07-rdkit/11-rdkit分子3D构象生成与优化.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# rdkit 分子3D构象生成与优化\n",
    "RDKit是一款非常强大的分子信息学工具包，其中包含了分子3D构象生成与优化的功能。通过RDKit，您可以将一个分子的2D结构转化为3D结构，并进行能量最小化优化，得到最稳定的构象。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 构象生成算法概述\n",
    "- 基于距离（distance-based）\n",
    "- 基于知识（knowledge-based）\n",
    "\n",
    "下面是基本代码逻辑"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "     RDKit          3D\n",
      "\n",
      " 13 13  0  0  0  0  0  0  0  0999 V2000\n",
      "    2.8746   -1.9481    0.7697 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    1.4519   -1.4942    0.7243 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.6610   -1.5944    1.6526 O   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    1.1953   -0.9612   -0.5309 O   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -0.1020   -0.4535   -0.6230 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -1.0417   -1.2552   -1.2736 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -2.3498   -0.8037   -1.4186 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -2.7129    0.4472   -0.9263 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -1.7653    1.2531   -0.2971 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -0.4475    0.8156   -0.1433 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.5472    1.7048    0.4969 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    1.7485    1.4985    0.5415 O   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -0.0592    2.7912    1.0278 O   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "  1  2  1  0\n",
      "  2  3  2  0\n",
      "  2  4  1  0\n",
      "  4  5  1  0\n",
      "  5  6  2  0\n",
      "  6  7  1  0\n",
      "  7  8  2  0\n",
      "  8  9  1  0\n",
      "  9 10  2  0\n",
      " 10 11  1  0\n",
      " 11 12  2  0\n",
      " 11 13  1  0\n",
      " 10  5  1  0\n",
      "M  END\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[08:51:13] Molecule does not have explicit Hs. Consider calling AddHs()\n",
      "[08:51:13] Molecule does not have explicit Hs. Consider calling AddHs()\n"
     ]
    }
   ],
   "source": [
    "from rdkit import Chem\n",
    "from rdkit.Chem import Draw\n",
    "from rdkit.Chem import AllChem\n",
    "\n",
    "# 读入分子SMILES字符串\n",
    "mol = Chem.MolFromSmiles('CC(=O)OC1=CC=CC=C1C(=O)O')\n",
    "\n",
    "# 生成初始3D构象\n",
    "AllChem.EmbedMolecule(mol)\n",
    "\n",
    "# 进行能量最小化优化\n",
    "AllChem.MMFFOptimizeMolecule(mol)\n",
    "\n",
    "# 输出优化后的3D结构\n",
    "print(Chem.MolToMolBlock(mol))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 基于距离（distance-based）\n",
    "\n",
    "传统的距离几何（Distance Geometry）法生成构象步骤：\n",
    "\n",
    "- (1) 通过分子的连接表信息和一套规则，生成分子的连接边界矩阵（molecule’s distance bounds matrix ）\n",
    "- (2) 使用三角形边界平滑算法（triangle-bounds smoothing algorithm），对边界矩阵进行平滑处理\n",
    "- (3) 根据边界矩阵，随机产生一个距离矩阵。\n",
    "- (4) 把产生的距离矩阵映射到三维空间中，并为每个原子计算坐标。\n",
    "- (5) 对计算的坐标结果使用力场和边界矩阵进行粗略的优化。\n",
    "\n",
    "这种方法虽然计算速度快，也能得到分子的三维坐标，但纯粹基于距离计算会导致部分结构的扭曲，也就是结果比较丑。想得到更好的结构，需要进行更细致的力场优化，比如再使用rdkit的通用力场（Universal Force Field，UFF）进行处理。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 基于知识（knowledge-based）\n",
    "Riniker和Landrum开发了一种（Experimental-Torsion Basic Knowledge Distance Geometry，ETKDG）的方法。他们从晶体结构数据库的小分子结构中总结了一些规则（扭转角的倾向性，torsion angle preference，符合某种SMARTS的结构更倾向于生成某种固定的构象），并用这套规则来修正距离几何算法产生的构象。如果使用了ETKDG，就可以不再使用力场进行优化了。\n",
    "\n",
    "在rdkit中，目前已经将ETKDG作为默认的构象生成方法。使用代码实现这种方法比上面的废话都要简单，几行代码就能搞定了。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 代码示例\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32 54\n"
     ]
    }
   ],
   "source": [
    "mol = Chem.MolFromSmiles('CC1=CC=C(C=C1)NC2=C3C(=C(C=C2)NC4=CC=C(C=C4)C)C(=O)C5=CC=CC=C5C3=O')\n",
    "m3d = Chem.AddHs(mol)\n",
    "print(mol.GetNumAtoms(), m3d.GetNumAtoms())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 距离几何算法生成3D结构\n",
    "使用距离几何算法初始化3D坐标。\n",
    "\n",
    "生成3D构象：`AllChem.EmbedMolecule(mol, randomSeed, clearConfs, useExpTorsionAnglePrefs, useBasicKnowledge, …)`\n",
    "- mol：传入mol对象\n",
    "- randomSeed：随机种子，方便结果重复\n",
    "- clearConfs：清除已有构象，默认True\n",
    "- useExpTorsionAnglePrefs和useBasicKnowledge两个参数即控制是否使用ETKDG，默认都为True\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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Bh0dv3rw5d+5cf39/vHNeQS5cuIAQ8vHxkWcgdhgUdu0nTpwAgNmzZ/N4PAAIDg7W0MBsxIgReLD06NEjHFZqG+3IHVd+0oTcMaGhoY6OjoGBgQ8fPrx79678dSsrK7f/MmrUqLy8PNwPaQKcfILnnQqCEMJfuQ96JCOTyU6ePAkAM2fOnDFjBgDMmjVLXecooqv17nJGjx7N4/HGjBnD4XCWLVvm4eHh4eHx1vSAxWLh7VEA4OLiQq0/OL0nPz+/trb2rW3zDQ0NJSUleXl5JSUlpaWleXl5uJ2fny+VSnV1dSdOnEihJwpCldxv3bpVVFTUq1evN2/eVFdXu7m5OTo6qu/eu3A4HHd3d9w2MTEZMmRIu5doU+7yrTHUghCytLQ0MTFJSEgAABcXl3Xr1rX6TlNTU/mGS3Nz85ZrXuqDU/wyMzO3bt3KZrOLiooKCwvz8/MLCwvr6ured5W+vr5EIjl37hze7NuR4MAU3o6jDngkM3fuXNzHa26Sam1tvWrVKtx2cHD4xz/+0e4lXXAww+fzq6qqJBIJrprZspJZB2Nubm5hYbFhw4a3XjcwMLC2tnZwcLCysmrZGDBgwM2bN6dOnbp27drg4GCNPv3ehZLeXSKR4PHYtGnTxo8fz2QyceSgk6AdueOgu4b+nPKRkkbv0i41NTV3797l8/m+vr7Ozs62trb29vb29vZ2dnbvc+nOnTsMBmPChAmpqak//vjjjz/+2JEOt5R7RkaGaqetXL58+c2bN66urs+ePROLxePHj7e1taXUTfWgPDykCIsWLQKA2NhYTRiXl9bfuXMnUJcBoiw4UWTSpEkKribeuHEDAGxsbO7du8dkMg0MDF6/fq1pJ+Xk5ubiWFBhYeG2bdsYDIZq1U2CgoIAYNOmTThvcf/+/ZS7qg7aWVXV6FQVGzczM9Ni787n83ft2gUAK1ascHJyWrZsWbs1usaMGTNy5Mji4uJr164FBQU1NDT88MMPHeIsxMbGDh06VCwWMxiMpUuXDhgwgMViRUZGHj58WFlT7u7ujo6OPj4+qampenp6ODLTidDKl2z8+PEAQHmNMQyO9QYGBuKEpx07dmjiLm2zceNGABg3bhxe5XZyclJksQnXB+dwOL/99hubzWYwGHgTneaoqKjAeTIAMGXKFDxZX7JkCXZbV1f36tWrKpjdvHkzAPj7+1PusJpoR+7ymLQmjOMNL6GhoXjzVEJCgibu0gZCoRBvUrl69So+v0XxxD0ccf/qq6+WL1+OvzCa8/PChUvYTzMzs5MnTyKEbt26hXP1duzY8d133wGAsbHx48ePFbf56NGj0NBQfX39bt26UZjxSxXakTuevqi2y7tdNm3aBAArV67ERWnOnz+vibu0wdatWwFgxIgRP//8MwAMHDhQ8TyCZ8+e6ejosFisjIwMHKjF66zUUl+PwsLQ6NEvAcDLy6vlH+LUqVNMJpPJZJ45c2bOnDkAYGNjU1hY2LZBqVSalJQ0duxY/KBgMpkMBoPJZHZ8X9M22pE7nhVpqJrAihUrAODHH3/09PQEABXSzdVBLBZbW1tjmeJ9W0eOHFHKwpIlSwDAz88Pb48YOHCgCtv/2uD+fTRgAAJA+vpo375r734VcX/BZrNv3LiBf4dDhw4VCAStWuPz+bGxsfLK7lwuNyws7NWrV9h5Fot1/PhxCp1XEy3IvaGhAQD09fURQuXl5TNnznz16hWF9nEKXlxc3KBBgwAgJyeHQuPt8q9//QsAhgwZcuzYMQBwcHBQVqzl5eW4rvKvv/6Kx0JxcXGU+NbcjGJikJ4eAkBOTujRo/e+E2+nMjc3f/DgAd6cgTdbtnxPXl5eRESEfFnDwcEhJiam5W5rvLrHYrFULhxAOR0t9+bmZrzY1r17d6lUGhISgruEuLg4qpLmAgICAODMmTM4ZaAjt7E2NTXhVcmzZ8/i+ioHDhxQwQ4WioeHB/5dWVpaqlYHNC4OJSf/1V6/Hvn6IgDEZKLvvkMNDW1d2NzcPG3aNADo27fvw4cPLS0tw8LC5M+BzMxMeXUxAPD09OTxeK3GW/Gqp6+vrwrOa4KOk3t2dnZkZCRexeByuQwGY+TIkQ8fPpSfr+3j49PuGFERcNgnNTUV7wNsaPsPSylxcXE4DnPmzBkAsLOza2xsVMFOfX29nZ0dAJw4cQIPJyIjI1WwM3s2cnREFRXoxo0bAwc+TkpCPXuiDRuuPH36tN1rBQIBzkIZPXo0Htw3NjbyeDz58pOenl5gYGBGRkbbdvDheF5eXvjHFy9eULX1WwU0Lvf8/PxNmzY5OTnJQ5/9+vWbN28e3lJtZGS0Z8+eU6dO4RCYiYmJ+mtPH3/8MQDgREhDQ0NKPoUiNDc39+/fHwBOnjyJU5fUKRJ44MABAOjdu/etW7cYDIaRkVFJSYkiF9bXo/x8lJGBfv8dzZ6NYmPRl1+i6Ojo3r2PIYREIvT999+fO3dOEVMlJSX4YTVt2rRNmzbJ10ctLS0jIiIUry5WWlrapeS+cePGuro63I6MjGxoaKitrcUFBeRbtszMzEJDQ9PT0/Ggpba2NjQ0FP+Xt7d3VlYWrgcPAJ9++mlxcbHKzpw6dWrLli3Z2dkAYGtrS8kHVASckda/f3+8+65nz56t1oNWEKlUijvXbdu2TZ8+HQAWLVqEEOLz+c+ePUtPTz979uyePXsiIyPXrv11+nTk6YkGDEBcLgL469+UKWj2bPTyJZo5Ey1YEN2797GysubKymrF5Y4QevLkCZfLlVeFGDx4cHx8vLIPzNLS0oEDB+7bt2/fvn3R0dEfvNy9vLzk9QKGDx++efNm+VZrNpsdFBR0/vz5VosfXbhwAXfzxsbGsbGxPB4PT30sLCzU3CB89OhRLPeOqSYplUpxHCYhIQEPP3766Sc1bV66dAkAevXqlZOTo6Ojo6urKy/T0JKxY5fJJY7jLba2yN0dffvtX3IvLES9ekWz2W7u7uOmTp3av39/xeWOEPL29tbR0XF3d1d5WbC0tHTw4MEXL168ePHiwYMHu4LcX716JRKJcHHDxMREXDYjNjZW3uu/j/LycvlS8+TJk3/77bfPPvsM/xgYGKhsscKamprY2Fg8nsHboo2NjUNDQxUZraoDnlM6ODjgikjm5ubvi9wpxfbt28vKykQikampKQ7DGxkZ9evXb9SoUX5+fkuWLFmzZk18/JXTp9GtW+jpU/RWFTosd4TQ5MnRurrHnj0rR0iJwQxCqKamhs1mM5lMdRJ4utpgxsvLa/r06UFBQUFBQd27d6+pqVG2bCyPx8N/TlNT03379sXGxnI4HADo0aNHsjy48H6kUunVq1eDgoLk/Z+lpeXs2bPlR1AwmUw/P79r165pYtOkTCZzdXUFgP379+Ollk2bNlFoH6dGDhkyRNkn1blzf30BoqKig4OP4ReVkjtOs5syZYpS932LLij3loMZ1Tq20tJS+aa1wMDArKws+SpdYGBgdXV1q1cVFxfHxMQ4ODjIZe3t7c3j8eQhkaysrPnz58u/Bk5OTj///JraAQ7eLmlnZ4ezGk1NTRU/4KRdBAIBXuqXVyZTgUuXLt2/fx+3k5KSFE/fwOkeiYmJKt8aISSVSuVVg5qbm1utZ90xdCK5YxISEnBRY0tLy8TExC1btuBpwFv5Rrjoe2BgoDz6a2NjExER8b4Vq4qKipiYGHt7exubXixWs7ExCg1FlAxwCgsLcde+e/duvOlOXqqJEtavXw8Anp6eFNpUkHv37uEHbGcrOqsy1Mj92LFj8kIRP//8s5q/ndevX+PYOe7X79696+npmZ2djf83Nzc3IiJCXkIVR39TUlIUSStvampKSsr19PxrVsdkIj8/dO0akslQYSG6efOvt2Vno/eNxWpqatLT02NjY8PCwry9vXG/a21tbWZmJhAIFi1aZG5u/r4HkQrU1tbiBOYOq3ndkgULFoAGTo3UIlormtc2MpksNjbWyMgIAKysrM6fPy8Wi3k8nre3t7yEw8CBA2NiYhSvrdeSrCw0fz7S1/9L905OKCYGGRoifCJLZCRKSkIIocZGlJ2NEhJQRATy9UUODmjw4G/eCox0794dV9rAR/pQWx8Gp7xPmDCBQpsKIhAI8GqgCscTdVo6qdwxz549w2t4DAZDXneFy+UuXLjw3r176tuvqEAxMcjeHunpof37UUgImjgRyWQoMhLt3YscHBCDgVrG+ADQxIlnR4wYsXDhwh07dly7dg0P4ZYtWwYAipT1UYqqqiqcPHPr1i1qLSsCTnnvyINDOoBOLXeEkFQq/emnnzgcjoGBgZubW2xsrDqnyLZKUxO6dw+dPo02bUIREejQIRQZiU6cQEwm0tVFjo4oMBBFRiIeD2Vno1YzeV+/fo3j4tSmNOPUTjWjIiqDF4ZxqccuQ2eXO0KorKwMR7I1ehcsd6EQubujZctQUhL6z3+Q4rmMuHIQhcPciooKHIqVR1Q6koKCAgMDg27dulFyAEHnQWsVgBUH7z1VoZSuChgZwfffw8GDAAD9+oGOwoUa8Oaj2NhYoVBIiScbNmwQCoX+/v64SnrHIBAIcNkzOzu7nJwceVisy/AByF3TFfYwZmaAS4zNmAEBAfD/l/1qH3d3d09Pz9raWkXKLrdLaWlpXFwcg8FYu3at+tYUJy8vD28zxT7gQGRX4oORu4ZKjsmZMAF8fGDxYtiyBeLjYdw4pS3gzZ3bt2+XSqVqOrN+/XqxWBwYGIhXeQhUoZ2ySkrRAQUlMSUlEBcHbm6wfLkql/v5+fXr1+/Fixfnzp3DW0xUIz8/Pz4+nsVidXDXjrl16xauTVBTU4OT3roSpHf/H01NeePGRQ0efEK1y5lMJi71oUjl5TZYt25dY2PjnDlz8ObDDmbMmDHJycnJyckxMTEdf3dNQ3r3/1FXl3vjxloDg8kAKlbxDAkJWbt27d27d99XdA4hVNWCioqKyspK3J45c6a/v/+LFy8OHz7MYrHwIeMEavkA5N5hvbuVlVVoaGjLjVfKYmhouGjRopiYmJ9++onH40kkkrCwsPLy8qqqqjdv3mBZy2SyVq/t06ePv79/VFRUc3PzggULtDKQYLPZ9vb2uG1kZNS5yjtSAQMhpG0f2iEwMPDMmTM8Hi8wMFBzd/n8889xpea32spSUlLSp08fqVSam5vbt29fAwODxsbGlm8wb4GFhYWlpSVuDxkyRCqVurm54XX7Pn36qPuRCO9Aeve/ePLkSattZbG2tg4KCjp8+PDu3bu3b9+OE/fNzc25XC6LxWpqaiorK6v5L6WlpY8fP275o76+vp+fH9G6hvgA5D5gwICampqWp8hrAvTfA00BoLm5WR1Ty5YtO3z48N69e7Oysmpra/EYBh/01za6urpNTU25ubnNzc06ii9xERSmU/9OL1y40LNnT1zzMTExUSaT4eItmqC5uTkpKQm33xp+KIujo6OFhYWenh4+QRdjYGBgZmZmZmZmbW1tZWVl9l9a/mhiYuLq6vr48eNdu3YpchYFQWm0nMTQJlFRUfJioitWrNBEtUQ5Tk5OrbZVAH8/HRwcrl69+vjx4+LiYsWrzVy+fBkADA0NX+JNpgRK6exxd4SQTCZ7XzSjE9LU1IQj1j/++KO3t7erq6u1tbW8cEW7TJ48ec6cOfX19V9//bUm3aQr2v6+tUVUVJSHh4e/v7+/v3///v012rtfuXKl1bay7N69GwCcnZ2VOj24JZWVlTgf7ujRoyq7QWiVzi73DhvMUIJYLMax6rNnz6pjB+eZmZubq7ZXi/A+Ovtg5sMiLi6uqKhoyJAh8opoqjF//nxvb++qqqrlqqXvEN6Htr9vbXHjxg35juwrV6508k2TYrHYxsYGAFJSUtS3lpeXh7fqqjOyIrxFp5b7h8W2bdsAwM3NjarKTfhYgd69e3dM3T86QORODUKhEC+EXbx4kSqbEokEV0XtSqUvtAuROzXgTUDu7u7UFuV78OABi8XS0dHBVT0IakLkTgHyo/bUqWv3PnAOvYeHh4LHERPagERmKGDXrl0VFRWjRo3y8fGh3PjGjRv79Onz8OFDfOoTQR0+gATgTo5QKHRwcKisrLx27dqECRM0cYvLly9/+umnhoaG2dnZSidL3r4Nz59D//7wySea8O3DgshdXdavX79mzRpPT8/bt29r7i