKPIESLMTRCWSKDPSQRPSMEEIVKIMTHLMRYFPGADEPLQYPCQYSDEGQSNSATSTGSFMDIASTNTSNKSDTNMEQVPATNDTIKRLESKLLKNQAKQQSESGRLSLGASRGSSVESLPPTSEGKRMSADMSEIEARIAATTAYSKPKRGHRKTASFGNILDVPEIVISGNGQPRRRSIQDLTVTGTEPGQVSSRSSSPSVRMITTSGPTSEKPTRSHPWTPDDSTDTNGSDNSIPMAYLTLDHQLQPLAPCPNSKESMAVFEQHCKMAQEYMKVQTEIALLLQRKQELVAELDQDEKDQQNTSRLVQEHKKLLDENKSLSTYYQQCKKQLEVIRSQQQKRQGTS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"affinity_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1368240513772159,\n        \"min\": 1.602059991,\n        \"max\": 13.32790214,\n        \"num_unique_values\": 1015,\n        \"samples\": [\n          8.209714836,\n          3.070581074,\n          8.026872146\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pkr4tQMBvhYS",
        "outputId": "ee497d05-25c7-4e70-ec15-cfeb336eee34"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ligand_id', 'smiles', 'prot_id', 'aa_sequence', 'affinity_score'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = df.drop(columns=['ligand_id', 'prot_id'], axis = 1)"
      ],
      "metadata": {
        "id": "2MijF9jRvx_U"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "YSztmU94wRTr",
        "outputId": "3af32029-a6d8-4b84-b150-5a1cdb40187c"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                  smiles  \\\n",
              "0      CNC(=O)c1c(F)cccc1Nc1nc(Nc2cc3N(CCc3cc2OC)C(=O...   \n",
              "1      CCN(CC)CCNC(=O)c1c(C)[nH]c(\\C=C2/C(=O)Nc3ccc(F...   \n",
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              "3      CNC(=O)c1c(F)cccc1Nc1nc(Nc2cc3N(CCc3cc2OC)C(=O...   \n",
              "4               NC[C@H]([C@@H](O)c1ccccc1)c1ccc2ccccc2c1   \n",
              "...                                                  ...   \n",
              "18456  COc1ccc(F)c(F)c1C(=O)c1cnc(NC2CCN(CC2)S(C)(=O)...   \n",
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              "18458  CCOc1cc2ncc(C#N)c(Nc3ccc(F)c(Cl)c3)c2cc1NC(=O)...   \n",
              "18459  C[C@H]1C[C@@H](Nc2ccc(Cl)cc2)c2cc(ccc2N1C(C)=O...   \n",
              "18460  CN1CCN(CC1)c1cc(Nc2cc(C)n[nH]2)nc(Sc2ccc(NC(=O...   \n",
              "\n",
              "                                             aa_sequence  affinity_score  \n",
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              "3      MRLLLALLGVLLSVPGPPVLSLEASEEVELEPCLAPSLEQQEQELT...        5.000000  \n",
              "4      MSKSKCSVGLMSSVVAPAKEPNAVGPKEVELILVKEQNGVQLTSST...        6.853872  \n",
              "...                                                  ...             ...  \n",
              "18456  MPPRPSSGELWGIHLMPPRILVECLLPNGMIVTLECLREATLITIK...        5.000000  \n",
              "18457  MSALAGEDVWRCPGCGDHIAPSQIWYRTVNETWHGSCFRCSECQDS...        5.000000  \n",
              "18458  MKPATGLWVWVSLLVAAGTVQPSDSQSVCAGTENKLSSLSDLEQQY...        4.677781  \n",
              "18459  MLQNVTPHNKLPGEGNAGLLGLGPEAAAPGKRIRKPSLLYEGFESP...        7.602060  \n",
              "18460  MDLVGVASPEPGTAAAWGPSKCPWAIPQNTISCSLADVMSEQLAKE...        6.221849  \n",
              "\n",
              "[18461 rows x 3 columns]"
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              "      <td>CNC(=O)c1c(F)cccc1Nc1nc(Nc2cc3N(CCc3cc2OC)C(=O...</td>\n",
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              "      <td>MRLLLALLGVLLSVPGPPVLSLEASEEVELEPCLAPSLEQQEQELT...</td>\n",
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              "      <td>COc1ccc(F)c(F)c1C(=O)c1cnc(NC2CCN(CC2)S(C)(=O)...</td>\n",
              "      <td>MPPRPSSGELWGIHLMPPRILVECLLPNGMIVTLECLREATLITIK...</td>\n",
              "      <td>5.000000</td>\n",
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              "      <td>C[C@@H](O)COc1cn2ncnc(Oc3ccc4[nH]c(C)cc4c3F)c2c1C</td>\n",
              "      <td>MSALAGEDVWRCPGCGDHIAPSQIWYRTVNETWHGSCFRCSECQDS...</td>\n",
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              "      <td>CCOc1cc2ncc(C#N)c(Nc3ccc(F)c(Cl)c3)c2cc1NC(=O)...</td>\n",
              "      <td>MKPATGLWVWVSLLVAAGTVQPSDSQSVCAGTENKLSSLSDLEQQY...</td>\n",
              "      <td>4.677781</td>\n",
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              "      <td>C[C@H]1C[C@@H](Nc2ccc(Cl)cc2)c2cc(ccc2N1C(C)=O...</td>\n",
              "      <td>MLQNVTPHNKLPGEGNAGLLGLGPEAAAPGKRIRKPSLLYEGFESP...</td>\n",
              "      <td>7.602060</td>\n",
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              "      <td>CN1CCN(CC1)c1cc(Nc2cc(C)n[nH]2)nc(Sc2ccc(NC(=O...</td>\n",
              "      <td>MDLVGVASPEPGTAAAWGPSKCPWAIPQNTISCSLADVMSEQLAKE...</td>\n",
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              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-70ddc351-642b-4890-87e7-a3e7f12ec854 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df2",
