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Uni-Mol预测液流电池溶解度

AI4S经验分享

Uni-Mol

Hackathon

AI4S经验分享Uni-MolHackathon

zmz

AndyX

更新于 2024-10-31

推荐镜像 :Third-party software:Deprecated

推荐机型 :c3_m4_1 * NVIDIA T4

数据集

液流电池溶解度(v2)

Uni-Mol预测液流电池溶解度

©️ Copyright 2023 @ Authors
作者： 谢启明 📨 张明正 📨
日期：2023-09-06
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:v0.4镜像及任意GPU节点配置，稍等片刻即可运行。

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AIMS：

利用Uni-Mol进行液流电池溶解度预测的实战
理解Uni-Mol的工作模块
使用以SMILE字符串为输入的训练方法
使用回归模型来预测连续数值
使用训练出的模型预测一些水系液流电池电活性分子的溶解度

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Overview

1.简介

水系有机液流电池 (AORFB)作为大规模储能技术，在实现可再生能源高效利用方面展现出良好的应用潜力。AORFB采用水溶性有机电活性分子作为电解质，这类有机电解质具备原料来源丰富、性能高度可调和动力学快速等优点，赋予液流电池更加广阔的应用前景。但是有机电活性分子溶解度有限、电解液凝固和反应动力学缓慢，低温下循环的水系有机液流电池性能难以保证[1]。

2.液流电池核心指标

液流电池最核心的性能指标为能量密度，RFB理论能量密度由下式得出[2]。

$W = \frac{n CF V}{μ _{v}}$

式中：W是能量密度，Wh·L^-1；n是参与氧化还原反应的电子数；C是溶解在电解液中的电活性物质的最低浓度，mol·L^-1；F是法拉第常数；V是电池电压，V； $μ_{v}$ 是体积因数， $μ_{v}$ =1+(溶解度较低的活性物质浓度/溶解度较高的活性物质浓度)，当浓度相等时， $μ_{v}$ =2。

因此能量密度由电解液中的活性物质的溶解度、氧化还原电位和转移的电子数共同决定。故提高能量密度的方法主要从增大溶解度、提高电化学窗口和电子的得失数这3个方面着手。结合高通量计算方法筛选有机电活性分子，助力下一代氧化还原液流电池的成功开发。

3.RedDB数据库简介

本项目利用RedDB[3]数据库。RedDB具有31,618条分子数据，这些分子是通过52个不同核心分子的结构功能化而衍生的。内含OPT几何优化、SPE单点能两种DFT计算结果的结果和利用水溶性预测模型（AqSolPred v1.0）[4]预测的溶解度数据。REdDB中溶解度数值单位为mol/L的无量纲对数--LogS。

4.AqSolPred简介

AqSolPred集成三种ML方法，在一个含9982条实验溶解度数据的数据库——AqSolDB上进行训练。该模型的MAE为0.348。AqSolPred 模型之前已在基准溶解度数据集上进行了验证[6].该模型的平均绝对误差为0.348LogS，低于通常用于预测化学物质水溶性的传统化学信息学和机器学习方法[4]。分子的SMILES表示被用作AqSolPred的输入，其ML预测的溶解度数据已被纳入RedDB。

5.目标

1)使用以SMILE字符串为输入的训练方法;

2)使用Uni-Mol分子通用学习框架来预测连续数值;

3)使用训练出的模型预测一些水系液流电池电活性化合物溶解度。

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Step1:导入Uni-Mol

导入Uni-Mol所需模块

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[1]

# 导入Uni-Mol

from unimol import MolTrain, MolPredict

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Step2:输入数据并训练（可跳过）

训练时间可能较长，训练好的数据（csv文件）均已上传至数据集，时间有限的朋友可以跳过此步。

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[2]

clf = MolTrain(task='regression',

data_type='molecule',

epochs=50,

learning_rate=0.0004,

batch_size=32,

early_stopping=5,

metrics='r2',

split='random',

save_path='./best_exp',

)

clf.fit('/bohr/aorfb-7iz1/v2/RedDB_train.csv')