5BQUGnTp2aMmUKPuMAAGQyWV1dHZ/PZ9bX2wkEwOcDnw9CIYhEwOeDQADR0RAeDlIpTJwI166BVAp79mjOww8DbY+mPmw6rGRpZWUlPr7K0tLSwsKCzWbL/4LzBg16u7Qf/vfyJWpZN/iTTxClB3p+iHTqBODOz/bt26urq728vMapUKlDGczNzWNiYhYuXFhRUYFfYTAYpqamHA6H1aMHGBmBqSlwuWBkBBwOmJgAlwv/+Q+03Ijo6gq5udC7t0b97OQQuQNIpXDnDtTUgJsbvLs7s6kJDw+ei8UCkaiurk4sFtfX19fV1VVXV+OiA5GRkR3gZkhIyNdff93U1LR69WofHx+JRCISiYRCYbNIBDU1IBSCUAgCAdTUQGEhiEQwbBhUV//v+jdvQPP7fTs72n68aBuJBE2ahFavRvHxaPx4lJKCTp5E/fsjOztkZoZYLPnY4KP3lELw8fHpGE/z8vKYzFYiaaYGBq0PZn7/Hbm7oz/+QAih7Gzk5qZEzcsuCu1799OnYfBgWLcOAGDqVJg4EZYuhf/8539v0NEBLhe43MF2dpyGBi6Xy2azORyOsbHxoUOHZDJZeHh4x3i6efNmmUzGZrPx7icOh8PhcIyMjExNTRGXy2g5jOFwgMOBfv3gzBlYvRpqasDUFM6cUaLmZReF7p8f/vwThg79q21uDo2N4OMDGRkgkQCDAUwmiEQgEoFYHNzQ8J/q6oaGBj6fLxQKxWIxvqiwsLAD3CwvL09ISGAymZmZmY6OjopeZmQEhw9r0q8PDNrL3dQU+Pz//SiVwuPH4Ov77hvPOTnF//nnu6+vX79+7ty5OHtRc2zfvl0sFgcEBCihdcI70F7un30GoaEwZw4YGcHp0zB8OJiYQLduYGoKbDaw2WBqCoaGwGaP7t3beOJENpttYmJiZGRkYGBgbGwcFRWVk5Ozbdu2NWvWaM5HPp+Pz9JYuXKl5u5CC7Q9eegEnDuHpkxBU6ei0NC3z+Ftjzt37jAYDDabnZ+frxnnEEJow4YNADBx4kTN3YImELmrC65t9sUXX2jIvlgs7tmzJwCofEY7QQ6Ru7oUFBQYGhoyGAwNnSqDM8OGDRumCeN0g2REqoudnd23336LEAoPD0dUJyBJJBK8SWrVqlXUWqYpWv66dQkEAoGVlRUAHD9+nFrLCQkJADBo0CCVy3gQWkJ6dwrgcDjR0dEAsGLFivr6eqrMIoS2bNkCAN9//32r66kEZSG/RGqYP3++u7t7UVERHntQQnJycnZ2tp2dXVBQEFU2aQ6ROzUwmcydO3cyGIyYmBiq1lnx/teIiAh8CD1BfYjcKWPUqFEBAQH19fWrV69W39r169fv3btnaWkZEhKivjUChsidSjZv3mxgYHD48OEHDx6oaQoXmQkPD2+5k4OgJkTuVNKnTx8cjlQzKPno0aPU1FRjY+OvvvqKQvcIRO4Us2rVKisrq3v37vF4PJWNREdHI4S+/vprU2VP7ya0CdmaTT0HDx5cuHChra1tbm6uoaFhu++vr6+vqqoqKyurrKysrKzEOWd6enp5eXk4nE+gCiJ36pHJZMOHD8/MzFy/fv0PP/xQU1NTUlKCTxrDjXfbb1lwcHCQSCQvX74kMRlqIXLXCDdv3hw3bhybzZbvAmkDAwMDfOJkjx49zM3NORwOj8erqqpatWoVzoUkUAWRu0YQCoUWFha6uroCgcDMzEx+2Jj84LGWJ5BZWVkxGAz5tSKR6P79+5MmTZLJZJcvX9ZEZTLaQuSuEQ4cOLBo0aJPPvnk1q1bSl2YnZ09a9asSZMmdevWbc2aNZaWlr///jsZwVMFicxohP379wNAaGjohg0b/vjjD8UvbGhoePny5c6dO52dnSdOnFhRUTFnzhypVKoxT2mG1pLTui5Y36amprhrt7CwaGpqUvxynHVjZmaWmZmJ+/X169drzltaQeROPUuXLgWApUuXLlq0CAD++c9/KnW5TCbz8/MDgOHDh1+5coXFYjGZzNTUVA15SyuI3ClGLBbjqpF37twxNjYGgJycHGWNVFdX9+rVCwBWrVqFN3bY2tpWVlZqwmFaQcbuFHPmzJnq6uphw4bl5OTw+fwxY8YMGjRIWSNmZmYnT57U1dWNiYkZPXr06NGji4qK8HZYTfhMI7T9fetqjB07FgDi4uKGDx8OAIcPH1bZ1Pr16wHA0tIyMzMTl//dtm0bha7SECJ3KsnNzWUwGBwO5+7duwBgYmIiEolUtiaVSidOnAgA48aNO3/+PIPB0NXVvXv3LoUO0w0ymKGSAwcOIISCgoJOnDgBAPPmzVMkZ+Z9MJnMo0ePWllZ3bhx49GjR99++61EIgkKCqpuWde3PXJyclpt0xRtf9+6DhKJBBeEuXXrFp6tPnr0SH2z169fx8GZX3/9dcSIEQAQEBCg+OVOTk6ttukJ6d0pIzk5uayszMXFpaCgoLq62sPD4+OPP1bf7Pjx41euXCmTyUJCQnbv3m1iYpKYmPjvf/9bfcs0hPY1IqkDr6QuWrRI3qDKclRU1L17965fv7569er9+/fPmjVr2bJlI0aMwIcM19fXl5aWlpeXV1RUlJSUVFRU4EZlZeWcOXNkMtnixYuxndraWqpc+lDR9uOli1BQUMBisQwMDDIzM/Fsta6ujkL7hYWFODizdetWLF9zc/N+/fq1XXn4m2++cXJyKv0vgwYNotClDxHSu7fC06dPjY2NbWxsACA7O9vCwgIfAN8GBw4ckEqlQUFBPB4PIRQUFITXmKjC1tb20KFDc+fOtba2DgkJOXr0KAC8ePECANhstqWlpbW1tYWFRc+ePXv27GlpaWllZdWjRw8HB4e0tDQ8owAAUqyG9O6tsHbtWqxahNDy5csvXrzY9vulUqm9vT0ApKam4iyXjIwMTTj25s0bhNCRI0cAwNnZ+dmzZ3w+v+1LPvnkk1bb9IT07u3T1NR09uzZt9IShUKhRCLB7SdPnhQUFPTt25fL5dbV1bm4uOA1JsrBAR9c6z08PPyjjz5q95KWGcjKZiN3PYjcW2fz5s14wJCTk+Pi4vLFF1+0/X57e3tzc3MPD4+SkpKCggLNOfb06dM7d+5wudxZs2Zp7i5dFSL31lmxYgUu3L5ixQo9Pb0ZM2awWKyWb+BwOPKNpLW1tSkpKQUFBRs3bly1apWLi4vmHNu3bx9CaN68eVwuV3N36aoQubcPl8tNTExs+z0XL1708/P74Ycf7O3tg4ODNeSJWCzGz5yFCxdq6BZdG9pP1VvDxsam239PUbWzs1Ok2Mtnn322detWhNCCBQvS0tI05BiPx6uurpZH3AlKo+25cpcCb+zo3r17bm6uJuyPHDkSAA4dOqQJ43SAbM2mEplMNmPGjOTkZAcHB1zQlELjT548cXV1NTExKSkpUSfzjM6QwQyVMJnM48ePDx8+PC8vLyAgoKGhgULjOP74xRdfEK2rDOndqae0tHTkyJH5+fmBgYEnT56kZC1TLBbb2NjU1NQ8efLE2dlZfYP0hPTu1GNlZXXp0iVTU9PTp09TUusdAE6cOFFTU+Pp6Um0rg5E7hrB0dExKSlJT09v48aNlCTr4pGMPLeRoBpkMKNB4uPjFyxYoKure+HCBVz7DiFUW1srEonq6+sFAgGfz6+vr6+vr6+pqamvrxeJRG+9+Omnny5btuyPP/4YPHiwqalpcXExGbirhXYDQ12elStXAgCXy338+DFC6MmTJ0r9db788kuE0JIlSwAAH5FAUAfSu2sWhNAXX3xx9OhRGxubjIwMqVQ6ZMgQQ0NDQ0NDY2NjLpdraGhoZGRkamqKXzQxMeFwOIaGhhwOx8TExNbW1tTUdNCgQXw+Pzs728nJSdsf6MOGyF3jNDU1TZ48OS0tzdXVddOmTQgh+eilvr6ez+cLBAI8kqmtrcUv1tXVCYXC+vp6oVAIAFwu18bG5unTp9r+KB88RO4dQVVV1dChQ0UikVJFBDBcLrepqUkmkz148ICSza90hsi9g/Dy8kpLSxswYADecfe+0Qt+0dTU1MjIyNDQEKc9fvPNN3v27Bk0aFBWVhY5iE8diNw7guLi4l69erFYrOLiYrzlVCkaGhqGDRv25MmTpUuX7tq1SxMe0gQSd+8I4uPjpVJpQECACloHAAMDg4SEBD09vd27d1+8eJFy9+gDkbvGQQglJCQAwIIFC1Q2MmTIEFzlPSQkpLy8nDrvaIbWQqC04cqVKwDQp08fqVSqjh2pVOrl5QUAfn5+FLlGO0jvrnFw1x4SEqJmrhiTyUxISOjWrVtycjKu3ERQFjJV1RRlZWXm5uY6Ojp1dXX79u2bO3eura2t+mYTExNnzpxpZGSUlZWlSCUCQktI764pFixYUFRUBAAmJiaXL1/W19enxGxAQEBwcLBIJDp79iwlBmkFkfuHx+7du5OSkhwdHeWvJCcna9GfDwgymNEUn332mb29PS6dd+LEiaysLAsLCwrtOzs7Z2dnv9smtAEpvKFBZs6ciQtNaq42AUEpiNw1SN++fXv37g0AmkhSb25uxudRAkBNTQ3l9rskRO6aQkdHh8FgvNum0L58yE529CkIkbumaDl9vHbtmhY9IcghkZkPlZZFhjVUcLjrQSIzBBpBencCjSByJ9AIIncCjSByJ9AIIncCjSByJ9AIIncCjSByJ9AIIncCjSByJ9AIIncCjSByJ9AIIncCjSByJ9AIIncCjSByJ9AIIncCjSByJ9AIIncCjSByJ9AIIncCjSByJ9AIIncCjSByJ9AIIncCjSByJ9AIIncCjSByJ9AIIncCjSByJ9AIIncCjSByJ9AIIncCjSByJ9AIIncCjSByJ9AIIncCjSByJ9AIIncCjSByJ9AIIncCjSByJ9AIIncCjSByJ9CI/wcfMNzSUxUvOgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=250x250>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AllChem.EmbedMolecule(m3d, randomSeed=10, useExpTorsionAnglePrefs=False, useBasicKnowledge=False)\n",
    "Draw.MolToImage(m3d, size=(250,250))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 距离几何+ETKDG生成3D构象\n",