              "summary": "{\n  \"name\": \"df2\",\n  \"rows\": 18461,\n  \"fields\": [\n    {\n      \"column\": \"smiles\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 750,\n        \"samples\": [\n          \"C[C@@H](O)[C@H](N(C)C(=O)[C@@H]1CSSC[C@@H](NC(=O)[C@H](N)Cc2ccccc2)C(=O)N[C@@H](Cc2ccccc2)C(=O)N[C@@H](Cc2c[nH]c3ccccc23)C(=O)N[C@@H](CCCCN)C(=O)N[C@@H]([C@@H](C)O)C(=O)N1)C(N)=O\",\n          \"Cc1ccc(NC(=O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(=O)NO)cc1C\",\n          \"CC[C@H](C)[C@H]1NC(=O)[C@H](Cc2cc3ccccc3[nH]2)NC(=O)CC2(CCCCC2)SSC[C@@H](NC(=O)[C@@H](CC(N)=O)NC(=O)[C@H](CCC(N)=O)NC1=O)C(=O)N1CCC[C@H]1C(=O)N[C@@H](CCCNC(N)=N)C(=O)NCC(N)=O\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"aa_sequence\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 398,\n        \"samples\": [\n          \"MASDAVQSEPRSWSLLEQLGLAGADLAAPGVQQQLELERERLRREIRKELKLKEGAENLRRATTDLGRSLGPVELLLRGSSRRLDLLHQQLQELHAHVVLPDPAATHDGPQSPGAGGPTCSATNLSRVAGLEKQLAIELKVKQGAENMIQTYSNGSTKDRKLLLTAQQMLQDSKTKIDIIRMQLRRALQAGQLENQAAPDDTQGSPDLGAVELRIEELRHHFRVEHAVAEGAKNVLRLLSAAKAPDRKAVSEAQEKLTESNQKLGLLREALERRLGELPADHPKGRLLREELAAASSAAFSTRLAGPFPATHYSTLCKPAPLTGTLEVRVVGCRDLPETIPWNPTPSMGGPGTPDSRPPFLSRPARGLYSRSGSLSGRSSLKAEAENTSEVSTVLKLDNTVVGQTSWKPCGPNAWDQSFTLELERARELELAVFWRDQRGLCALKFLKLEDFLDNERHEVQLDMEPQGCLVAEVTFRNPVIERIPRLRRQKKIFSKQQGKAFQRARQMNIDVATWVRLLRRLIPNATGTGTFSPGASPGSEARTTGDISVEKLNLGTDSDSSPQKSSRDPPSSPSSLSSPIQESTAPELPSETQETPGPALCSPLRKSPLTLEDFKFLAVLGRGHFGKVLLSEFRPSGELFAIKALKKGDIVARDEVESLMCEKRILAAVTSAGHPFLVNLFGCFQTPEHVCFVMEYSAGGDLMLHIHSDVFSEPRAIFYSACVVLGLQFLHEHKIVYRDLKLDNLLLDTEGYVKIADFGLCKEGMGYGDRTSTFCGTPEFLAPEVLTDTSYTRAVDWWGLGVLLYEMLVGESPFPGDDEEEVFDSIVNDEVRYPRFLSAEAIGIMRRLLRRNPERRLGSSERDAEDVKKQPFFRTLGWEALLARRLPPPFVPTLSGRTDVSNFDEEFTGEAPTLSPPRDARPLTAAEQAAFLDFDFVAGGC\",\n          \"MGETLGDSPIDPESDSFTDTLSANISQEMTMVDTEMPFWPTNFGISSVDLSVMEDHSHSFDIKPFTTVDFSSISTPHYEDIPFTRTDPVVADYKYDLKLQEYQSAIKVEPASPPYYSEKTQLYNKPHEEPSNSLMAIECRVCGDKASGFHYGVHACEGCKGFFRRTIRLKLIYDRCDLNCRIHKKSRNKCQYCRFQKCLAVGMSHNAIRFGRMPQAEKEKLLAEISSDIDQLNPESADLRALAKHLYDSYIKSFPLTKAKARAILTGKTTDKSPFVIYDMNSLMMGEDKIKFKHITPLQEQSKEVAIRIFQGCQFRSVEAVQEITEYAKSIPGFVNLDLNDQVTLLKYGVHEIIYTMLASLMNKDGVLISEGQGFMTREFLKSLRKPFGDFMEPKFEFAVKFNALELDDSDLAIFIAVIILSGDRPGLLNVKPIEDIQDNLLQALELQLKLNHPESSQLFAKLLQKMTDLRQIVTEHVQLLQVIKKTETDMSLHPLLQEIYKDLY\",\n          \"MSTASAASSSSSSSAGEMIEAPSQVLNFEEIDYKEIEVEEVVGRGAFGVVCKAKWRAKDVAIKQIESESERKAFIVELRQLSRVNHPNIVKLYGACLNPVCLVMEYAEGGSLYNVLHGAEPLPYYTAAHAMSWCLQCSQGVAYLHSMQPKALIHRDLKPPNLLLVAGGTVLKICDFGTACDIQTHMTNNKGSAAWMAPEVFEGSNYSEKCDVFSWGIILWEVITRRKPFDEIGGPAFRIMWAVHNGTRPPLIKNLPKPIESLMTRCWSKDPSQRPSMEEIVKIMTHLMRYFPGADEPLQYPCQYSDEGQSNSATSTGSFMDIASTNTSNKSDTNMEQVPATNDTIKRLESKLLKNQAKQQSESGRLSLGASRGSSVESLPPTSEGKRMSADMSEIEARIAATTAYSKPKRGHRKTASFGNILDVPEIVISGNGQPRRRSIQDLTVTGTEPGQVSSRSSSPSVRMITTSGPTSEKPTRSHPWTPDDSTDTNGSDNSIPMAYLTLDHQLQPLAPCPNSKESMAVFEQHCKMAQEYMKVQTEIALLLQRKQELVAELDQDEKDQQNTSRLVQEHKKLLDENKSLSTYYQQCKKQLEVIRSQQQKRQGTS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"affinity_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1368240513772159,\n        \"min\": 1.602059991,\n        \"max\": 13.32790214,\n        \"num_unique_values\": 1015,\n        \"samples\": [\n          8.209714836,\n          3.070581074,\n          8.026872146\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6vcgtxO9x1nq",
        "outputId": "10038de7-d373-4174-f5b7-7c44bda697b3"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "smiles            0\n",
              "aa_sequence       0\n",
              "affinity_score    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.dropna(subset = ['affinity_score'], axis = 0, inplace = True)"
      ],
      "metadata": {
        "id": "NVv6ChAiyXBI"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(df2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "1YzfguBty0IT",
        "outputId": "65e77034-ebfb-4e97-a940-8e2fff6ee3f0"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ],
            "text/html": [
              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
              "      pre.function-repr-contents {\n",
              "        overflow-x: auto;\n",
              "        padding: 8px 12px;\n",
              "        max-height: 500px;\n",
              "      }\n",
              "\n",
              "      pre.function-repr-contents.function-repr-contents-collapsed {\n",
              "        cursor: pointer;\n",
              "        max-height: 100px;\n",
              "      }\n",
              "    </style>\n",
              "    <pre style=\"white-space: initial; background:\n",