2023-09-04 15:51:18 | unimol/data/datareader.py | 138 | INFO | Uni-Mol(QSAR) | Anomaly clean with 3 sigma threshold: 25294 -> 25293
2023-09-04 15:51:22 | unimol/data/conformer.py | 62 | INFO | Uni-Mol(QSAR) | Start generating conformers...
25293it [02:46, 151.98it/s]
2023-09-04 15:54:09 | unimol/data/conformer.py | 66 | INFO | Uni-Mol(QSAR) | Failed to generate conformers for 0.00% of molecules.
2023-09-04 15:54:09 | unimol/data/conformer.py | 68 | INFO | Uni-Mol(QSAR) | Failed to generate 3d conformers for 0.03% of molecules.
2023-09-04 15:54:09 | unimol/train.py | 88 | INFO | Uni-Mol(QSAR) | Output directory already exists: ./best_exp
2023-09-04 15:54:09 | unimol/train.py | 89 | INFO | Uni-Mol(QSAR) | Warning: Overwrite output directory: ./best_exp
2023-09-04 15:54:10 | unimol/models/unimol.py | 114 | 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
2023-09-04 15:54:10 | unimol/models/nnmodel.py | 103 | INFO | Uni-Mol(QSAR) | start training Uni-Mol:unimolv1
2023-09-04 15:55:22 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [1/50] train_loss: 0.1694, val_loss: 0.0961, val_r2: 0.9043, lr: 0.000267, 69.2s
2023-09-04 15:56:27 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [2/50] train_loss: 0.0543, val_loss: 0.0285, val_r2: 0.9716, lr: 0.000396, 63.7s
2023-09-04 15:57:32 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [3/50] train_loss: 0.0273, val_loss: 0.0284, val_r2: 0.9716, lr: 0.000388, 64.2s
2023-09-04 15:58:36 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [4/50] train_loss: 0.0210, val_loss: 0.0169, val_r2: 0.9831, lr: 0.000379, 63.1s
2023-09-04 15:59:44 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [5/50] train_loss: 0.0184, val_loss: 0.0140, val_r2: 0.9860, lr: 0.000371, 67.6s
2023-09-04 16:00:56 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [6/50] train_loss: 0.0151, val_loss: 0.0165, val_r2: 0.9835, lr: 0.000363, 71.0s
2023-09-04 16:01:59 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [7/50] train_loss: 0.0144, val_loss: 0.0121, val_r2: 0.9879, lr: 0.000355, 63.4s
2023-09-04 16:03:03 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [8/50] train_loss: 0.0126, val_loss: 0.0121, val_r2: 0.9878, lr: 0.000346, 63.7s
2023-09-04 16:04:07 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [9/50] train_loss: 0.0115, val_loss: 0.0169, val_r2: 0.9831, lr: 0.000338, 63.7s
2023-09-04 16:05:13 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [10/50] train_loss: 0.0103, val_loss: 0.0141, val_r2: 0.9858, lr: 0.000330, 65.6s
2023-09-04 16:06:19 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [11/50] train_loss: 0.0102, val_loss: 0.0122, val_r2: 0.9877, lr: 0.000322, 66.1s
2023-09-04 16:07:21 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [12/50] train_loss: 0.0092, val_loss: 0.0084, val_r2: 0.9917, lr: 0.000313, 62.2s
2023-09-04 16:08:26 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [13/50] train_loss: 0.0084, val_loss: 0.0087, val_r2: 0.9913, lr: 0.000305, 64.6s
2023-09-04 16:09:37 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [14/50] train_loss: 0.0074, val_loss: 0.0092, val_r2: 0.9908, lr: 0.000297, 70.3s
2023-09-04 16:10:44 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [15/50] train_loss: 0.0081, val_loss: 0.0113, val_r2: 0.9887, lr: 0.000289, 67.2s
2023-09-04 16:11:53 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [16/50] train_loss: 0.0069, val_loss: 0.0080, val_r2: 0.9920, lr: 0.000280, 69.3s
2023-09-04 16:12:57 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [17/50] train_loss: 0.0064, val_loss: 0.0090, val_r2: 0.9909, lr: 0.000272, 62.8s
2023-09-04 16:14:08 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [18/50] train_loss: 0.0057, val_loss: 0.0069, val_r2: 0.9931, lr: 0.000264, 71.6s
2023-09-04 16:15:12 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [19/50] train_loss: 0.0054, val_loss: 0.0069, val_r2: 0.9931, lr: 0.000256, 63.6s
2023-09-04 16:16:19 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [20/50] train_loss: 0.0056, val_loss: 0.0074, val_r2: 0.9926, lr: 0.000247, 65.7s
2023-09-04 16:17:21 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [21/50] train_loss: 0.0050, val_loss: 0.0071, val_r2: 0.9929, lr: 0.000239, 61.8s
2023-09-04 16:18:24 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [22/50] train_loss: 0.0046, val_loss: 0.0085, val_r2: 0.9916, lr: 0.000231, 63.6s
2023-09-04 16:19:29 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [23/50] train_loss: 0.0041, val_loss: 0.0061, val_r2: 0.9939, lr: 0.000223, 64.9s