    "生成3D构象：`AllChem.EmbedMolecule()`\n",
    "先用距离几何初始化3D坐标，再使用ETKDG算法优化\n",
    "参数同上，默认`useExpTorsionAnglePrefs`和`useBasicKnowledge`为True\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=250x250>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AllChem.EmbedMolecule(m3d, randomSeed=10)\n",
    "Draw.MolToImage(m3d, size=(250,250))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 距离几何+ETKDG生成多构象\n",
    "\n",
    "生成多个构象：`AllChem.EmbedMultipleConfs(mol, numConfs, maxAttempts, randomSeed, clearConfs, pruneRmsThresh, useExpTorsionAnglePrefs, useBasicKnowledge, …)`\n",
    "\n",
    "其中numConfs控制了生成构象的个数。\n",
    "- `mol`：mol对象\n",
    "- `numConfs`：生成的构象数量\n",
    "- `maxAttempts`：尝试生成构象的最多次数\n",
    "- `randomSeed`：随机种子\n",
    "- `clearConfs`：清除已有构象\n",
    "- `pruneRmsThresh`：根据RMS进行合并\n",
    "- `ETKDG`相关参数同上\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    }
   ],
   "source": [
    "cids = AllChem.EmbedMultipleConfs(m3d, numConfs=10)\n",
    "print(len(cids))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GDRtGLmfy8/Pr9RH/u7DZsH27SWlp5vHjCZhDUX/BNIpE1tLSkpmZCQAvX76sIfe9F1Ftbe3Zs2cpFIrYR4T6+vrOmTOnubnZ29tbmE9UXC7w+aCoCIGBMGuW/MKFw8UbF6G3YRpF4khISNi/f/8vv/wiXvejR4+2tbUtWLBg9OjRYscQExNjYGDw6NEjYRY1nT4NAQEgLw8lJXDvHlAoYg+LUE/4pB6JY8aMGdu3b798+bIYfRsbG8nEFxoaKkkMBgYGp0+fXrBgwbZt2/h8vqqqKofD4XA4TU1NysoLystdWlqAxQIOB9hs+Oc/QU0Nnj2TZECEeodpFIlMVVXV2toaAExNTTU0NBITE6dNmyZ896ioKBaL5enpOX78eEnCuHXrlp2dnbu7e0ZGxpYtW7pfcnMzfvLEpfsrs2eDjw8cOACSrX5CqBf4pB6Jr6OjY9asWSyWwqFD96dPF+oLopaWFgsLi6ampqSkpClTpog9dGNjo5WVVWtr67Bhw+h0upOT09ixY1VUVFRVVbW0tAwMpgkEEzU1QU0NVFRATQ0SE2HhQqivB39/SEkRe1iEeoHVKBKfvLz8nDkBBw58xuO9M4dyOG0FBfl5eXm5ubm5ublpaWlsNtvZ2dnU1DQ6OtrPz0+8oQ8fPtzc3DxmzJjs7GwTE5OkpKS+n7ybmoKmJlhaQmQkLFwIp0+Dvr54IyPUE1ajSCICAZSXw7FjMH06zJ8PwcGwdCnk5UFeHuTmQl4eGBuX/ve/lt270Gg0fX39tra25ubmhIQEkb4QINXU1FhbW7PZbHNz87KysrNnz65bt07IvvPnw+3bsHQpXLsm6rAI9Q7TKOoH27YBnw8RERASAmfOAJ//16WRI9solPH29vZ2dnb29vY2Njbe3t7p6ekTJkxISUmxtLTMzs4WdSv7TZs2nTx5cty4cRkZGcOHD8/LyxP+XKbXr2HUKGCx4Pp1WLxYpGER6h2mUdQPtm2DjRvhhx+guRlKSkBJCezswN4e7OxgxIieRy/n5eU5OTlxuVxra+uXL19u2rTp66+/Fn6ssrIyGxsbPp9vYGBQU1Nz5cqV5cuXixRtdDR8+SXo6UFuLhgYvLNZSUmJubk5lUotKioiH6kh1DsCIYkFBBAEQURGEuvWCdV+7969ADBkyBAajUalUhMTE4UfizwCj3zKP3r06M7OTlGjFQiI6dMJAGLVKkEfzQIDA1tbWwmC8Pf3F3UINKjg9Hskqdu3wcoKAGDzZli1SqguoaGh48ePr6qqcnBwEAgEa9eubW1tFaYjnU6/dOkSeRwTAERERFCpIv8dplDg7FlwcmpNT19x8+bNPlpGR0d/88031dXVog6BBhdZ53H0YWOxCGNjAoB48kS0jllZWTQajUKhDB8+HAA2b94sTK/FixcDwIQJE8j/CwR9lZN9++abbwBAX1+/rq6u1waBgYFVVVXNzc1+fn5ij4IGA6xGkUQOH4bqanBxgalTRes4ZsyYHTt2EATBZrNpNNrJkyefPHnSd5fs7OybN28qKioWFhYCwMGDBykSLOrcsGGDm5sbg8EICAh4VxsNDQ1NTU05OTmxR0GDgqzzOPqAVVYSqqoEAJGUJE53Pp/v5OQEb6pLS0tLFovVR/t58+YBgIuLCwDMnDlTzKC7KS4uJicJ3Lp16+2rPB5v7969wcHBTU1Nko+FPmKYRpH4vL0JAGLJEvHvQH60p1KpVlZWABAaGtpH4+Li4pUrV6qqqgLA8+fPxR+1G/JMJyMjoxMnThQXF/e4qqmpCQCYRlHfcMITElN2dvZXX91JT9+anq4iyXSgPXv27N6928TEZOrUqSdPntTV1QUALpdbV1dXXV1dV1dXV1dXVVXFYDBqampSUlJKS0vnzp17586dfnkXAoHAycmJTqdzOBwAGDZs2Ny5c+fNm+fm5qagoEBud8Jms3s90QShP8k6j6MP1axZswAgMHCHhPfh8XgODg4AYGNj88knn9ja2mppafXxN1ZFRWXFihX98hZIkyZNAgB3d3ey9iTp6Oh8/vnn5LeiPB6vH4dDHx+sRpE47t+/7+npqaWlVVRURNaPkrhx44aPjw+Tyex6hVwwamRkZGRkpK+vb2xsbGhoqK+v397e/uWXX3Z0dDx58kSSnU26mzBhQmpqakpKSkBAQElJydChQxsbG1++fEle1dTUzM/Px5PvUB9waxIkss7OzuDgYAAICwuTPIcCwMOHD5lMpoeHR1BQkIGBgaGhYR+3LSkpCQ8P9/HxyczMVOqPbe86OjoAoLOzMzc3t6mpqbKyEgB0dHSsrKy4XG52dvbnn3/+6NEj4debokFH1uUwGohqa2tbWloIgnj58uXbV0+dOgUAlpaW7e3tko/V2NioqqpKoVAKCgqEad/e3m5nZwcAu3btknx0giBGjRoFADk5OR0dHUlJScHBwSNHjgQAW1vb2tpaExMTAAgODu6XsdBHCdMo6sXFixefPXtGEMSGDRvevqSlpWVoaHjmzJmuFyMiIsiUKkZqi4yMBAAPDw/huzx79oxKpdJotBcvXgjfq7S0lIw5Li7ujz/+6Hrd1tYWAPLz87s3zs/P//XXX8mxyGUCcXFxwo+FBhWcfo96FxcXd/LkyRcvXnS9wuVyfX19vby8mpuba2trhw4d2nWpoaGhpaWFxWLV1dWJNEpnZ+d3330HAJs2bRK+l6ur6xdffMHj8datW9fZ2Slkr46ODgaD0dzczGAwOBwOj8cjX+fz+QDQ4zO7ra0tuYOfq6trREQEQRBr1qwhp/0j1AOmUdS72bNnr1q1quv04/Ly8mnTpp05c0ZJSYnML/n5+d3bx8bGxsTE1NbWijTKnTt3SkpKrK2tZ8+eLVLHQ4cO2dvbL1++nFzIJBAIhOmVnp5+6dKlZ8+epaena2trz5w589ChQ+RUJ4Ue+1B1ExAQsHjxYhaLtXTpUrIxQt1hGkW9U1NT09LSotFoAPDrr7+OHz8+OTnZzMzs6dOnixYtgjdpNCMjY8OGDQDg7++/ZcsWUZ9okwvbN23aJOoOIxoaGtnZ2S9fviQ7ClnMTp48eePGje7u7uRE0fj4+JCQEHLnkStXrkyePLmgoODtXhQK5fz58yNHjszOzv7iiy9EihMNCrL+VgENRB0dHUwm88CBA5s2bTp48CA5fXLOnDnp6enjxo2zt7cHgMmTJ8fExJDPykNCQjo6OgiCePr0qfCjMBgMLS0tdXX15uZm8eIkd4BOT09/+zvct7W0tGRnZxMEQafTySn9sbGxq1atMjAwoFKppqam0OcyqpycHHIB1dmzZ8WLFn2sMI2i3nG5XPLRCgDIycnt27dPIBAwmUwKhaKoqAgA5P8BwMvLi8PhiHRzFotF7jGalpZ27do1sYP08vK6d+/evXv3hEmj7/L69Ws9PT3yvZiYmJC/D3r1448/AoCSklJaWprYw6GPD36oR73w8/Pbu3evsbGxkpKStrb2vXv3wsLCKBSKhobG6tWrv/rqK2VlZR6Pp6SkdP78+QsXLvR9nNzbWltbnz59CgBlZWVdKUwM6urqnp6enp6eYt8BAExNTa9evUpW3JWVlY8ePXpXyxUrVvj5+bW3ty9atKihoUGSQdFHRdZ5HA04bDZbXl6eTCsGBgavXr3q0eDu3bsAMHz48IyMDPGGqK6unjFjxr59+1asWJGQkCB2qF1TTYWcc9qHXbt2kf8i5s+f30ez9vZ2Z2dnAJgzZ44ku52ijwmmUdRTUlISAJBfen777bc9rnZ0dJDfjR4/flzsIaqrq/fu3UsQRFxcnCRptB91dnaSMxCoVGpNTU0fLUtKSrS1tY2NjXV1db28vH766aempiY6nc5kMgmCwM/7gxB+qEc9paWlAUB7e7uNjc369et7XI2JicnNzbW0tPzyyy/FHoJGo5GPdHR1dbtvCCJDVCo1Li5OWVlZIBCsevdZKImJiefOnZs4cWJ1dXVDQ8PFixeXLl1qYGBw4sQJchVpTEyMFKNGAwKmUdTT8+fPyR8OHTrUYzZlW1sbeRpdRERE1yMmMejo6JAn07m5uZE7Nw8EOjo65F4B8fHxN27c6LXNf//73/Dw8KamJgAIDQ2Niopyd3eXk5MzNTWNjo4ODw/Pzc2VatBoAMA0inqKj48HgHHjxs2fP7/HpSNHjlRUVDg7Oy9btkwWob13QUFB5OOyNWvW9DqHlFzyRDIyMtqyZcujR4/