              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>pandas.core.frame.DataFrame</b><br/>def __init__(data=None, index: Axes | None=None, columns: Axes | None=None, dtype: Dtype | None=None, copy: bool | None=None) -&gt; None</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py</a>Two-dimensional, size-mutable, potentially heterogeneous tabular data.\n",
              "\n",
              "Data structure also contains labeled axes (rows and columns).\n",
              "Arithmetic operations align on both row and column labels. Can be\n",
              "thought of as a dict-like container for Series objects. The primary\n",
              "pandas data structure.\n",
              "\n",
              "Parameters\n",
              "----------\n",
              "data : ndarray (structured or homogeneous), Iterable, dict, or DataFrame\n",
              "    Dict can contain Series, arrays, constants, dataclass or list-like objects. If\n",
              "    data is a dict, column order follows insertion-order. If a dict contains Series\n",
              "    which have an index defined, it is aligned by its index.\n",
              "\n",
              "    .. versionchanged:: 0.25.0\n",
              "       If data is a list of dicts, column order follows insertion-order.\n",
              "\n",
              "index : Index or array-like\n",
              "    Index to use for resulting frame. Will default to RangeIndex if\n",
              "    no indexing information part of input data and no index provided.\n",
              "columns : Index or array-like\n",
              "    Column labels to use for resulting frame when data does not have them,\n",
              "    defaulting to RangeIndex(0, 1, 2, ..., n). If data contains column labels,\n",
              "    will perform column selection instead.\n",
              "dtype : dtype, default None\n",
              "    Data type to force. Only a single dtype is allowed. If None, infer.\n",
              "copy : bool or None, default None\n",
              "    Copy data from inputs.\n",
              "    For dict data, the default of None behaves like ``copy=True``.  For DataFrame\n",
              "    or 2d ndarray input, the default of None behaves like ``copy=False``.\n",
              "    If data is a dict containing one or more Series (possibly of different dtypes),\n",
              "    ``copy=False`` will ensure that these inputs are not copied.\n",
              "\n",
              "    .. versionchanged:: 1.3.0\n",
              "\n",
              "See Also\n",
              "--------\n",
              "DataFrame.from_records : Constructor from tuples, also record arrays.\n",
              "DataFrame.from_dict : From dicts of Series, arrays, or dicts.\n",
              "read_csv : Read a comma-separated values (csv) file into DataFrame.\n",
              "read_table : Read general delimited file into DataFrame.\n",
              "read_clipboard : Read text from clipboard into DataFrame.\n",
              "\n",
              "Notes\n",
              "-----\n",
              "Please reference the :ref:`User Guide &lt;basics.dataframe&gt;` for more information.\n",
              "\n",
              "Examples\n",
              "--------\n",
              "Constructing DataFrame from a dictionary.\n",
              "\n",
              "&gt;&gt;&gt; d = {&#x27;col1&#x27;: [1, 2], &#x27;col2&#x27;: [3, 4]}\n",
              "&gt;&gt;&gt; df = pd.DataFrame(data=d)\n",
              "&gt;&gt;&gt; df\n",
              "   col1  col2\n",
              "0     1     3\n",
              "1     2     4\n",
              "\n",
              "Notice that the inferred dtype is int64.\n",
              "\n",
              "&gt;&gt;&gt; df.dtypes\n",
              "col1    int64\n",
              "col2    int64\n",
              "dtype: object\n",
              "\n",
              "To enforce a single dtype:\n",
              "\n",
              "&gt;&gt;&gt; df = pd.DataFrame(data=d, dtype=np.int8)\n",
              "&gt;&gt;&gt; df.dtypes\n",
              "col1    int8\n",
              "col2    int8\n",
              "dtype: object\n",
              "\n",
              "Constructing DataFrame from a dictionary including Series:\n",
              "\n",
              "&gt;&gt;&gt; d = {&#x27;col1&#x27;: [0, 1, 2, 3], &#x27;col2&#x27;: pd.Series([2, 3], index=[2, 3])}\n",
              "&gt;&gt;&gt; pd.DataFrame(data=d, index=[0, 1, 2, 3])\n",
              "   col1  col2\n",
              "0     0   NaN\n",
              "1     1   NaN\n",
              "2     2   2.0\n",
              "3     3   3.0\n",
              "\n",
              "Constructing DataFrame from numpy ndarray:\n",
              "\n",
              "&gt;&gt;&gt; df2 = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),\n",
