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QSAR

Uni-MolnotebookTutorial中文Machine LearningQSAR

OvO

更新于 2024-08-26

推荐镜像 :unimol-qsar:v0.2

推荐机型 :c12_m92_1 * NVIDIA V100

基于Uni-Mol的分子向量表征

©️ Copyright 2023 @ Authors
作者： 高志锋 📨
日期：2023-06-06
共享协议：本作品采用知识共享署名-非商业性使用-相同方式共享 4.0 国际许可协议进行许可。
快速开始：点击上方的 开始连接 按钮，选择 unimol-qsar:v0.2镜像及任意GPU节点配置，稍等片刻即可运行。

开源代码地址：https://github.com/dptech-corp/Uni-Mol

论文地址：https://openreview.net/forum?id=6K2RM6wVqKu

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[1]

# 导入unimol

from unimol import UniMolRepr

import numpy as np

import pandas as pd

/opt/conda/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

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[2]

# single smiles unimol representation

clf = UniMolRepr(data_type='molecule')

df = pd.read_csv('/personal/compounddata.csv')

error_ids = []

# for index, row in df.iterrows():

for index, row in df.iloc[26555:].iterrows():

compound_id = row['COMPOUND_ID']

compound_smiles = row['COMPOUND_SMILES']

# smiles = 'CN1CCC(C(C1)O)C2=C(C=C(C3=C2OC(=CC3=O)C4=CC=CC=C4Cl)O)O'

# id = '5287969'

smiles_list = [compound_smiles]

try:

unimol_repr = np.array(clf.get_repr(smiles_list)["cls_repr"])

print(unimol_repr.shape)

print(str(compound_id))

np.save('/personal/bindingdb3/'+ str(compound_id) + '.npy', unimol_repr)

except Exception as e:

print(f"Error processing compound id {compound_id}: {e}")

# Add the error id into the list

error_ids.append(compound_id)

# Convert the list into a DataFrame and save it into a csv file

error_df = pd.DataFrame(error_ids, columns=['Error_ID'])

error_df.to_csv('/personal/failed.csv', index=False)

2024-07-26 09:28:52 | unimol/models/unimol.py | 116 | INFO | Uni-Mol(QSAR) | Loading pretrained weights from /opt/conda/lib/python3.8/site-packages/unimol-0.0.2-py3.8.egg/unimol/weights/mol_pre_all_h_220816.pt
2024-07-26 09:28:56 | unimol/data/conformer.py | 62 | INFO | Uni-Mol(QSAR) | Start generating conformers...
0it [00:00, ?it/s]

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[3]

%%bash

# 下载样例数据, CNS drug data

rm -rf mol_train.csv

wget -nv https://bohrium-example.oss-cn-zhangjiakou.aliyuncs.com/unimol-qsar/mol_train.csv

2023-06-12 16:10:30 URL:https://bohrium-example.oss-cn-zhangjiakou.aliyuncs.com/unimol-qsar/mol_train.csv [30600/30600] -> "mol_train.csv" [1]

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[4]

smiles_list = pd.read_csv('mol_train.csv')['SMILES'].to_list()

y = pd.read_csv('mol_train.csv')['TARGET'].to_list()

unimol_repr_list = np.array(clf.get_repr(smiles_list)["cls_repr"])

2023-06-12 16:10:31 | unimol/data/conformer.py | 56 | INFO | Uni-Mol(QSAR) | Start generating conformers...
700it [00:10, 66.34it/s]
2023-06-12 16:10:41 | unimol/data/conformer.py | 60 | INFO | Uni-Mol(QSAR) | Failed to generate conformers for 0.00% of molecules.
2023-06-12 16:10:41 | unimol/data/conformer.py | 62 | INFO | Uni-Mol(QSAR) | Failed to generate 3d conformers for 0.00% of molecules.

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[5]

print(unimol_repr_list.shape)

(700, 512)

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[6]

import numpy as np

import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

from sklearn.decomposition import PCA

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[7]

pca = PCA(n_components=2)

X_reduced = pca.fit_transform(unimol_repr_list)

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[8]

# 可视化

colors = ['r', 'g', 'b']

markers = ['s', 'o', 'D']

labels = ['Target:0','Target:1']

plt.figure(figsize=(8, 6))

for label, color, marker in zip(np.unique(y), colors, markers):

plt.scatter(X_reduced[y == label, 0],

X_reduced[y == label, 1],

c=color,

marker=marker,

label=labels[label],

edgecolors='black')

plt.xlabel('Principal Component 1')

plt.ylabel('Principal Component 2')

plt.legend(loc='best')

plt.title('Unimol Repr')

plt.show()

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