2023-09-04 16:20:37 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [24/50] train_loss: 0.0040, val_loss: 0.0062, val_r2: 0.9938, lr: 0.000214, 67.2s
2023-09-04 16:21:41 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [25/50] train_loss: 0.0043, val_loss: 0.0061, val_r2: 0.9939, lr: 0.000206, 64.2s
2023-09-04 16:22:45 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [26/50] train_loss: 0.0037, val_loss: 0.0056, val_r2: 0.9944, lr: 0.000198, 63.6s
2023-09-04 16:23:57 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [27/50] train_loss: 0.0035, val_loss: 0.0057, val_r2: 0.9943, lr: 0.000190, 71.6s
2023-09-04 16:25:01 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [28/50] train_loss: 0.0033, val_loss: 0.0058, val_r2: 0.9942, lr: 0.000181, 64.2s
2023-09-04 16:26:06 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [29/50] train_loss: 0.0031, val_loss: 0.0056, val_r2: 0.9944, lr: 0.000173, 65.1s
2023-09-04 16:27:19 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [30/50] train_loss: 0.0030, val_loss: 0.0055, val_r2: 0.9945, lr: 0.000165, 72.3s
2023-09-04 16:28:31 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [31/50] train_loss: 0.0029, val_loss: 0.0049, val_r2: 0.9950, lr: 0.000157, 72.2s
2023-09-04 16:29:40 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [32/50] train_loss: 0.0028, val_loss: 0.0051, val_r2: 0.9949, lr: 0.000148, 67.9s
2023-09-04 16:30:45 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [33/50] train_loss: 0.0026, val_loss: 0.0051, val_r2: 0.9949, lr: 0.000140, 65.1s
2023-09-04 16:31:48 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [34/50] train_loss: 0.0026, val_loss: 0.0050, val_r2: 0.9950, lr: 0.000132, 62.8s
2023-09-04 16:33:04 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [35/50] train_loss: 0.0023, val_loss: 0.0049, val_r2: 0.9951, lr: 0.000124, 76.5s
2023-09-04 16:34:22 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [36/50] train_loss: 0.0022, val_loss: 0.0048, val_r2: 0.9952, lr: 0.000115, 76.7s
2023-09-04 16:35:30 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [37/50] train_loss: 0.0021, val_loss: 0.0050, val_r2: 0.9950, lr: 0.000107, 67.9s
2023-09-04 16:36:41 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [38/50] train_loss: 0.0020, val_loss: 0.0048, val_r2: 0.9951, lr: 0.000099, 70.6s
2023-09-04 16:37:53 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [39/50] train_loss: 0.0019, val_loss: 0.0046, val_r2: 0.9954, lr: 0.000091, 72.5s
2023-09-04 16:38:59 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [40/50] train_loss: 0.0020, val_loss: 0.0045, val_r2: 0.9955, lr: 0.000082, 64.9s
2023-09-04 16:40:03 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [41/50] train_loss: 0.0018, val_loss: 0.0046, val_r2: 0.9954, lr: 0.000074, 63.6s
2023-09-04 16:41:07 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [42/50] train_loss: 0.0018, val_loss: 0.0046, val_r2: 0.9954, lr: 0.000066, 64.4s
2023-09-04 16:42:10 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [43/50] train_loss: 0.0018, val_loss: 0.0045, val_r2: 0.9955, lr: 0.000058, 62.9s
2023-09-04 16:43:14 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [44/50] train_loss: 0.0016, val_loss: 0.0046, val_r2: 0.9954, lr: 0.000049, 63.2s
2023-09-04 16:44:16 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [45/50] train_loss: 0.0015, val_loss: 0.0045, val_r2: 0.9954, lr: 0.000041, 62.2s
2023-09-04 16:45:20 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [46/50] train_loss: 0.0015, val_loss: 0.0044, val_r2: 0.9956, lr: 0.000033, 63.6s
2023-09-04 16:46:29 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [47/50] train_loss: 0.0014, val_loss: 0.0044, val_r2: 0.9956, lr: 0.000025, 68.4s
2023-09-04 16:47:33 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [48/50] train_loss: 0.0014, val_loss: 0.0044, val_r2: 0.9956, lr: 0.000016, 64.3s
2023-09-04 16:48:37 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [49/50] train_loss: 0.0014, val_loss: 0.0045, val_r2: 0.9955, lr: 0.000008, 63.3s
2023-09-04 16:49:40 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [50/50] train_loss: 0.0013, val_loss: 0.0044, val_r2: 0.9956, lr: 0.000000, 63.5s
2023-09-04 16:49:41 | unimol/tasks/trainer.py | 213 | INFO | Uni-Mol(QSAR) | load model success!
2023-09-04 16:49:46 | unimol/models/nnmodel.py | 129 | INFO | Uni-Mol(QSAR) | fold 0, result {'r2': 0.9956226848041148, 'mae': 0.05991403, 'pearsonr': 0.9978418748820779, 'spearmanr': 0.9973003817239617, 'mse': 0.0075112535}
2023-09-04 16:49:46 | unimol/models/unimol.py | 114 | 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