q6+vV1NT8/PzCwsLIbzzQoIJpFP2PgoKCxsZGeDMxvjsGg3HkyBEAOHDggCSHIA1kysrKK1euBAA2m93rmiUyjRIEAd0WPqmqqioqKpIP5XCD50EI0yj6H5s3bwYAXV1dcjPj7vbs2dPS0jJ37twZM2bIIjQp+de//gUA8vLyOTk5b69ZIlfi90ijAODj40MunD18+LD0YkUDA6ZR9JfMzEzyE72Hh0ePS8XFxd9//72cnNyBAwdkEZr0TJkyxcbGpqOjQ0lJ6fLlywsWLLhz5w6bzSavdq9GyZWyCGEaRX8JDAwkE8T06dN7XAoKCuLxeGvXriV35/y4eXl5GRsbz5o1S09P7+eff/7HP/6hp6dH7mOybNmyoqIicnerPnYzQYMKHiKC/nT37t158+ZRqVSBQJCTk9M9Xf7++++urq5KSkqFhYXkRKWPG4fDUVRUvHTp0po1axwcHBQVFVNTU7s2kbKxsSEIgk6nX79+ffHixbINFQ0EWI0iAICOjo6QkBAAIAhCRUWF3Mn49u3bn376aVtb2/bt2wmC2L59+2DIoQCgoqIiJyfX1tYGAC4uLsnJyXV1dT/99JOXl5e2tnZhYSGdTjcyMkpPT5d1pGhAwDSKoLW1dcGCBVwul1ze7ujoCABBQUH//Oc/ExMTly9fnpmZqaent337dllHKlXt7e3wZjWXrq7ukiVLLly4UFdX9/Tp05UrV9bU1ERFRWVkZMg6TDQAyG4BFRoQ8vPzyaONAMDV1bWpqSk5Odnd3R0A5OXlyZRKpVKnT58u60ilLTExcevWrTdu3Oj1KrmIy9zcvL6+XsqBoYEGq9FB7fbt2xMnTszLyxs2bBgAvHr1qqioaNGiRfHx8QYGBubm5n/88YeKiopAICgtLZV1sFL122+/VVZWHjt2rLi4uNf3fuLECVdX17KysjVr1hD4gGFwwzQ66OTm5pJ54Zdfftm6dSuTySQ/tqurq9fV1cnLy3O5XHt7+/b29levXo0YMSIhIeHUqVNpaWlFRUWinhHyQSsrK8vMzKyqqur1qoKCwtWrV/X09O7evUueyocGLUyjg05eXl5ZWRkA/Prrr1evXo2Kirpy5Yq6ujp5qnBzc/PSpUvz8/NbWlrmz5+fkpJiZGTU2tqqra2dkpJSVFQk6/Clp7GxsaKigslkvquBmZlZbGwslUrdsWMHOd8WDU6YRgejixcvhoeHp6SkODg4bNmyhXyR/IZ0w4YNp06dolKpBw8evHXrFrn9UkJCQnh4+M8//yzLoKXO0dFx7ty55G+Xd/H09AwNDe3s7Fy1atW76lb00ZP/+yboo+Pl5eXm5lZfX9/9RTMzMwDIz883MjK6du3aJ5980nVpxowZgYGB5BEag4S9vb2VlRUALFmyxMjIqI+We/fuTU1Nffjw4eLFi588eYJz8gchrEYHna4tPi0tLbufx5mRkaGnpzdx4sSMjIzuOVReXl5DQwPebMAh/YBlQktLi8yeFhYW5Jynd6FSqRcvXjQxMXn+/PnOnTt7bcPj8cgvUhgMRnNz8/sIGMkQrmJCAADJycmTJk0aPOuU+l1ycrKbmxufz79x48bChQt7XK2urj537lxYWFhcXJy2tvbba23RBw2rUQQEQZDrlAIDAzGHimfixIkREREEQaxbt+7Vq1fki0wmMyEhISIiIjMz8/nz5ydOnLh3755s40TvA1ajCK5evbpixQpDQ8OXL1+qq6vLOpwPFUEQS5YsiYuLMzMzmzRpUlZWVmFhIbkSf+vWrWpqaoGBgbdv3zY2NsVRD1AAAAOTSURBVMZq9CODj5gGOx6PFxYWBgD79+/HHCoJCoVy/vz5hISE5ubmq1evAgCNRnNycrKwsBg1alRVVZW6unrfX7OiDxSm0cEuKirq1atXdnZ25HbFSBJkDiW3wV+1apWrq+uGDRuio6OdnZ3J+WQLFiyQdYyo/2EaHdTq6+sjIiIA4Pjx4/Ly+JdBIi0tLZs2bQKAqKgoW1tbDQ2N/fv3NzY2pqamTp48mcyt3adGoI8G/ssZ1Hbv3s1kMj08PGbNmiXrWD54QUFBFRUVLi4uX375pZycXHZ2tqenp6+vb3R0tKxDQ+8X/m4cvAoLC8+cOSMnJ4dLwiX3/Pnz77//nkajnTt3rutsO319fQDQ19cnTxtFHyusRj82hYWFHA7H0dHx/v37kydPJmfO9yowMJDP5/v6+g6Gc0HeKy6Xu27dOoFAEBoa2nXAsrW1tbW1NQAsWrRIptGh9w6r0Y9NZWVlcXExAKSnp9fX15MnWb4tMTHxzp07ampqu3btkm6AH6HHjx/T6XQ7O7vQ0FBZx4JkAKvRj1BcXFx+fv6TJ08oFEpYWJixsbG9vf2wN+zs7IYPH05uZR8SEmJsbCzreD9UBEE8ePDAw8PD3t7+8uXLVlZWg2exLOoO0+hHaNGiReQHyaamJnl5+erq6urq6u4NFBQUOjo6DA0Nt27dKqMYPwYCgeD+/fseHh5lZWUEQYwfP17WESHZwDT6sel6oGFjY+Pu7n7gwIHy8vLiN3Jzc/Py8kpLSykUCovFYrPZKioqsg75A5aamhoeHl5WVjZjxgxZx4JkBheDDkY///wzOQ989uzZDx48kHU4H6rOzs6tW7d+/fXXSUlJlZWVy5cvl3VESDbwEdNgNGvWLBqNRqFQ1q5dy+fzAaDH3qNIGBQKxcLCAgA0NDTIQ1XR4IRpdDBSUVGZMGECQRCPHz8mH+vj83oxUKnUgIAAABg7dix5lioanPC70UFq9erVHh4ePB7vzp07eXl5lZWVso4IoQ8VptFBqrKyMuzqVfnCwiRjY90HD3BvJ4TEhml0kNpw6pQ8gwEAU6urQSDoY7ETQqhv+KR+kKozMDBgMP78wx9/1A0ZYmBgINOIEPpQ4SOmQeq5qytQqQDwwtwcRo7EHIqQ2LAaHaRqa2v1aDQ5gEoWy8TMTNbhIPQBwzSKEEISwQ/1CCEkEUyjCCEkEUyjCCEkEUyjCCEkEUyjCCEkkf8Hd49jRbJzcC0AABk6elRYdHJka2l0UEtMIHJka2l0IDIwMjIuMDkuNQAAeJzVl3dM1k2z9y96U0SQoggiKNKkSFGB6zejKGAXO2LBhtgLFrjFLoKgotIRQRREugIKKNdvRqUpFrCgiN4WFLmxYu8efZ7kzcmbPCfn37PJ5LPJ7s7uZL/JzLwW8/+W/B6dJf8e8r/N5be5/rYtcsom/r+pqKDsb/GbCorK/oH/otx/m/ynFRWTf00U/tuW/8T/xdH//b3/780m/96h+m/K/0++/qP3/8+HnOQPJXL/V6ghkZPIyQfKyyv4KygGyisq+Ssp/4aKiYqqiaqav5p6oLy6hr9Gp0D5Tp39NTRNNLuYdNEKlNfq6t9VO1BeW8dfp9tv6ErU9Uz09C3k9QxMDLoHynfv4d/DMFDesKd/T6NAeSNjE+NeFvLKcv6dVQPljdVMunXxNzLwl5hIJL0lElOJvJlEoY9Eqa9E2Vyi0k/SyULS2VKiaSXRspZ0tZHo9Jd0s5Xo2kl07SW6DpLuAyQ9HCWGTpKezpJuSr9DUJZTVlL8/XmdOquqqWsoa3XV1unWRVnPwMhYTV25ew/DnkYG+vu6SyTqf8L9E7PL3OhzOMg2mL+tnwg2yRmoedqGheWjYa5lJm560UYtmq/hlHskGo93pdcuz2GZwkxMrX4jrlrlBUXqMzChKpcOGkSL2gN3YQL15NF1W0VJuyW+DHaH3a8mwc/24xA0yQbe9ioSZP004bVvCxxeY+F+1H0k9fC9DcdVKsWWJycp7FGLoDnosbg63oz+fmtLRl2aZfYDbGDwfC2a1m+PMPNGf94bv0BwDVpIMWt8+dz2LWTnZUhdP0xgp7R6MqQeEP0llIu7OfPt8umQtCyBL7b58eGBd2SbapJ5Vz9HbpmTS+PPhPF3u6uU1F5LhSsK+E7fBB64S14YMK+GYjcPxHeTXSnA15597jujfPdIWuSzknp3hOH1chta+vo6eX/KwQzVLfTVKpLs9G5gQs0ayqxfKzyf3IgrDXRoccpaOBF8Ep8vChV9FGVCW9YuvBKdKF6MjgWbA554VGOR9IWFPuZ9G4dyLZcEybxmVBywjE8rXIQpK6/iwj3bOGLHUHHMkELsVZzIcCQeqjLPoHhewtUxNtj9ZCT+PdxUJnW3Rl45AK+7xJFTwD4y3BqBoSe8uavOHrr32Asf/lULqhaH4dKMXfTTv4kqK/qLrXmp8Cy2nPoc1gKDteP5nmQePMmU0bRO/dnHP47iq06AZOM6nv1oAp/e0gbHsgrY3WkCn1qmzO25Efwgfx+tUjPllaMb+HWXA6wY5ysoNx7n4z/zeEQz0AWPIzwh8hj7OoeBw1/WbDbqODZaHKPWDzdI3v81OkQdpFdtdvCo/QOWCuokCfoFy8xLcLnTNKncHzm6HD53iuXKo/HzxSwhbvQhfhs8Bp+tLhCWpGTzSFbHdstYcPt1gG/d3QAnLsTBt2Wz2OqTFxyIJKFo3XA+WvIMHA0ei6qRQYxnEXupPxOl6524Y44caeY2CPdbTxMoGpBKSpyw/40bDXpzj8LGm8nSHvSH7hpPSXv9KFmyVhEYm1rRJ9VY4ZG8LUyW/yicr+0F5zY70sOTyrDlhwm0hQ7AUVetqMW/Vkivn407RhpAuv5ZQetcDC5/PAR+bIyFLtdy0WHfe3DfGQN4+DBeL5Li5MAc4cq9ELxX4oi9dr4Qe33zxkeTG6CsoE08XlWOd0eHo795gjD5WT4o6Q7lY7Mei5LL5ugpN4J/6XWIC0f0hM7m4ZxcM5FAMwMGRefxk7eJ9KGqK2SG1vPc5TVkFOtDbw5e44z0GtoXU0YfBuewxr5E0ls4hCwfhfFZs4n01qqUetu788z2+2JWuzX3PyTla0PbRJt7dRx2fj9mlytRxvMq3uMfh7tmZUH6gVT+1Z6DrVfmC7VLq7ijQQc3TXsCVskp/DjQQLhd0Ab58zXZbMxtmPo6iiZpzWYPyyB0roqizUa+nPA0nxpTOsPzHwfBZHABdH+7GAaUx9I97wpoVVoBN1onYenMKpp+TAEGGabg1IQLMivpM+jTUoMpcyUYMf4pLFwbiIvVFuG1/D30vVUbW5UuwvDSPaS2/TLuuheF9tWHoBtcw3WrIrCTpTYtkGbgbLkMrO66RlAYoo+eD/JZmphE99ZEQsbel3xa7gWFTM+hppgX3LPzS1rRtzd31c3m3SaJJPevxO5B53C09Q40bE8T