如何用Python筛选与药物V0L最相似的候选分子

我一直尝试用Python从下方数据中筛选出最相似的分子，但作为Python编程新手，仅能完成绘图操作。请问如何结合表面积（Su）、体积（Vol）、椭圆度（Ov）等所有特征因素，选出能在各方面复刻药物V0L的最佳候选分子？V0L为真实药物（最后一行），其余为候选分子。

分子特征数据 #

Mol   Su     Vol        Su/Vol  PSA      Ov     D   A     Mw    Vina

 1.  1  357.18  333.9   1.069721473 143.239 1.53    5   10  369.35  -8.3
 2.  2  510.31  496.15  1.028539756 137.388 1.68    6   12  562.522 -8.8
 3.  3  507.07  449.84  1.127223013 161.116 1.68    6   12  516.527 -9.0
 4.  4  536.54  524.75  1.022467842 172.004 1.71    7   13  555.564 -9.8
 5.  5  513.67  499.05  1.029295662 180.428 1.69    7   13  532.526 -8.9
 6.  6  391.19  371.71  1.052406446 152.437 1.56    6   11  408.387 -8.9
 7.  7  540.01  528.8   1.021198941 149.769 1.71    7   13  565.559 -9.4
 8.  8  534.81  525.99  1.01676838  174.741 1.7     7   13  555.564 -9.3
 9.  9  533.42  520.67  1.024487679 181.606 1.7     7   14  566.547 -9.7
 10. 10 532.52  529.47  1.005760477 179.053 1.68    8   14  571.563 -9.4
 11. 11 366.72  345.89  1.060221458 159.973 1.54    6   11  385.349 -8.2
 12. 12 520.75  504.36  1.032496629 168.866 1.7     6   13  542.521 -8.7
 13. 13 512.69  499     1.02743487  179.477 1.69    7   13  532.526 -8.6
 14. 14 542.78  531.52  1.021184527 189.293 1.71    7   14  571.563 -9.6
 15. 15 519.04  505.7   1.026379276 196.982 1.69    8   14  548.525 -8.8
 16. 16 328.95  314.03  1.047511384 125.069 1.47    4   9   339.324 -6.9
 17. 17 451.68  444.63  1.01585588  118.025 1.6     5   10  466.47  -9.4
 18. 18 469.67  466.11  1.007637682 130.99  1.62    5   11  486.501 -8.3
 19. 19 500.79  498.09  1.005420707 146.805 1.65    6   12  525.538 -9.8
 20. 20 476.59  473.03  1.00752595  149.821 1.62    6   12  502.5   -8.4
 21. 21 357.84  347.14  1.030823299 138.147 1.5     5   10  378.361 -8.6
 22. 22 484.15  477.28  1.014394066 129.93  1.64    6   11  505.507 -10.2
 23. 23 502.15  498.71  1.006897796 142.918 1.65    6   12  525.538 -9.3
 24. 24 526.73  530.31  0.993249232 154.106 1.66    7   13  564.575 -9.9
 25. 25 509.34  505.64  1.007317459 161.844 1.66    7   13  541.537 -9.2
 26. 26 337.53  320.98  1.051560845 144.797 1.49    5   10  355.323 -7.1
 27. 27 460.25  451.58  1.019199256 137.732 1.62    5   11  482.469 -9.6
 28. 28 478.4   473.25  1.010882198 155.442 1.63    6   12  502.5   -8.9
 29. 29 507.62  505.68  1.003836418 161.884 1.65    6   13  541.537 -9.2
 30. 30 482.27  479.07  1.006679608 171.298 1.63    7   13  518.499 -9.1
 31.V0L 355.19  333.42  1.065293024 59.105  1.530   0   9   345.37  -10.4

特征说明 #

• Su：表面积（单位：平方埃）
• Vol：体积（单位：立方埃）
• Su/Vol：表面积体积比
• PSA：极性表面积（单位：平方埃）
• Ov：椭圆度
• D：氢键供体基团数量
• A：氢键受体基团数量
• Vina：结合亲和力（数值越低，亲和力越强）
• Mw：分子量
• Mol：候选分子编号

实现步骤与代码 #

1. 安装依赖库 #

如果还没安装pandas和scikit-learn，先执行：

pip install pandas scikit-learn

2. 完整代码 #

import pandas as pd
from sklearn.preprocessing import StandardScaler

# 1. 读取数据
data_str = """Mol,Su,Vol,Su/Vol,PSA,Ov,D,A,Mw,Vina
1,357.18,333.9,1.069721473,143.239,1.53,5,10,369.35,-8.3
2,510.31,496.15,1.028539756,137.388,1.68,6,12,562.522,-8.8
3,507.07,449.84,1.127223013,161.116,1.68,6,12,516.527,-9.0
4,536.54,524.75,1.022467842,172.004,1.71,7,13,555.564,-9.8
5,513.67,499.05,1.029295662,180.428,1.69,7,13,532.526,-8.9
6,391.19,371.71,1.052406446,152.437,1.56,6,11,408.387,-8.9
7,540.01,528.8,1.021198941,149.769,1.71,7,13,565.559,-9.4
8,534.81,525.99,1.01676838,174.741,1.7,7,13,555.564,-9.3
9,533.42,520.67,1.024487679,181.606,1.7,7,14,566.547,-9.7
10,532.52,529.47,1.005760477,179.053,1.68,8,14,571.563,-9.4
11,366.72,345.89,1.060221458,159.973,1.54,6,11,385.349,-8.2
12,520.75,504.36,1.032496629,168.866,1.7,6,13,542.521,-8.7
13,512.69,499,1.02743487,179.477,1.69,7,13,532.526,-8.6
14,542.78,531.52,1.021184527,189.293,1.71,7,14,571.563,-9.6
15,519.04,505.7,1.026379276,196.982,1.69,8,14,548.525,-8.8
16,328.95,314.03,1.047511384,125.069,1.47,4,9,339.324,-6.9
17,451.68,444.63,1.01585588,118.025,1.6,5,10,466.47,-9.4
18,469.67,466.11,1.007637682,130.99,1.62,5,11,486.501,-8.3
19,500.79,498.09,1.005420707,146.805,1.65,6,12,525.538,-9.8
20,476.59,473.03,1.00752595,149.821,1.62,6,12,502.5,-8.4
21,357.84,347.14,1.030823299,138.147,1.5,5,10,378.361,-8.6
22,484.15,477.28,1.014394066,129.93,1.64,6,11,505.507,-10.2
23,502.15,498.71,1.006897796,142.918,1.65,6,12,525.538,-9.3
24,526.73,530.31,0.993249232,154.106,1.66,7,13,564.575,-9.9
25,509.34,505.64,1.007317459,161.844,1.66,7,13,541.537,-9.2
26,337.53,320.98,1.051560845,144.797,1.49,5,10,355.323,-7.1
27,460.25,451.58,1.019199256,137.732,1.62,5,11,482.469,-9.6
28,478.4,473.25,1.010882198,155.442,1.63,6,12,502.5,-8.9
29,507.62,5