2023-09-04 16:50:51 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [1/50] train_loss: 0.1861, val_loss: 0.0841, val_r2: 0.9168, lr: 0.000267, 64.9s
2023-09-04 16:51:57 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [2/50] train_loss: 0.0599, val_loss: 0.0362, val_r2: 0.9642, lr: 0.000396, 64.7s
2023-09-04 16:53:06 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [3/50] train_loss: 0.0300, val_loss: 0.0262, val_r2: 0.9741, lr: 0.000388, 68.0s
2023-09-04 16:54:10 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [4/50] train_loss: 0.0227, val_loss: 0.0228, val_r2: 0.9774, lr: 0.000379, 63.1s
2023-09-04 16:55:22 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [5/50] train_loss: 0.0179, val_loss: 0.0199, val_r2: 0.9804, lr: 0.000371, 71.4s
2023-09-04 16:56:29 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [6/50] train_loss: 0.0166, val_loss: 0.0142, val_r2: 0.9859, lr: 0.000363, 66.7s
2023-09-04 16:57:33 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [7/50] train_loss: 0.0142, val_loss: 0.0163, val_r2: 0.9839, lr: 0.000355, 63.5s
2023-09-04 16:58:37 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [8/50] train_loss: 0.0136, val_loss: 0.0163, val_r2: 0.9839, lr: 0.000346, 64.0s
2023-09-04 16:59:43 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [9/50] train_loss: 0.0113, val_loss: 0.0192, val_r2: 0.9810, lr: 0.000338, 66.1s
2023-09-04 17:00:46 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [10/50] train_loss: 0.0119, val_loss: 0.0127, val_r2: 0.9874, lr: 0.000330, 62.6s
2023-09-04 17:01:50 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [11/50] train_loss: 0.0110, val_loss: 0.0165, val_r2: 0.9836, lr: 0.000322, 63.0s
2023-09-04 17:02:54 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [12/50] train_loss: 0.0105, val_loss: 0.0100, val_r2: 0.9901, lr: 0.000313, 64.6s
2023-09-04 17:04:02 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [13/50] train_loss: 0.0091, val_loss: 0.0092, val_r2: 0.9909, lr: 0.000305, 67.8s
2023-09-04 17:05:06 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [14/50] train_loss: 0.0097, val_loss: 0.0091, val_r2: 0.9910, lr: 0.000297, 63.1s
2023-09-04 17:06:11 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [15/50] train_loss: 0.0076, val_loss: 0.0077, val_r2: 0.9923, lr: 0.000289, 63.6s
2023-09-04 17:07:14 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [16/50] train_loss: 0.0072, val_loss: 0.0110, val_r2: 0.9891, lr: 0.000280, 62.7s
2023-09-04 17:08:17 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [17/50] train_loss: 0.0075, val_loss: 0.0071, val_r2: 0.9929, lr: 0.000272, 62.6s
2023-09-04 17:09:21 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [18/50] train_loss: 0.0065, val_loss: 0.0072, val_r2: 0.9929, lr: 0.000264, 63.8s
2023-09-04 17:10:27 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [19/50] train_loss: 0.0063, val_loss: 0.0086, val_r2: 0.9914, lr: 0.000256, 65.5s
2023-09-04 17:11:30 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [20/50] train_loss: 0.0064, val_loss: 0.0078, val_r2: 0.9922, lr: 0.000247, 63.8s
2023-09-04 17:12:33 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [21/50] train_loss: 0.0055, val_loss: 0.0058, val_r2: 0.9943, lr: 0.000239, 63.1s
2023-09-04 17:13:40 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [22/50] train_loss: 0.0052, val_loss: 0.0063, val_r2: 0.9938, lr: 0.000231, 66.2s
2023-09-04 17:14:49 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [23/50] train_loss: 0.0053, val_loss: 0.0090, val_r2: 0.9911, lr: 0.000223, 69.1s
2023-09-04 17:16:03 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [24/50] train_loss: 0.0051, val_loss: 0.0058, val_r2: 0.9943, lr: 0.000214, 73.9s
2023-09-04 17:17:08 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [25/50] train_loss: 0.0047, val_loss: 0.0053, val_r2: 0.9947, lr: 0.000206, 63.8s
2023-09-04 17:18:12 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [26/50] train_loss: 0.0040, val_loss: 0.0056, val_r2: 0.9945, lr: 0.000198, 63.4s
2023-09-04 17:19:18 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [27/50] train_loss: 0.0040, val_loss: 0.0060, val_r2: 0.9941, lr: 0.000190, 65.5s
2023-09-04 17:20:20 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [28/50] train_loss: 0.0039, val_loss: 0.0070, val_r2: 0.9931, lr: 0.000181, 62.9s
2023-09-04 17:21:35 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [29/50] train_loss: 0.0036, val_loss: 0.0080, val_r2: 0.9921, lr: 0.000173, 74.9s