              "...                    columns=[&#x27;a&#x27;, &#x27;b&#x27;, &#x27;c&#x27;])\n",
              "&gt;&gt;&gt; df2\n",
              "   a  b  c\n",
              "0  1  2  3\n",
              "1  4  5  6\n",
              "2  7  8  9\n",
              "\n",
              "Constructing DataFrame from a numpy ndarray that has labeled columns:\n",
              "\n",
              "&gt;&gt;&gt; data = np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)],\n",
              "...                 dtype=[(&quot;a&quot;, &quot;i4&quot;), (&quot;b&quot;, &quot;i4&quot;), (&quot;c&quot;, &quot;i4&quot;)])\n",
              "&gt;&gt;&gt; df3 = pd.DataFrame(data, columns=[&#x27;c&#x27;, &#x27;a&#x27;])\n",
              "...\n",
              "&gt;&gt;&gt; df3\n",
              "   c  a\n",
              "0  3  1\n",
              "1  6  4\n",
              "2  9  7\n",
              "\n",
              "Constructing DataFrame from dataclass:\n",
              "\n",
              "&gt;&gt;&gt; from dataclasses import make_dataclass\n",
              "&gt;&gt;&gt; Point = make_dataclass(&quot;Point&quot;, [(&quot;x&quot;, int), (&quot;y&quot;, int)])\n",
              "&gt;&gt;&gt; pd.DataFrame([Point(0, 0), Point(0, 3), Point(2, 3)])\n",
              "   x  y\n",
              "0  0  0\n",
              "1  0  3\n",
              "2  2  3</pre>\n",
              "      <script>\n",
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              "      }\n",
              "      </script>\n",
              "      </div>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Extracting columns into separate lists\n",
        "smiles_list = df2['smiles'].tolist()\n",
        "aa_sequence_list = df2['aa_sequence'].tolist()\n",
        "affinity_score_list = df2['affinity_score'].tolist()\n"
      ],
      "metadata": {
        "id": "_whXlTmw3BdF"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "smiles_list[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "5wqzrpO2_ReZ",
        "outputId": "01b7db65-97d1-40d4-b567-ac931b712715"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'CNC(=O)c1c(F)cccc1Nc1nc(Nc2cc3N(CCc3cc2OC)C(=O)CN(C)C)nc2[nH]ccc12'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aa_sequence_list[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "id": "UjF51j78_Y2s",
        "outputId": "f1ddaffc-22c2-4167-ef76-eff1feeb3fdc"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'MALDYLLLLLLASAVAAMEETLMDTRTATAELGWTANPASGWEEVSGYDENLNTIRTYQVCNVFEPNQNNWLLTTFINRRGAHRIYTEMRFTVRDCSSLPNVPGSCKETFNLYYYETDSVIATKKSAFWSEAPYLKVDTIAADESFSQVDFGGRLMKVNTEVRSFGPLTRNGFYLAFQDYGACMSLLSVRVFFKKCPSIVQNFAVFPETMTGAESTSLVIARGTCIPNAEEVDVPIKLYCNGDGEWMVPIGRCTCKPGYEPENSVACKACPAGTFKASQEAEGCSHCPSNSRSPAEASPICTCRTGYYRADFDPPEVACTSVPSGPRNVISIVNETSIILEWHPPRETGGRDDVTYNIICKKCRADRRSCSRCDDNVEFVPRQLGLTECRVSISSLWAHTPYTFDIQAINGVSSKSPFPPQHVSVNITTNQAAPSTVPIMHQVSATMRSITLSWPQPEQPNGIILDYEIRYYEKEHNEFNSSMARSQTNTARIDGLRPGMVYVVQVRARTVAGYGKFSGKMCFQTLTDDDYKSELREQLPLIAGSAAAGVVFVVSLVAISIVCSRKRAYSKEAVYSDKLQHYSTGRGSPGMKIYIDPFTYEDPNEAVREFAKEIDVSFVKIEEVIGAGEFGEVYKGRLKLPGKREIYVAIKTLKAGYSEKQRRDFLSEASIMGQFDHPNIIRLEGVVTKSRPVMIITEFMENGALDSFLRQNDGQFTVIQLVGMLRGIAAGMKYLAEMNYVHRDLAARNILVNSNLVCKVSDFGLSRYLQDDTSDPTYTSSLGGKIPVRWTAPEAIAYRKFTSASDVWSYGIVMWEVMSFGERPYWDMSNQDVINAIEQDYRLPPPMDCPAALHQLMLDCWQKDRNSRPRFAEIVNTLDKMIRNPASLKTVATITAVPSQPLLDRSIPDFTAFTTVDDWLSAIKMVQYRDSFLTAGFTSLQLVTQMTSEDLLRIGITLAGHQKKILNSIHSMRVQISQSPTAMA'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "affinity_score_list[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zKr2KAVD_RPl",
        "outputId": "fc653e24-469f-4659-c532-4834761770f5"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5.0"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Train_Test_split**"
      ],
      "metadata": {
        "id": "Tdiv5vB5EwKv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "split = int(0.9 * len(smiles_list))\n",
        "train_smiles = smiles_list[:split]\n",
        "test_smiles = smiles_list[split:]\n",
        "train_proteins = aa_sequence_list[:split]\n",
        "test_proteins = aa_sequence_list[split:]\n",
        "train_labels = affinity_score_list[:split]\n",
        "test_labels = affinity_score_list[split:]"
      ],
      "metadata": {
        "id": "6WccnG8-Cv4A"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(train_smiles), len(train_proteins), len(train_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dxwebb-0FGFM",
        "outputId": "d66894c3-395a-4299-da51-d0f212efa9a4"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(16614, 16614, 16614)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Our input data is in form of strings so we need to encode them"
      ],
      "metadata": {
        "id": "q-QIHXYdBmnz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Tokenize smiles\n",
