python画出分子化学空间分布（UMAP）

利用umap画出分子化学空间分布图
安装

pip install umap-learn

下面是用一个数据集举的例子

import torch
import umap
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.manifold import TSNE
from rdkit.Chem import AllChem as Chem
from sklearn.preprocessing import StandardScaler

def get_fp(list_of_smi):
    """ Function to get fingerprint from a list of SMILES"""
    fingerprints = []
    mols = [Chem.MolFromSmiles(x) for x in list_of_smi]
    # if rdkit can't compute the fingerprint on a SMILES
    # we remove that SMILES
    idx_to_remove = []
    for idx,mol in enumerate(mols):
        try:
            fprint = Chem.GetMorganFingerprintAsBitVect(mol, 2, useFeatures=False)
            fingerprints.append(fprint)
        except:
            idx_to_remove.append(idx)
    
    smi_to_keep = [smi for i,smi in enumerate(list_of_smi) if i not in idx_to_remove]
    return fingerprints, smi_to_keep
def get_embedding(data):
    """ Function to compute the UMAP embedding"""            
    data_scaled = StandardScaler().fit_transform(data)
    
    embedding = umap.UMAP(n_neighbors=10,
                          min_dist=0.5,
                          metric='correlation',
                          random_state=16).fit_transform(data_scaled)
    
    return embedding
def draw_umap(embedding_hp1):
    fig, ax = plt.subplots(figsize=(50, 40))
    contour_c='#444444'
    plt.xlim([np.min(embedding_hp1[:,0])-0.5, np.max(embedding_hp1[:,0])+1.5])
    plt.ylim([np.min(embedding_hp1[:,1])-0.5, np.max(embedding_hp1[:,1])+0.5])
    labelsize = 40
    plt.xlabel('UMAP 1', fontsize=labelsize)
    plt.ylabel('UMAP 2', fontsize=labelsize)
    ax.spines['right'].set_visible(False)
    ax.spines['top'].set_visible(False)
    plt.scatter(embedding_hp1[:, 0], embedding_hp1[:, 1], lw=0, c='#D55E00', label='HPK1', alpha=1.0, s=180, marker="o", edgecolors='k', linewidth=2) 
    leg = plt.legend(prop={'size': labelsize}, loc='upper right', markerscale=2.00)
    leg.get_frame().set_alpha(0.9)    
    plt.setp(ax, xticks=[], yticks=[])
    plt.show()
def main():
    hpk1 = pd.read_csv("/y/Aurora/Fergie/data/preprocessed/HPK1_preprocess.csv")
    smiles_h1 = hpk1["SMILES"]
    fp_hp1, sm_for_hp1 = get_fp(smiles_h1)
    fp_hp1 = np.array(fp_hp1)

    embedding_hp1 = get_embedding(fp_hp1)
    draw_umap(embedding_hp1)

if __name__ == '__main__':
    main()

在jupyter notebook上运行成功，结果如下：
可用于对比不同数据集的化学空间，以下是三个数据集化学空间的分布图
Reference：
https://github.com/ETHmodlab/virtual_libraries

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