2023-09-04 17:22:39 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [30/50] train_loss: 0.0036, val_loss: 0.0050, val_r2: 0.9950, lr: 0.000165, 64.1s
2023-09-04 17:23:44 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [31/50] train_loss: 0.0036, val_loss: 0.0060, val_r2: 0.9941, lr: 0.000157, 63.5s
2023-09-04 17:24:48 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [32/50] train_loss: 0.0033, val_loss: 0.0050, val_r2: 0.9950, lr: 0.000148, 64.0s
2023-09-04 17:25:51 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [33/50] train_loss: 0.0032, val_loss: 0.0044, val_r2: 0.9956, lr: 0.000140, 63.4s
2023-09-04 17:27:01 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [34/50] train_loss: 0.0028, val_loss: 0.0046, val_r2: 0.9954, lr: 0.000132, 69.0s
2023-09-04 17:28:11 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [35/50] train_loss: 0.0026, val_loss: 0.0043, val_r2: 0.9958, lr: 0.000124, 70.3s
2023-09-04 17:29:19 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [36/50] train_loss: 0.0025, val_loss: 0.0052, val_r2: 0.9949, lr: 0.000115, 67.5s
2023-09-04 17:30:25 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [37/50] train_loss: 0.0025, val_loss: 0.0046, val_r2: 0.9955, lr: 0.000107, 65.6s
2023-09-04 17:31:30 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [38/50] train_loss: 0.0024, val_loss: 0.0046, val_r2: 0.9955, lr: 0.000099, 65.2s
2023-09-04 17:32:34 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [39/50] train_loss: 0.0023, val_loss: 0.0042, val_r2: 0.9958, lr: 0.000091, 63.9s
2023-09-04 17:33:39 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [40/50] train_loss: 0.0022, val_loss: 0.0054, val_r2: 0.9947, lr: 0.000082, 64.3s
2023-09-04 17:34:44 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [41/50] train_loss: 0.0020, val_loss: 0.0043, val_r2: 0.9957, lr: 0.000074, 64.5s
2023-09-04 17:35:48 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [42/50] train_loss: 0.0020, val_loss: 0.0049, val_r2: 0.9951, lr: 0.000066, 64.6s
2023-09-04 17:36:56 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [43/50] train_loss: 0.0018, val_loss: 0.0047, val_r2: 0.9953, lr: 0.000058, 67.7s
2023-09-04 17:38:05 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [44/50] train_loss: 0.0018, val_loss: 0.0041, val_r2: 0.9959, lr: 0.000049, 68.6s
2023-09-04 17:39:14 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [45/50] train_loss: 0.0017, val_loss: 0.0042, val_r2: 0.9959, lr: 0.000041, 68.0s
2023-09-04 17:40:19 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [46/50] train_loss: 0.0017, val_loss: 0.0040, val_r2: 0.9960, lr: 0.000033, 65.3s
2023-09-04 17:41:23 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [47/50] train_loss: 0.0019, val_loss: 0.0043, val_r2: 0.9957, lr: 0.000025, 63.4s
2023-09-04 17:42:32 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [48/50] train_loss: 0.0019, val_loss: 0.0042, val_r2: 0.9959, lr: 0.000016, 68.8s
2023-09-04 17:43:37 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [49/50] train_loss: 0.0015, val_loss: 0.0040, val_r2: 0.9960, lr: 0.000008, 64.9s
2023-09-04 17:44:43 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [50/50] train_loss: 0.0015, val_loss: 0.0042, val_r2: 0.9959, lr: 0.000000, 66.0s
2023-09-04 17:44:45 | unimol/tasks/trainer.py | 213 | INFO | Uni-Mol(QSAR) | load model success!
2023-09-04 17:44:50 | unimol/models/nnmodel.py | 129 | INFO | Uni-Mol(QSAR) | fold 1, result {'r2': 0.9960434374756032, 'mae': 0.059422977, 'pearsonr': 0.9980879792930252, 'spearmanr': 0.997501112441128, 'mse': 0.0068540904}
2023-09-04 17:44:50 | unimol/models/unimol.py | 114 | 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
2023-09-04 17:45:54 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [1/50] train_loss: 0.1815, val_loss: 0.0471, val_r2: 0.9540, lr: 0.000267, 64.0s
2023-09-04 17:47:01 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [2/50] train_loss: 0.0518, val_loss: 0.0225, val_r2: 0.9778, lr: 0.000396, 65.7s
2023-09-04 17:48:20 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [3/50] train_loss: 0.0276, val_loss: 0.0249, val_r2: 0.9756, lr: 0.000388, 77.8s
2023-09-04 17:49:31 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [4/50] train_loss: 0.0196, val_loss: 0.0150, val_r2: 0.9852, lr: 0.000379, 71.6s
2023-09-04 17:50:45 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [5/50] train_loss: 0.0169, val_loss: 0.0158, val_r2: 0.9843, lr: 0.000371, 73.1s
2023-09-04 17:51:53 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [6/50] train_loss: 0.0153, val_loss: 0.0114, val_r2: 0.9888, lr: 0.000363, 68.1s