        "\n",
        "# train (this set is performed on only training data, never on test data)\n",
        "tokenizer_smiles = Tokenizer(char_level = True)\n",
        "tokenizer_smiles.fit_on_texts(train_smiles)\n",
        "\n",
        "# word index is just a dictionary that shows us what index corresponds to what character\n",
        "word_index_smiles = tokenizer_smiles.word_index\n",
        "vocab_size_smiles = len(word_index_smiles)\n",
        "\n",
        "#every drug in our dataset is converted into sequence of numbers\n",
        "train_sequences_smiles = tokenizer_smiles.texts_to_sequences(train_smiles)\n",
        "'''Every smile file has not the same length, but our neural network expects a\n",
        "fixed length input so to do that we use padding layers.\n",
        "`maxlen =85` means the maximum length of input sequence, it is 85 because authors of papaer have stated it. '''\n",
        "train_padded_smiles = pad_sequences(train_sequences_smiles, truncating = 'post', padding = 'post', maxlen = 85)\n",
        "\n",
        "\n",
        "#test\n",
        "test_sequences_smiles = tokenizer_smiles.texts_to_sequences(test_smiles)\n",
        "test_padded_smiles = pad_sequences(test_sequences_smiles, truncating = 'post', padding = 'post', maxlen = 85)"
      ],
      "metadata": {
        "id": "RuTqtFj0y_HU"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Tokenize Proteins\n",
        "\n",
        "#train\n",
        "tokenizer_proteins = Tokenizer(char_level = True)\n",
        "tokenizer_proteins.fit_on_texts(train_proteins)\n",
        "\n",
        "word_index_proteins = tokenizer_proteins.word_index\n",
        "vocab_size_proteins = len(word_index_proteins)\n",
        "\n",
        "train_sequences_proteins = tokenizer_proteins.texts_to_sequences(train_proteins)\n",
        "train_padded_proteins = pad_sequences(train_sequences_proteins, truncating  = 'post', padding = 'post', maxlen = 1200)\n",
        "\n",
        "#test\n",
        "test_sequences_proteins = tokenizer_proteins.texts_to_sequences(test_proteins)\n",
        "test_padded_proteins = pad_sequences(test_sequences_proteins, truncating = 'post', padding = 'post', maxlen = 1200)"
      ],
      "metadata": {
        "id": "YNDr4NIbN3JO"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(vocab_size_smiles)\n",
        "print(vocab_size_proteins)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8G3wNeN1P_v0",
        "outputId": "68b7172c-4656-488f-fa2d-0af1b6fc4e0a"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "30\n",
            "20\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# convert to numpy arrays and check dimensions\n",
        "train_smiles_array = np.array(train_padded_smiles)\n",
        "test_smiles_array = np.array(test_padded_smiles)\n",
        "train_proteins_array = np.array(train_padded_proteins)\n",
        "test_proteins_array = np.array(test_padded_proteins)\n",
        "train_labels_array = np.array(train_labels, dtype = 'float32')\n",
        "test_labels_array = np.array(test_labels, dtype = 'float32')\n",
        "\n",
        "print(f\"Train smiles array shape: {train_smiles_array.shape}\")\n",
        "print(f\"Test smile array shape: {test_smiles_array.shape}\")\n",
        "print(f\" Train protein array shape: {train_proteins_array.shape}\")\n",
        "print(f\"Test protein array shape: {test_proteins_array.shape}\")\n",
        "print(f\"Train labels array shape: {train_labels_array.shape}\")\n",
        "print(f\"Test labels array shape: {test_labels_array.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dSphNqSzQksx",
        "outputId": "85181856-5541-4fc7-afbe-31931625f5b7"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train smiles array shape: (16614, 85)\n",
            "Test smile array shape: (1847, 85)\n",
            " Train protein array shape: (16614, 1200)\n",
            "Test protein array shape: (1847, 1200)\n",
            "Train labels array shape: (16614,)\n",
            "Test labels array shape: (1847,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Deep DTA Model**"
      ],
      "metadata": {
        "id": "Ze-XdUqnXXyq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.layers import Input, Embedding, Concatenate, Conv1D, GlobalAveragePooling1D, Dense, Dropout, GlobalMaxPooling1D\n",
        "from keras.models import Model\n",
        "\n",
        "# Define two input layers i. one for smile sequence and one for protein\n",
        "smile_input = Input((85,))\n",
        "protein_input = Input((1200,))\n",
        "\n",
        "#smiles encoder\n",
        "embed_smiles = Embedding(vocab_size_smiles+1, 128, input_length = 58)(smile_input)\n",