9UyOYYm1G07WfCmTTw9Dq9fmeGxRKM28MxW/p8igfsJoWilbgDn2FnC+KFpwiYjB+tT18I1i4VFAHoa7KWHNoTXwKMseIwZ8F6WBEpgmdwICpPJ0y2GDMFLFGjybmsj7tppwN3kwhbtcp19Xesj+Kj5JjiMeiQbBdbKwDHMaozYQCrNmue8Z5gJxOYMgslIq2Og6cNykw6LmuBLRKGgOhwyeA7dDSJaRtZ/Xdj4IZ2+G0ArO5hVDdbHC0YmCD6bz4wpPdGusF+zUt/LuOAfsyDkKaZvGs3VDAxiP3waDLCpYc3IkpnvaQ9WbUlLMd+V3ZnpilYMN730pZc3iRHG44QiSMw7nlPxSWfOSC+TwPo/jXMaKx/rOo4TaGzx1QTfxp88LwUi8xYOtk91bbRPAw/4Em12RFwwuasPHsAhe15osVQnJhjoD5MT+HsJLlT5Y4+vFT2/lCd8uPcBXD1ZgSvextPNpHWZKDmCNeR9QaDiJL8IO4pGsEFpYsAn3Fo/DbVovie3NMWhuJizXr6bw5Un4MCNP2BIkwbGrqlHdXg/PK14HA9/xOO1OJi0/NA9cSqLo69dmuHt+rGzttCMw6ukNUFNOEqa6T+SJT6NoaMFwCv+QwF4l+hBz6SWNDbjAG6v6oe7LfMKba9jQYBr2b1VDlQBj5s/FMODQAxg//zYfT4zExjPHzyyQq+YFPlEYIPcQ1nhm84Y3WRia+k1wUjHn1Ul5XHYpWwzQr6DxG99wTNYy8d3MLXB44zs++CFKunuOFpYvOclfRu0Q5BT+qHHvzjo+rBSIutu2C78gn31XGWOfEBPhXnsUn7hijCsfulI/K39uNC2GuXvG0vrgRbz2bqLwYnPvitun4xn6HxJsNsyFDzFFrLatEL69DYZua+x49vBJxH/5yCyG51JCeH9SfRUkZmUok5B/n3b21aetm31hUmUlTblgR9YvKkDwyhNnUKs4Z6Ip5LcMBLlplTJ3RVea8EMDxl664tYz1hmtSx1lrXe7k23uDLxYvx+C2+9V7Py1B9+NfAbPPQdR1uEjaDzLFd2bxou9IRFLDvvi++IwWDdnFeatssO2pQ+gP3lhvPc1qMg9CKnB+bit8gDWx16HEwMbYWSxFa/sH05jddzw+dC+3HQmlkYq7IfHPlt4g3cuxX35CX0j07nE9g61jyyD5e21PDbuM+V7H5JWfbvK5Ude0Fy7EfTcvYAtrZgGB88RU2x2sjmkkSwvnI6GePDtA7NonK4219pM5ObwsbT1SBtPWzwBvx4cDQ9Lr/N1j20YcsiNlCdU8OZ9u3Gp/BKo94pgvfQpuOlTPmll27Jt+FlI7lxJt7UP8hjvzpRidQHmLrrMNx8fh3SdO6Dy1zhOyLhPr16w1Mh5A4DCfXimcV46ZMEJytlXCFNMDgtRV/3RUs6ITq3bT5lj4nGdSykssm+iIQf+JLgZqBMEtLBwNspiPHET26LtZH1MerMXgiOfQeaCMxj4PgsrZ0yH6IaLGGoTgboqWnhtYQLuvHEEs/vpYr9sD3yxI4UF6SPaNlcFrU1beMzPzlyfkS2e8HzFmQsU2DPhORnoFHF659Mkp/hHjSNDy/BDrxiuVt8sDtyShjMbJ7FZ/1jRwDQE90V4s95oFShYNRJXmn6mI+FfhN0lc3DNjTWk97BclNnFYlJEHNmrJpDYPRe9XHX5+9IDdNHPGaX3Y4WQm/Lk51gK0QMbhZ51V8V7S0ZBjlo9JNrPE4//MKLn25pBV2mcSM7F1BhuAJVFZ8ROwzzIY68ZjbjViR7qdIeu0TZUUtWFXC2d+On7nrC3+2Px46tADh2iSuX3I8TCFUm887ATjZZspq2VJRzTu4W6KE2iMPUcDpjRn8Pjv0nvm4dxzzZT3nrQD8Qlk7jPl7O0+IUN7DGtZuntYF7WcUrQdkyjB78QD12Oc4e3xlyhMwq9NSdJJw/6RyyrD0fBa67Qd/ROshxeiK77moUSw13iBa8baC41gjmP9sDg/g0427M7iLV3YEpLNrbH1QoH1AOg9mgYHjCaIDzuVgl+Iwbi8vgdFaPzHfCc+kCULYuucI+7i71/RXGwWS+Zk1Yp2lSn81Exjepy8rCpbxa779aCRKX5+LdGCE9vToE9R7uhRmErbf4ZA3NKUrHV5YrYVvWGxNpqNNUxYd8eT8h143S8n7QHxge60cpzBZQ3qZoU9Z3pq/EuWFTUQPYn3EihbhKr/6gA462WdMriMC8U90u72f5N8jUN3K/bK7rapYz8ykJ5aPoklpRcBtt3hlx48RT52WSDzbYSvquQxLn9rECltJllDxaywu+K4MXDJl7suIN7TFQn3Ycq/M/yk7iiT4uwbvVAelv4Fi/GeMGtb82QeeklBv4QoNnPAb+szMJMKQtyf9oelxm1ZzFpxT/UtdMytGlLRw2HWDJPt8Cd0dvRbOIrutf6AWqrpmCrQyq51y6BWWvm4sKpp6XaISYgOx2NvzzGgM2pXBg/PAtvxA0QViR3+93l2OOiXcNgHleLCzudBNHsmdB54lcxaFQv6PlxMGyeepXuzPKkRds+CLUZDRQIReTmPFA24qMKJeYbUohjqjhzjTa0znaFL7OzZTYDesMoGzs+mHdZtEiTo5lDZrDxJ226kIvwPX4Ux0xbK4TWtsH7dev50aV299q5Q9BjWhJjp430stkLf5gf4R5J/5DbGXksuhjFibaXaPDiOXDmn0K+0z2R5uXvxpr11bTfdySkrHXhfLsB3PWEHszfP5gv+c2mXRl5MK52C++adJ267L4GtpMzOOtuKFX9zmDpZrW87eNZIVytO85cXvO7sYiGRboqqLblKLvsfSVItC/Bqu2bubZPOug/PgovzB3ZLKkXhoxlsAx15KalzWh4ScLmOT6o256LL1b151TH7Vh+VoZpDhE02Gg/7orchIuUXdkkVB3fN1pgbN9/aHaXC4JDUwx+ffocTK13g85swgXtA8HCZTCOXzoBR786A3WZq8moVyTFI5L+sDJwSM2GvD4R4tNR56B89ETWfqlHayYU0LMGY6YB4bBw41PYuGAxX4m1gXXf12N7QTk3KVlw5T1VfD9hP5u878SmdjrlbonXeGWjLZ1zjcRPf6fwZU1v6rs0C5fnl3JlYlf+y3k/drPuzzV3SmFiewZ/irxMo+fq47VbLWz+Zhps8HPE6ytbeESkBk4+3RV/eaeznPIfNUa7ynjBpO18b4QUbAYcYTNtV64pk4NZRwoZCr9RwKo68fzHeKYdU6lPeW9yWzKH60aa0juLS+6F7Z685HflPn75YmgcEczWljbc51AMJNv+/s2kbrA2Z74YMa2A3r7ShXzJFjG+kwVZJj6EEUbvZaMzh4JG9d+wdESoOF33NOy8rwYqlyW0KtwcBhzsSWnnehH2dKS2KiOavl6OnvRxwvKvHcJXHzcK1vXH0X7DSWVXD+qiNRLdO92goJ9VQvclq/Fj/SD2HaMBqrpJ2OA9hkfpFohRtpnYvECPf9rnk+eYffhTaz8F+FSQZ/kJtP5rH/cUt4n+ORfA8aAUb9jly2rSHHChujuqNHmJ6w2mwas3O/HJ6SmCoHANms1ycVZSifCpTwg8+VmPOj69QdX/mnjhfAN23ToErEwTKT4tH7WdjWD2eHm6eiwcb63IETYEHifr60NwjJOLUFRtxs88h6Oxc7KQWtvAbzSj+Kpxi8z21lW+P3oje1rdgdbpx/j8ylRW0VwGHrOvcOx4RT5wZDWl5qWz18ZLstRNefR1kQb3zCyn7SNFKKyYx25P/fjW5FegWDeVO5aegnluDaJ7XBzUOVeS8NiXjp7PoNPvKqlPRm8K2+yHP8rTwMt5O00cZ4CnT5WS1HEfnA2ag+dOzOcW5eNQ2CHigvcWfPUfed7rmoRtfm1imZYWY8MV7Hd71+/KoEhoHHgIF0/I4rWb8oVXK8vw9seD7HAzna4etMbFO7MxLX+z8EilGlymvsSNxQKs7zaH2tJf44/qYNB/pMWr3xeg/OLflZnKHzXmDhHZeud2ti7aSCdsjnJuF3f2+dqLFkQXsM9gNV75uUXQ0Irnojk7KX7iONgoncv99Fyop/pTqVrjKO64RbTz7Gq67LaRo56a8xXVbNLb4MQvzV8KW1yUoHVjMe1OUYKvj9uEamMrajS8A80NHQJGCbAgqgn8yyTQ5VIJjDX5JQR+VfjdZ9vDrlIbUi/8Kbw+a0daZE8Gwa8Fx3hHHLqUhIDhZlCf7I9Tbk+h9El7hPKKGAyYmEbTWzfBF8tcXOavz96vLGFlZhpynDc7Nt0Rb2YE40wjOx795ADluozA2VGX6aj8OIr5WIqKo6O46qsPrWqtAO3FAs5r+C7sDbTFqYaI4zYogu9LbyhxiMC24jZBseMsTD9fgO/bnwjNN8eBZ/At3NTeINS806LpzrdwxNzzwoviNFqyuAArh1YLIWt1KPBBOGrPbxLKErNp70cpmnreFwIt+rJqyxC8seSyEJ9UwTfWpfCHNyAqnT3J8rr7OLLHZ6qc+YAvjVvJhrPCKKKwjk2UDXld31A4EpTGPlWPxJuSG/BNrhtLndIpUP8CTRKWcM2osaz9TI3Pz5vC72vS4bNFAPQNiIOf3rdIb58q9L90mILwMnU3/CDcT5mKjxpj4MLC7dC+KgVj3E3pgOcLuKxfgy9KrVi3IA0sKgLQxG0Kf5wh4VgrHWwZnEd9ksrILFqGMUt3s6LLRwqCO7hvURS3nCkV91zIwnlO2bz5qiIFlZlicnUBjjLoENQu5YK45h32+XJV+KoYRVmr3mHFmmLhZkc3joJ8vOl/RpBT/aNG41MiNyXtxHW9E2DAm6P8aroHnshEeDg9jzf4dUfryX+LF0fFcItPDqg6+9CFwLmc9X02pE/ylt3c68MB7udg+Zy9sNplG49xNkf4wtCs4cRHj4SLtiMUKHXxKRq88a7YaXGWWFzgSp9TrtI5t4fiw1wb2LftDlXuyBZNlYph8Wt1Ktl2W3Yh2gn2bjCDA60VblMbLalaOgici+pke80dsVJfgfZfZmmFRwBWNlvDuJAe7uk/YzHPfDpIE2aA35cCtJr0Azo9QgiZkIXKyu5o3XmqGD8qDI9sscP3juHU1WUKJlleBfPtQZT79QK6uO7AaqU40SkoF5YkCrxiSbvoN6QvVvzlxZ3CzomNEgMY6xTOKzId6NuPQxC2rIClY8bTLVtlkN9/g00X7KAFlr6kSQ08r3A/fS6vILcVOfzSYhuN9RpGuq5hPLd+FBUHlFN1mQuHjLImt1obdrIfyNPvDyPHaUXseCsD65IF2LDmAZ/stA1fjJNCl2HnePaXcOwRoY3zh9Zw/192eNpqJ511TOb7HhPg6sKHlDLCmA/IJcCTXg8h8O91/D3SEy3OGOKYrVP4pGooBSXvoJJfh2G1UAP9T/kKagYH6KvPHbg87X4FPJyKdUmFtCWrSEjtl4J+tzukwxMbwCXwIuq0K+LElnKYULoJtfRm47imVlpz3AwN/qqFb8du05f5Faj5+CB6hEgg7/tTvHNrFSpn9RaHlxOGesSi2tJ0Ki/TwRK/Qu70zpUGPQ6D59ZveYzpPlJPK6TQkNdsfiOLTAeY8exZx7nPwN0kp/ZHjdKNp7F2w3b2WvodzDqnYn22O9eNLYXVb5ajZZAZf4v8Ds7vhqDfqTO0Ma0cXjrPwbnXgeacqhbeuiZgY+N+2hFjIn73ycdbdzrzjuOlwtpUR6wdJgpOwggxfFMR7LS/IzjuPSC+3QUg13EVYjK+iLO3mJP+vGuQPcWaWoflk1/F30JKbR/aL7OliYreVNn7gaiYZQ12el6UsT1MfPrKlmu0LwheLxdRxGJ/9nsvUNdMBTreMYGX9TxN2YaW8GnWVlbqYsq5+7bDJ6V0Xi4VuLQnCG4bj/O7ezpsI7+F7Hbv4ycfkknOK588as+yZdsO3lytCN6+uaT8eRD6xE0n+cK+vNwZ0GfvNpJEGJNzexiGN/iR6v5MmtwrD7lsB21U7UO3u97AisIwmmfgClv3XEeP6Jn0zK0YZg3Lw+Z2Ozo/3RWafcMw57gtpfYth2/ZgzF62nexa601fs0UsL0jR4zeX4JF53ewYsBw3KXZhIkxW/jEz/vgY5KDe08f5iXJBFWyyZha78kFdY7oZKKMTfVxdKf4A4RLMnC9uS49tthCX9Uv44OfpqwpN1e2rnUqJq8+AL8e6dM7rxR619FMG548FLM1UqDO4i61BnyXHf48hvs9iIbAstOkNsOUR0yJooFZeZAevJ6lUR5cpPcVzAee477BFiyXf5vcJfF8v5891U/oyrXN59mnbwK7jqoRi99mcQ+VZB66ORHu/9XEM4aEcEDoTjiVqs9qG/Iwuyye8G48PevWgY9lCaSmng4rVDswcP4scthmjBpqebj8kwbp/Re1OrxUM+ncawAABX56VFh0TU9MIHJka2l0IDIwMjIuMDkuNQAAeJx9V0tyHTcM3OsUcwG9Iv7gMpZcUSplqSpxcofsc/9KA7Q51CZ6fvRo3AM2Gg1w/HTVzx+vv//z77V/5PXp6brG//yZc15/yxjj6dtVF9eXr7/+9n69fP/ly887Lx9/vX//8zK9LPEMPp+xv3z/+PbzDl0vlz+GZphcz/LIiGS6xiPZhx2PMoD2IJXkcT3zw2c6MYAx0yxuoCygkwy+numRNIPiwoWHK99ABVAfQnMSIiIQY28roIST3kADUB5qIqIF1BjsVlvHHGE30BdQkioZeojS9KhHOKbmgYzemzNmcGWjOml2cDaxcWye1/uFbE2xVW0pzhAA+giFHxEnIlJl41HyeQg5woADSB46ItcX3DYzxoaEHJyydBw0OA95qEpTsuScmUB6so7OBjXI2mVDqziVb05UA7FUbWSLqkopRzokK6rLHNQJe4pMqSsaPv1koE3Vg12pAOY6eF4lp/k8CRhUgoxBjtIUgJUX1zkUiR3QKhKMJjNCR7EeA1E7vg+HLgc2FpZBa2RhneG2ytCHQtgDmgs6jUO6pMlTsqBpKqXKhlapnhWlZEI2z+UYH/lDAnj5YMBjYVM8rcPGnJzNRSA3n1haWKqKtf+cEHbWFeRyO+hyV8xgaiKRZsMiXtoSh8bRTyyruCAW0JmrjwhlWh2TcRSX9fpojmZqXlAll6YyIjMPEdiWD6BRoCnBOg0pdhWwP52d74uAQ3BIa7A0e/oqHcp9EogFHWg3CQwWm66oV21kTnFGzTYXLD0wqBzzJMrm5bJQm4djeDZy2oSXsT1ym9n5s7qelZXVW9CRIAtmy8Df3sjZnX4jqdsVdnVJzAxY1KO7ECpMP4AMSRkhKUMAZJ0YAbUJPOlyAOV6QxqYhSlWpoqBhKtvLYbyGVIbidIzdemhFoZA2w/OOW2CSG/IWAPkqF0SlF4zEsMnPwX1RqbA/lYeGESZ5YFyHp0xA0hog+GI29h0IBV0GpBRU+lAJpBwUyZJj3Jyq+TripM+BZ0dFGM5fFT2JLDregjNrYdH0PRvpTgLI6dqDWccNITJS8zn+UAAtpmkApQDUffoyhOyOqcvLPxWIaSSLSgGetapVDNrftq+6lQzxYK0AFApVnqa6KszaBWq2mmgSeYSIOD4mvNJQw43q62oaSQx18yyMbxOM5wDfJhUq1Ld+gGyDU3FmVU7EWahHlXVqhXKzjHm6jicqoKwwJrQJwdoVesZLafDss2C1HEQVJMRax5e1bkoMGyHEEVmYAJ2p0TJdWRmY2FHjC4onhpgWx7E3PZ5Qmnp5ZgTaAcgDY3Y0mJsEx8HhvGqLeb9RO0C2g8c6w0lo/OotipYHVOoVwLpqZPbBMh0nCY0bROghTBMsD0Oiz6l0NQYlIcHvr6/fnoJWq9FXz7eX+/Xovrw/fKj+Mr9iqP1vV9k6mP36wp+ufx+KcF4u+J+8yD8a96vF4TvvN8iFF863xa0FqLjtUBroU2Oih0W2vyoCGKhTRFHOTVms8RRQr1solRMsdDmiqmgtdCmiwGstdBmTEUZC59nptbCmzO3nMDcgnLdgcKbM4457mVz5uJcdzZnLmmx8ObMxRkLb85cnLHw5szFGQtvzlycsch5ctDV58JxQnAtsjlL6Ry3KaRV1rt4UoTxCJ3zWzv7TViKcKdyzOOSR2535bqxhcBw7Rv7EW22iHWMyrKFbBW09HWEOsYegbxssqrtPD1HGLVztgSYRFQJ6k5ZY5lGN1ltstD87oZmi8h3QxTdcsXma91doLwJWxGGK3QzNvlxZ1M2XXfs7vjijPLb5mzFGeW3zdmKM8pvm7MVZ5TfNucaCGf71+8//0eF66f/AF7/i5TQQRknAAADbHpUWHRTTUlMRVMgcmRraXQgMjAyMi4wOS41AAB4nJVUu44UQQz8FcI7aXbUftusiEggAXJEgDY6CQSCC+/jKXsI2MlIZre7utx2ld2f33250e3h87svj3+//PbhzcfHm9wejj98owP9cOD3p3Gof+fzzzb9e2S+8r/0Vy8PF9rdrHSLXXNpbNhgMqor/qwo1c13K1ffLmsvc4rr2lnXska8oqoRC7ViQJ5BmgjnqcUD4TDiNbuUcjPsWGVDrK6VV9qrLAA4l9e29mUcqQqOeFHoprssChsOrgwGh3y5brLTYo8heeKKK4NEGUBYC1wEKWcXRDMzRq20R+BIc0BOlIS4kVWZwDxZqW+SZBTTQphF0MZ7etAIEcgv9HrhPcygDSAll21Ey6wOWIIjjNQpkVmzPPSI5xolqFeUUW+TForocA6gETbUPWZUEjdHsjpjVVsTTKGlHClACQHErLxifFqKwq4XSGBBBiyRunAH1ESiDUkF1JnLdY1TEBQKDQ2KUWNwJH1rSJHz3LYEMl1a/9g6LVoTruAZBIASXNImpqnAXEBpJKB0WFsEElJQkHRHTiSNqPhRFi2IjRxsV6PGcIhF2mBi6AevvFvKsiFz6NGlE2tKsxjOggX68jWtGYoubmjFimiIl0o7jbq9Oo0UT+vc0c08hgjk5iOLKJ/c0ZDaZwh6WbOoW2ZEopWjpHm5N8SxDohkCTXLhDpBKCm1SEfuFEEbTNEg8jFyQhPKK3m8pI5pPVnBfjQmql7cEnPgRsIsMFrcxl09/MKRrhqFUM10wH6I3sPQPqLRTIuwDHHmdksocA3GiQUbXYuz4ZeNmLGvJjLJaqBXW/SIWrBK+46iv2MR2aQU2NCIKE1rLSTYg9wd3j1mqItmXpIwzk3xcMV+CsfovIiymoDSuAkwjRuAND6pV6JXrg4fs98GNBqun2LYlwEAntNesXreUVTAegDore5v7ScuZqJxVz8eMDwg9LRd9CsCCwNGoRY8BhV9PzSm6Sw2saUNucU0CYnFjC3Gj7RVw3yMMRCixk/Go5oGB/BOHAoQ2OM54S3ix+3r84/vn379+Pl67U+/33//+e3p9vS802u5X9r9ks7rvF8zndancOyndZzWp3hyzo5Paz2tT/Gl7td64uuJr6d89JSPnuLZqV67r/flDwTTvfjFPMUmAAAAAElFTkSuQmCC",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x7f548058cc90>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m3d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "获取某个构象GetConformer(id)\n",
    "通过传入id获取指定构象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取第n个构象\n",
    "conformer = m3d.GetConformer(id=6)\n",
    "\n",
    "# 将Conformer对象添加到Mol对象中\n",
    "m3d.AddConformer(conformer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将Conformer类转为Mol类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0;31mDocstring:\u001b[0m\n",
      "GetConformer( (Mol)self [, (int)id=-1]) -> Conformer :\n",
      "    Get the conformer with a specified ID\n",
      "\n",
      "    C++ signature :\n",
      "        RDKit::Conformer* GetConformer(RDKit::ROMol {lvalue} [,int=-1])\n",
      "\u001b[0;31mType:\u001b[0m      method\n"
     ]
    }
   ],
   "source": [
    "mol.GetConformer?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[09:27:05] Molecule does not have explicit Hs. Consider calling AddHs()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from rdkit import Chem\n",