2023-09-04 17:52:59 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [7/50] train_loss: 0.0131, val_loss: 0.0123, val_r2: 0.9880, lr: 0.000355, 65.7s
2023-09-04 17:54:04 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [8/50] train_loss: 0.0123, val_loss: 0.0099, val_r2: 0.9902, lr: 0.000346, 64.8s
2023-09-04 17:55:10 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [9/50] train_loss: 0.0105, val_loss: 0.0110, val_r2: 0.9891, lr: 0.000338, 64.9s
2023-09-04 17:56:12 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [10/50] train_loss: 0.0101, val_loss: 0.0123, val_r2: 0.9878, lr: 0.000330, 62.7s
2023-09-04 17:57:16 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [11/50] train_loss: 0.0094, val_loss: 0.0116, val_r2: 0.9885, lr: 0.000322, 63.1s
2023-09-04 17:58:18 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [12/50] train_loss: 0.0089, val_loss: 0.0103, val_r2: 0.9899, lr: 0.000313, 62.9s
2023-09-04 17:59:22 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [13/50] train_loss: 0.0080, val_loss: 0.0082, val_r2: 0.9919, lr: 0.000305, 63.6s
2023-09-04 18:00:28 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [14/50] train_loss: 0.0081, val_loss: 0.0089, val_r2: 0.9912, lr: 0.000297, 65.5s
2023-09-04 18:01:31 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [15/50] train_loss: 0.0072, val_loss: 0.0107, val_r2: 0.9894, lr: 0.000289, 63.3s
2023-09-04 18:02:34 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [16/50] train_loss: 0.0068, val_loss: 0.0074, val_r2: 0.9927, lr: 0.000280, 62.6s
2023-09-04 18:03:38 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [17/50] train_loss: 0.0060, val_loss: 0.0069, val_r2: 0.9932, lr: 0.000272, 63.2s
2023-09-04 18:04:45 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [18/50] train_loss: 0.0055, val_loss: 0.0074, val_r2: 0.9927, lr: 0.000264, 66.7s
2023-09-04 18:05:56 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [19/50] train_loss: 0.0054, val_loss: 0.0067, val_r2: 0.9933, lr: 0.000256, 70.5s
2023-09-04 18:06:59 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [20/50] train_loss: 0.0053, val_loss: 0.0072, val_r2: 0.9929, lr: 0.000247, 62.5s
2023-09-04 18:08:01 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [21/50] train_loss: 0.0049, val_loss: 0.0074, val_r2: 0.9927, lr: 0.000239, 62.2s
2023-09-04 18:09:03 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [22/50] train_loss: 0.0044, val_loss: 0.0063, val_r2: 0.9938, lr: 0.000231, 62.6s
2023-09-04 18:10:10 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [23/50] train_loss: 0.0045, val_loss: 0.0072, val_r2: 0.9929, lr: 0.000223, 66.2s
2023-09-04 18:11:14 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [24/50] train_loss: 0.0041, val_loss: 0.0057, val_r2: 0.9943, lr: 0.000214, 64.2s
2023-09-04 18:12:19 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [25/50] train_loss: 0.0043, val_loss: 0.0069, val_r2: 0.9932, lr: 0.000206, 63.9s
2023-09-04 18:13:22 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [26/50] train_loss: 0.0038, val_loss: 0.0057, val_r2: 0.9944, lr: 0.000198, 63.1s
2023-09-04 18:14:27 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [27/50] train_loss: 0.0034, val_loss: 0.0054, val_r2: 0.9946, lr: 0.000190, 64.9s
2023-09-04 18:15:46 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [28/50] train_loss: 0.0034, val_loss: 0.0060, val_r2: 0.9941, lr: 0.000181, 78.0s
2023-09-04 18:17:04 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [29/50] train_loss: 0.0032, val_loss: 0.0054, val_r2: 0.9947, lr: 0.000173, 77.9s
2023-09-04 18:18:17 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [30/50] train_loss: 0.0029, val_loss: 0.0053, val_r2: 0.9947, lr: 0.000165, 72.7s
2023-09-04 18:19:21 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [31/50] train_loss: 0.0030, val_loss: 0.0053, val_r2: 0.9948, lr: 0.000157, 63.4s
2023-09-04 18:20:27 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [32/50] train_loss: 0.0026, val_loss: 0.0050, val_r2: 0.9951, lr: 0.000148, 64.5s
2023-09-04 18:21:32 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [33/50] train_loss: 0.0024, val_loss: 0.0053, val_r2: 0.9948, lr: 0.000140, 64.2s
2023-09-04 18:22:35 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [34/50] train_loss: 0.0025, val_loss: 0.0047, val_r2: 0.9954, lr: 0.000132, 63.0s
2023-09-04 18:23:42 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [35/50] train_loss: 0.0023, val_loss: 0.0047, val_r2: 0.9954, lr: 0.000124, 67.0s
2023-09-04 18:24:47 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [36/50] train_loss: 0.0022, val_loss: 0.0046, val_r2: 0.9954, lr: 0.000115, 64.3s