        "conv_smiles = Conv1D(32, 4, activation = 'relu')(embed_smiles)\n",
        "conv_smiles = Conv1D(64, 6, activation = 'relu')(conv_smiles)\n",
        "conv_smiles = Conv1D(96, 8, activation = 'relu')(conv_smiles)\n",
        "max_smiles = GlobalMaxPooling1D()(conv_smiles)\n",
        "#flat_smiles = Flatten()(max_smiles)\n",
        "\n",
        "#proteins encoder\n",
        "embed_protein = Embedding(vocab_size_proteins+1, 128, input_length = 1200)(protein_input)\n",
        "conv_protein = Conv1D(32, 4, activation = \"relu\")(embed_protein)\n",
        "conv_protein = Conv1D(64, 8, activation = \"relu\")(conv_protein)\n",
        "conv_protein = Conv1D(96, 12, activation = \"relu\")(conv_protein)\n",
        "max_protein = GlobalMaxPooling1D()(conv_protein)\n",
        "\n",
        "# concatenate the two encoder outputs\n",
        "concat_layer = Concatenate()([max_smiles, max_protein])\n",
        "\n",
        "# decoder\n",
        "output = Dense(1024, activation = \"relu\")(concat_layer)\n",
        "output = Dropout(0.1)(output)\n",
        "output = Dense(1024, activation = \"relu\")(output)\n",
        "output = Dropout(0.1)(output)\n",
        "output = Dense(512, activation = \"relu\")(output)\n",
        "output = Dense(1)(output)\n",
        "\n",
        "# define model with a list of two inputs\n",
        "model = Model(inputs = [smile_input, protein_input], outputs = output)"
      ],
      "metadata": {
        "id": "m0Zd7yiRWK8q"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# compile the model\n",
        "optimizer = optimizers.Adam(learning_rate = 0.001)\n",
        "model.compile(loss = \"mse\", optimizer = optimizer, metrics = ['mse'])"
      ],
      "metadata": {
        "id": "06UzeDmsEfYN"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# visualize the model\n",
        "tf.keras.utils.plot_model(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "FhFAu-LIEvPh",
        "outputId": "bfcaaff8-9d47-4891-fad8-e831f70433eb"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XLGjcNPKE4sB",
        "outputId": "84f9ca42-ce98-4513-dab9-0f145d2f7ee5"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)        [(None, 85)]                 0         []                            \n",
            "                                                                                                  \n",
            " input_2 (InputLayer)        [(None, 1200)]               0         []                            \n",
            "                                                                                                  \n",
            " embedding (Embedding)       (None, 85, 128)              3968      ['input_1[0][0]']             \n",
            "                                                                                                  \n",
            " embedding_1 (Embedding)     (None, 1200, 128)            2688      ['input_2[0][0]']             \n",
            "                                                                                                  \n",
            " conv1d (Conv1D)             (None, 82, 32)               16416     ['embedding[0][0]']           \n",
            "                                                                                                  \n",
            " conv1d_3 (Conv1D)           (None, 1197, 32)             16416     ['embedding_1[0][0]']         \n",
            "                                                                                                  \n",
            " conv1d_1 (Conv1D)           (None, 77, 64)               12352     ['conv1d[0][0]']              \n",
            "                                                                                                  \n",
            " conv1d_4 (Conv1D)           (None, 1190, 64)             16448     ['conv1d_3[0][0]']            \n",
            "                                                                                                  \n",
            " conv1d_2 (Conv1D)           (None, 70, 96)               49248     ['conv1d_1[0][0]']            \n",
            "                                                                                                  \n",
            " conv1d_5 (Conv1D)           (None, 1179, 96)             73824     ['conv1d_4[0][0]']            \n",
            "                                                                                                  \n",
            " global_max_pooling1d (Glob  (None, 96)                   0         ['conv1d_2[0][0]']            \n",
            " alMaxPooling1D)                                                                                  \n",
            "                                                                                                  \n",
