projet de deep-learning. Apprentissage de poches de liaison de protéines-ligands

DeepDrug.ipynb 2.1KB

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113
  1. {
  2. "cells": [
  3. {
  4. "cell_type": "markdown",
  5. "metadata": {},
  6. "source": [
  7. "# DeepDrug3D"
  8. ]
  9. },
  10. {
  11. "cell_type": "code",
  12. "execution_count": 2,
  13. "metadata": {},
  14. "outputs": [],
  15. "source": [
  16. "import numpy as np"
  17. ]
  18. },
  19. {
  20. "cell_type": "markdown",
  21. "metadata": {},
  22. "source": [
  23. "## Create pocket lists\n",
  24. "4 pockets are created :\n",
  25. " + control\n",
  26. " + steroid\n",
  27. " + heme\n",
  28. " + nucleotide"
  29. ]
  30. },
  31. {
  32. "cell_type": "code",
  33. "execution_count": 3,
  34. "metadata": {},
  35. "outputs": [
  36. {
  37. "data": {
  38. "text/plain": [
  39. "''"
  40. ]
  41. },
  42. "execution_count": 3,
  43. "metadata": {},
  44. "output_type": "execute_result"
  45. }
  46. ],
  47. "source": [
  48. "with open(\"control.list\", \"r\") as filin:\n",
  49. " control = filin.read()\n",
  50. "control = control.split(\"\\n\")\n",
  51. "control.pop()\n",
  52. "\n",
  53. "with open(\"steroid.list\", \"r\") as filin:\n",
  54. " steroid = filin.read()\n",
  55. "steroid = steroid.split(\"\\n\")\n",
  56. "steroid.pop()\n",
  57. "\n",
  58. "with open(\"heme.list\", \"r\") as filin:\n",
  59. " heme = filin.read()\n",
  60. "heme = heme.split(\"\\n\")\n",
  61. "heme.pop()\n",
  62. "\n",
  63. "with open(\"nucleotide.list\", \"r\") as filin:\n",
  64. " nucleotide = filin.read()\n",
  65. "nucleotide = nucleotide.split(\"\\n\")\n",
  66. "nucleotide.pop()"
  67. ]
  68. },
  69. {
  70. "cell_type": "code",
  71. "execution_count": 6,
  72. "metadata": {},
  73. "outputs": [],
  74. "source": []
  75. },
  76. {
  77. "cell_type": "code",
  78. "execution_count": null,
  79. "metadata": {},
  80. "outputs": [],
  81. "source": []
  82. },
  83. {
  84. "cell_type": "code",
  85. "execution_count": null,
  86. "metadata": {},
  87. "outputs": [],
  88. "source": []
  89. }
  90. ],
  91. "metadata": {
  92. "kernelspec": {
  93. "display_name": "Python 3",
  94. "language": "python",
  95. "name": "python3"
  96. },
  97. "language_info": {
  98. "codemirror_mode": {
  99. "name": "ipython",
  100. "version": 3
  101. },
  102. "file_extension": ".py",
  103. "mimetype": "text/x-python",
  104. "name": "python",
  105. "nbconvert_exporter": "python",
  106. "pygments_lexer": "ipython3",
  107. "version": "3.7.4"
  108. }
  109. },
  110. "nbformat": 4,
  111. "nbformat_minor": 4
  112. }