2023-09-04 18:25:58 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [37/50] train_loss: 0.0020, val_loss: 0.0045, val_r2: 0.9956, lr: 0.000107, 70.8s
2023-09-04 18:27:01 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [38/50] train_loss: 0.0020, val_loss: 0.0046, val_r2: 0.9955, lr: 0.000099, 62.5s
2023-09-04 18:28:05 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [39/50] train_loss: 0.0018, val_loss: 0.0046, val_r2: 0.9955, lr: 0.000091, 63.7s
2023-09-04 18:29:10 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [40/50] train_loss: 0.0018, val_loss: 0.0047, val_r2: 0.9953, lr: 0.000082, 64.7s
2023-09-04 18:30:12 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [41/50] train_loss: 0.0018, val_loss: 0.0043, val_r2: 0.9958, lr: 0.000074, 62.3s
2023-09-04 18:31:15 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [42/50] train_loss: 0.0017, val_loss: 0.0045, val_r2: 0.9956, lr: 0.000066, 62.5s
2023-09-04 18:32:19 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [43/50] train_loss: 0.0016, val_loss: 0.0043, val_r2: 0.9957, lr: 0.000058, 64.3s
2023-09-04 18:33:24 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [44/50] train_loss: 0.0016, val_loss: 0.0043, val_r2: 0.9958, lr: 0.000049, 65.1s
2023-09-04 18:34:29 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [45/50] train_loss: 0.0017, val_loss: 0.0043, val_r2: 0.9957, lr: 0.000041, 64.3s
2023-09-04 18:35:36 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [46/50] train_loss: 0.0014, val_loss: 0.0042, val_r2: 0.9958, lr: 0.000033, 67.2s
2023-09-04 18:36:47 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [47/50] train_loss: 0.0013, val_loss: 0.0044, val_r2: 0.9957, lr: 0.000025, 69.9s
2023-09-04 18:38:05 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [48/50] train_loss: 0.0013, val_loss: 0.0042, val_r2: 0.9958, lr: 0.000016, 78.2s
2023-09-04 18:39:19 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [49/50] train_loss: 0.0014, val_loss: 0.0042, val_r2: 0.9959, lr: 0.000008, 73.7s
2023-09-04 18:40:22 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [50/50] train_loss: 0.0013, val_loss: 0.0042, val_r2: 0.9959, lr: 0.000000, 62.9s
2023-09-04 18:40:23 | unimol/tasks/trainer.py | 213 | INFO | Uni-Mol(QSAR) | load model success!
2023-09-04 18:40:28 | unimol/models/nnmodel.py | 129 | INFO | Uni-Mol(QSAR) | fold 2, result {'r2': 0.9959009040119442, 'mae': 0.058838677, 'pearsonr': 0.9979535268752111, 'spearmanr': 0.9973975017714561, 'mse': 0.007119618}
2023-09-04 18:40:29 | unimol/models/unimol.py | 114 | 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
2023-09-04 18:41:33 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [1/50] train_loss: 0.1751, val_loss: 0.0821, val_r2: 0.9176, lr: 0.000267, 64.2s
2023-09-04 18:42:36 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [2/50] train_loss: 0.0520, val_loss: 0.0254, val_r2: 0.9746, lr: 0.000396, 62.4s
2023-09-04 18:43:42 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [3/50] train_loss: 0.0273, val_loss: 0.0234, val_r2: 0.9765, lr: 0.000388, 64.9s
2023-09-04 18:44:53 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [4/50] train_loss: 0.0211, val_loss: 0.0153, val_r2: 0.9847, lr: 0.000379, 71.2s
2023-09-04 18:46:04 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [5/50] train_loss: 0.0181, val_loss: 0.0128, val_r2: 0.9873, lr: 0.000371, 70.1s
2023-09-04 18:47:11 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [6/50] train_loss: 0.0152, val_loss: 0.0138, val_r2: 0.9861, lr: 0.000363, 66.3s
2023-09-04 18:48:15 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [7/50] train_loss: 0.0132, val_loss: 0.0132, val_r2: 0.9868, lr: 0.000355, 64.3s
2023-09-04 18:49:19 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [8/50] train_loss: 0.0130, val_loss: 0.0111, val_r2: 0.9889, lr: 0.000346, 64.0s
2023-09-04 18:50:27 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [9/50] train_loss: 0.0123, val_loss: 0.0120, val_r2: 0.9881, lr: 0.000338, 66.6s
2023-09-04 18:51:32 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [10/50] train_loss: 0.0108, val_loss: 0.0095, val_r2: 0.9904, lr: 0.000330, 65.3s
2023-09-04 18:52:35 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [11/50] train_loss: 0.0097, val_loss: 0.0084, val_r2: 0.9916, lr: 0.000322, 62.6s
2023-09-04 18:53:38 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [12/50] train_loss: 0.0087, val_loss: 0.0084, val_r2: 0.9916, lr: 0.000313, 62.6s