            " global_max_pooling1d_1 (Gl  (None, 96)                   0         ['conv1d_5[0][0]']            \n",
            " obalMaxPooling1D)                                                                                \n",
            "                                                                                                  \n",
            " concatenate (Concatenate)   (None, 192)                  0         ['global_max_pooling1d[0][0]',\n",
            "                                                                     'global_max_pooling1d_1[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " dense (Dense)               (None, 1024)                 197632    ['concatenate[0][0]']         \n",
            "                                                                                                  \n",
            " dropout (Dropout)           (None, 1024)                 0         ['dense[0][0]']               \n",
            "                                                                                                  \n",
            " dense_1 (Dense)             (None, 1024)                 1049600   ['dropout[0][0]']             \n",
            "                                                                                                  \n",
            " dropout_1 (Dropout)         (None, 1024)                 0         ['dense_1[0][0]']             \n",
            "                                                                                                  \n",
            " dense_2 (Dense)             (None, 512)                  524800    ['dropout_1[0][0]']           \n",
            "                                                                                                  \n",
            " dense_3 (Dense)             (None, 1)                    513       ['dense_2[0][0]']             \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 1963905 (7.49 MB)\n",
            "Trainable params: 1963905 (7.49 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "\n",
        "early_stopping = EarlyStopping(\n",
        "    min_delta = 0.01, #minimum amout of change to count as an improvement\n",
        "    patience = 5, #how many epochs to wait before stopping\n",
        "    restore_best_weights = True,\n",
        ")\n",
        "\n",
        "history = model.fit([train_smiles_array, train_proteins_array], train_labels_array,\n",
        "                    epochs = 500, batch_size = 32, validation_data = ([test_smiles_array, test_proteins_array], test_labels_array), callbacks=[early_stopping])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dxLB_ILGFP6I",
        "outputId": "c253f3d9-5203-41a6-9ebe-0592bfa468aa"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/500\n",
            "520/520 [==============================] - 20s 19ms/step - loss: 1.3891 - mse: 1.3891 - val_loss: 0.9689 - val_mse: 0.9689\n",
            "Epoch 2/500\n",
            "520/520 [==============================] - 8s 15ms/step - loss: 0.8681 - mse: 0.8681 - val_loss: 0.7683 - val_mse: 0.7683\n",
            "Epoch 3/500\n",
            "520/520 [==============================] - 7s 14ms/step - loss: 0.8388 - mse: 0.8388 - val_loss: 0.7227 - val_mse: 0.7227\n",
            "Epoch 4/500\n",
            "520/520 [==============================] - 8s 15ms/step - loss: 0.8112 - mse: 0.8112 - val_loss: 0.7735 - val_mse: 0.7735\n",
            "Epoch 5/500\n",
            "520/520 [==============================] - 7s 14ms/step - loss: 0.7471 - mse: 0.7471 - val_loss: 0.6988 - val_mse: 0.6988\n",
            "Epoch 6/500\n",
            "520/520 [==============================] - 8s 15ms/step - loss: 0.6977 - mse: 0.6977 - val_loss: 0.6190 - val_mse: 0.6190\n",
            "Epoch 7/500\n",
            "520/520 [==============================] - 8s 15ms/step - loss: 0.6101 - mse: 0.6101 - val_loss: 0.6944 - val_mse: 0.6944\n",
            "Epoch 8/500\n",
            "520/520 [==============================] - 7s 14ms/step - loss: 0.5768 - mse: 0.5768 - val_loss: 0.7109 - val_mse: 0.7109\n",
            "Epoch 9/500\n",
            "520/520 [==============================] - 8s 15ms/step - loss: 0.5333 - mse: 0.5333 - val_loss: 0.5343 - val_mse: 0.5343\n",
            "Epoch 10/500\n",
            "520/520 [==============================] - 7s 14ms/step - loss: 0.5362 - mse: 0.5362 - val_loss: 0.5198 - val_mse: 0.5198\n",
            "Epoch 11/500\n",
            "520/520 [==============================] - 8s 15ms/step - loss: 0.5065 - mse: 0.5065 - val_loss: 0.5996 - val_mse: 0.5996\n",
            "Epoch 12/500\n",
            "520/520 [==============================] - 8s 16ms/step - loss: 0.4912 - mse: 0.4912 - val_loss: 0.5973 - val_mse: 0.5973\n",