    "\n",
    "# 创建一个rdkit.Chem.rdchem.Mol对象\n",
    "mol = Chem.MolFromSmiles('CCOCC')\n",
    "\n",
    "# 创建一个新的rdkit.Chem.rdchem.Mol对象，通过复制已有的Mol对象\n",
    "new_mol = Chem.Mol(mol)\n",
    "\n",
    "# 创建一个新的rdkit.Chem.rdchem.Conformer对象\n",
    "conformer = Chem.Conformer()\n",
    "\n",
    "AllChem.EmbedMolecule(mol, randomSeed=1)\n",
    "\n",
    "# 设置Conformer对象的原子坐标\n",
    "for atom_idx in range(mol.GetNumAtoms()):\n",
    "    position = mol.GetConformer().GetAtomPosition(atom_idx)\n",
    "    conformer.SetAtomPosition(atom_idx, position)\n",
    "\n",
    "# 将Conformer对象添加到新的Mol对象中\n",
    "new_mol.AddConformer(conformer)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "计算不同构象间的差异：`AlignMolConformers(mol, RMSlist, …)`\n",
    "\n",
    "对同一分子不同的构象先进行重叠排列，再计算RMS（root mean square）值\n",
    "RMSlist：用于接收RMS的列表，它由第一个构象与剩余9个构象依次比对产生。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1.0139971348311405,\n",
       " 1.1711823774961843,\n",
       " 1.9002487407817126,\n",
       " 1.6769520475902233,\n",
       " 1.3894728842138364,\n",
       " 1.2035553979283575,\n",
       " 1.8398346451264453,\n",
       " 1.5654488264569402,\n",
       " 1.026309470592818,\n",
       " 1.2035553979283575]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmslist = []\n",
    "\n",
    "AllChem.AlignMolConformers(m3d, RMSlist=rmslist)\n",
    "\n",
    "print(len(rmslist))\n",
    "rmslist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "也可以计算任意两个指定构象的RMS值`GetConformerRMS(mol, confId1, confId2, atomIds, prealigned)`\n",
    "- `confId1`：第一个构象\n",
    "- `confId2`：第二个构象\n",
    "- `atomIds`：需要对比的原子，默认全部\n",
    "- `prealigned`：构象是否已经对齐，默认False（没有时，函数会自动将它们对齐）\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5589162691593905"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AllChem.GetConformerRMS(m3d, 1, 9, prealigned=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 手动对齐\n",
    "也可以使用`AllChem.AlignMol()`方法。这个方法需要两个参数，即待对齐的两个分子。\n",
    "\n",
    "> 注意，`AlignMol()`方法会修改第二个分子的坐标，将其与第一个分子对齐。如果您想保留原来的分子，可以先复制一份再进行对齐。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "     RDKit          3D\n",
      "\n",
      "  4  3  0  0  0  0  0  0  0  0999 V2000\n",
      "    1.3544   -0.4910   -0.0081 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -0.0213    0.0283   -0.0003 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -0.1937    1.2670   -0.0244 O   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -1.1394   -0.8042    0.0328 O   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "  1  2  1  0\n",
      "  2  3  2  0\n",
      "  2  4  1  0\n",
      "M  END\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[09:37:16] Molecule does not have explicit Hs. Consider calling AddHs()\n",
      "[09:37:16] Molecule does not have explicit Hs. Consider calling AddHs()\n"
     ]
    }
   ],
   "source": [
    "from rdkit import Chem\n",
    "from rdkit.Chem import AllChem\n",
    "\n",
    "# 读取分子\n",
    "mol1 = Chem.MolFromSmiles('CCO')\n",
    "mol2 = Chem.MolFromSmiles('CC(=O)O')\n",
    "AllChem.EmbedMolecule(mol1, randomSeed=1)\n",
    "AllChem.EmbedMolecule(mol2, randomSeed=1)\n",
    "# 对齐分子\n",
    "AllChem.AlignMol(mol1, mol2)\n",
    "\n",
    "# 打印对齐后的分子坐标\n",
    "print(Chem.MolToMolBlock(mol2))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 距离几何+ETKDG+MMFF生成3D构象\n",
    "\n",
    "对距离几何产生的构象，进行ETKDG优化后，还可以继续使用MMFF94等力场进行优化。不过需要注意的是，MMFF力场中的原子类型编码采用了自身的芳香性模型，因此在使用MMFF相关方法后，分子的芳香属性（aromaticity flags）会改变。\n",
    "\n",
    "\n",
    "使用MMFF94进行优化构象：`MMFFOptimizeMolecule(mol, mmffVariant, maxIters, …)`\n",
    "- `mmffVariant`：可选\"MMFF94\"或\"MMFF94s\"，默认MMFF94\n",
    "- `maxIters`：最多迭代次数，默认200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=250x250>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AllChem.EmbedMolecule(m3d, randomSeed=10)\n",
    "AllChem.MMFFOptimizeMolecule(m3d)\n",
    "Draw.MolToImage(m3d, size=(250,250))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 距离几何+ETKDG+MMFF生成多构象\n",
    "\n",
    "其实当使用ETKDG算法生成3D构象时，通常无需再使用MMFF94力场优化。如果执意要这么做，有一个方便的函数可以调用\n",
    "\n",
    "使用MMFF94优化多个构象：`MMFFOptimizeMoleculeConfs()`\n",
    "返回的结果是元组组成的列表，每个元组表示**每个构象的收敛值**和**能量(not_converged, energy)**。如果`not_converged`是0，则收敛，接近最稳态，否则没有到达最稳态。\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 102.33441624587803)]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = AllChem.MMFFOptimizeMoleculeConfs(m3d)\n",
    "res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 多线程生成多构象\n",
    "\n",
    "可以通过numThreads参数进行设置。\n",
    "- `EmbedMultipleConfs(mol, numThreads)`\n",
    "- `MMFFOptimizeMoleculeConfs(mol, numThreads)`\n",
    "    - `numThreads`：默认为1，表示单进程执行。设置为0表示将会使用本机最大线程数执行。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "cids = AllChem.EmbedMultipleConfs(m3d, numThreads=0)\n",
    "res = AllChem.MMFFOptimizeMoleculeConfs(m3d, numThreads=0)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "gnn",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
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  "orig_nbformat": 4
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}