update notebooks

bindingdb.ipynb

@@ -783,7 +783,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.4"
+   "version": "3.8.10"
   }
  },
  "nbformat": 4,

biolip.ipynb

@@ -452,7 +452,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.4"
+   "version": "3.8.10"
   }
  },
  "nbformat": 4,

combine_dbs.ipynb

@@ -760,7 +760,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "4ccf2ee5-d369-4c0e-bb91-792765d661bf",
    "metadata": {},
    "outputs": [],
@@ -1368,7 +1368,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 3,
    "id": "4e2d89f7-f6ea-41de-a13b-4a184b4fd580",
    "metadata": {},
    "outputs": [],
@@ -1388,7 +1388,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 4,
    "id": "8f949038-d07d-4d3a-a47e-b825cc9018ca",
    "metadata": {},
    "outputs": [],
@@ -1398,7 +1398,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 5,
    "id": "0c027988-0b44-4010-ad61-7d70eead1654",
    "metadata": {},
    "outputs": [],
@@ -1408,7 +1408,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 33,
    "id": "6aeba020-b6ff-4633-902e-4df74463eb2f",
    "metadata": {},
    "outputs": [],
@@ -1459,13 +1459,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 9,
+   "id": "469cf0dd-7b87-4245-973c-2a445e1fcca9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['seq', 'smiles', 'affinity_uM', 'neg_log10_affinity_M', 'affinity'], dtype='object')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
    "id": "d91c0d91-474c-4ab2-9a5e-3b7861f7a832",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1477,58 +1498,58 @@
     }
    ],
    "source": [
-    "ax = df['affinity'].hist(bins=100,density=True)\n",
-    "ax.set_xlabel('affinity[M]',fontsize=16)\n",
+    "ax = df['neg_log10_affinity_M'].hist(bins=50,density=True)\n",
+    "ax.set_xlabel('-$\\log_{10}$ affinity[M]',fontsize=16)\n",
     "ax.set_ylabel('probability',fontsize=16)\n",
-    "ax.figure.savefig('affinity.pdf')"
+    "ax.figure.savefig('affinity_neglog10_M.pdf')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "11571486-901c-474b-a8ec-215ec5c9e661",
+   "execution_count": 16,
+   "id": "9ca8df46-15d3-40f9-b304-dd6e5597be5e",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "1848949"
+       "2938"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "len(df)"
+    "(df['neg_log10_affinity_M']<2).sum()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "9ca8df46-15d3-40f9-b304-dd6e5597be5e",
+   "execution_count": 1,
+   "id": "88cf855d-704f-4ed4-827e-9f4e3288b3a0",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "5.142857142857143"
+       "0.001298888888888889"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "1.8/0.35"
+    "2338/1.8e6"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "88cf855d-704f-4ed4-827e-9f4e3288b3a0",
+   "id": "0e895ef5-1812-46c7-a4c2-dd6619b49157",
    "metadata": {},
    "outputs": [],
    "source": []

moad.ipynb

@@ -505,7 +505,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.4"
+   "version": "3.8.10"
   }
  },
  "nbformat": 4,

pdbbind.ipynb

@@ -288,7 +288,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.4"
+   "version": "3.8.10"
   }
  },
  "nbformat": 4,