            "Epoch 13/500\n",
            "520/520 [==============================] - 7s 14ms/step - loss: 0.4682 - mse: 0.4682 - val_loss: 0.5156 - val_mse: 0.5156\n",
            "Epoch 14/500\n",
            "520/520 [==============================] - 8s 15ms/step - loss: 0.4440 - mse: 0.4440 - val_loss: 0.4908 - val_mse: 0.4908\n",
            "Epoch 15/500\n",
            "520/520 [==============================] - 7s 14ms/step - loss: 0.4379 - mse: 0.4379 - val_loss: 0.4469 - val_mse: 0.4469\n",
            "Epoch 16/500\n",
            "520/520 [==============================] - 8s 15ms/step - loss: 0.4213 - mse: 0.4213 - val_loss: 0.5066 - val_mse: 0.5066\n",
            "Epoch 17/500\n",
            "520/520 [==============================] - 8s 15ms/step - loss: 0.4158 - mse: 0.4158 - val_loss: 0.4551 - val_mse: 0.4551\n",
            "Epoch 18/500\n",
            "520/520 [==============================] - 8s 15ms/step - loss: 0.3900 - mse: 0.3900 - val_loss: 0.5250 - val_mse: 0.5250\n",
            "Epoch 19/500\n",
            "520/520 [==============================] - 8s 15ms/step - loss: 0.3863 - mse: 0.3863 - val_loss: 0.4840 - val_mse: 0.4840\n",
            "Epoch 20/500\n",
            "520/520 [==============================] - 8s 15ms/step - loss: 0.3621 - mse: 0.3621 - val_loss: 0.4501 - val_mse: 0.4501\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#---------------------------------------------------------------\n",
        "# Retrieve a list of list results on training and test data\n",
        "#Sets for each training epoch\n",
        "#---------------------------------------------------------------\n",
        "mse = history.history['mse']\n",
        "val_mse = history.history['val_mse']\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs = range(len(mse)) #Get number of epochs\n",
        "\n",
        "#---------------------------------------------------------------\n",
        "# Plot training and validation mse per epoch\n",
        "#----------------------------------------------------------------\n",
        "plt.plot(epochs, mse, 'r')\n",
        "plt.plot(epochs, val_mse, 'b')\n",
        "plt.title(\"Training and validation MSE\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"MSE\")\n",
        "plt.legend([\"Training MSE\", \"Validation MSE\"])\n",
        "\n",
        "plt.figure()"
      ],
      "metadata": {
        "id": "KykoE85OFDgg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "outputId": "9be9fe19-49af-4d7a-c031-d04d55663ba2"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save(\"replicating_deepDTA_0.h5\")"
      ],
      "metadata": {
        "id": "M7Y6oVvXJHnL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "665f7f09-7bb1-4d74-cbc3-a6e824bd2a6a"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Make predications**"
      ],
      "metadata": {
        "id": "OzJ1AXX5KI8h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to predict drug-protein pKd\n",
        "\n",
        "def predict_pKd(drug, protein):\n",
        "    drug_sequence = tokenizer_smiles.texts_to_sequences([drug])\n",
        "    drug_padded = pad_sequences(drug_sequence, truncating= \"post\", padding = \"post\", maxlen = 85)\n",
        "    protein_sequence = tokenizer_proteins.texts_to_sequences([protein])\n",
        "    protein_padded = pad_sequences(protein_sequence, truncating = \"post\", padding = \"post\", maxlen = 1200)\n",
        "    prediction = model.predict([tf.expand_dims(drug_padded, axis = -1), tf.expand_dims(protein_padded, axis = -1)])\n",
        "\n",
        "    return(prediction.item())"
      ],
      "metadata": {
        "id": "RHCC-HmHJwB_"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "drug = test_smiles[131]\n",
        "protein = test_proteins[131]\n",
        "label = test_labels[131]\n",
        "\n",
        "print(label)\n",
        "predict_pKd(drug = drug, protein = protein)"
      ],
      "metadata": {
        "id": "WJSBjAAkPzkK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "36ddea74-b28c-46fc-bb93-0a373badd150"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5.346787486\n",
            "1/1 [==============================] - 1s 517ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5.145613670349121"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_labels.index(max(test_labels))"
      ],
      "metadata": {
        "id": "nHDIiXK_Pd1e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3a19c489-d8ba-4b73-c476-f53127afbd7f"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1232"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qukeu16MCozc"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}