2023-09-04 18:54:42 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [13/50] train_loss: 0.0086, val_loss: 0.0099, val_r2: 0.9901, lr: 0.000305, 62.7s
2023-09-04 18:55:47 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [14/50] train_loss: 0.0076, val_loss: 0.0084, val_r2: 0.9916, lr: 0.000297, 65.1s
2023-09-04 18:56:52 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [15/50] train_loss: 0.0076, val_loss: 0.0079, val_r2: 0.9921, lr: 0.000289, 64.3s
2023-09-04 18:58:07 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [16/50] train_loss: 0.0068, val_loss: 0.0074, val_r2: 0.9926, lr: 0.000280, 74.5s
2023-09-04 18:59:26 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [17/50] train_loss: 0.0062, val_loss: 0.0077, val_r2: 0.9923, lr: 0.000272, 78.4s
2023-09-04 19:00:44 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [18/50] train_loss: 0.0060, val_loss: 0.0068, val_r2: 0.9931, lr: 0.000264, 78.2s
2023-09-04 19:02:03 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [19/50] train_loss: 0.0060, val_loss: 0.0065, val_r2: 0.9935, lr: 0.000256, 78.1s
2023-09-04 19:03:12 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [20/50] train_loss: 0.0056, val_loss: 0.0081, val_r2: 0.9919, lr: 0.000247, 67.9s
2023-09-04 19:04:17 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [21/50] train_loss: 0.0047, val_loss: 0.0073, val_r2: 0.9926, lr: 0.000239, 65.1s
2023-09-04 19:05:20 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [22/50] train_loss: 0.0053, val_loss: 0.0075, val_r2: 0.9924, lr: 0.000231, 63.3s
2023-09-04 19:06:23 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [23/50] train_loss: 0.0046, val_loss: 0.0060, val_r2: 0.9940, lr: 0.000223, 63.3s
2023-09-04 19:07:29 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [24/50] train_loss: 0.0040, val_loss: 0.0057, val_r2: 0.9943, lr: 0.000214, 65.0s
2023-09-04 19:08:36 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [25/50] train_loss: 0.0038, val_loss: 0.0068, val_r2: 0.9931, lr: 0.000206, 65.9s
2023-09-04 19:09:42 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [26/50] train_loss: 0.0039, val_loss: 0.0056, val_r2: 0.9944, lr: 0.000198, 66.0s
2023-09-04 19:10:49 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [27/50] train_loss: 0.0034, val_loss: 0.0055, val_r2: 0.9944, lr: 0.000190, 65.6s
2023-09-04 19:11:56 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [28/50] train_loss: 0.0035, val_loss: 0.0064, val_r2: 0.9936, lr: 0.000181, 66.5s
2023-09-04 19:12:58 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [29/50] train_loss: 0.0033, val_loss: 0.0067, val_r2: 0.9933, lr: 0.000173, 62.4s
2023-09-04 19:14:03 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [30/50] train_loss: 0.0031, val_loss: 0.0053, val_r2: 0.9947, lr: 0.000165, 64.5s
2023-09-04 19:15:07 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [31/50] train_loss: 0.0029, val_loss: 0.0048, val_r2: 0.9952, lr: 0.000157, 63.0s
2023-09-04 19:16:11 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [32/50] train_loss: 0.0027, val_loss: 0.0050, val_r2: 0.9950, lr: 0.000148, 63.7s
2023-09-04 19:17:29 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [33/50] train_loss: 0.0025, val_loss: 0.0050, val_r2: 0.9950, lr: 0.000140, 77.8s
2023-09-04 19:18:42 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [34/50] train_loss: 0.0026, val_loss: 0.0055, val_r2: 0.9945, lr: 0.000132, 72.9s
2023-09-04 19:19:55 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [35/50] train_loss: 0.0024, val_loss: 0.0048, val_r2: 0.9952, lr: 0.000124, 73.3s
2023-09-04 19:21:05 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [36/50] train_loss: 0.0023, val_loss: 0.0054, val_r2: 0.9946, lr: 0.000115, 70.1s
2023-09-04 19:22:11 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [37/50] train_loss: 0.0022, val_loss: 0.0046, val_r2: 0.9953, lr: 0.000107, 65.7s
2023-09-04 19:23:16 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [38/50] train_loss: 0.0021, val_loss: 0.0051, val_r2: 0.9949, lr: 0.000099, 65.0s
2023-09-04 19:24:25 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [39/50] train_loss: 0.0020, val_loss: 0.0047, val_r2: 0.9952, lr: 0.000091, 68.9s
2023-09-04 19:25:33 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [40/50] train_loss: 0.0021, val_loss: 0.0046, val_r2: 0.9954, lr: 0.000082, 67.2s
2023-09-04 19:26:39 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [41/50] train_loss: 0.0018, val_loss: 0.0050, val_r2: 0.9950, lr: 0.000074, 65.2s
2023-09-04 19:27:44 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [42/50] train_loss: 0.0017, val_loss: 0.0046, val_r2: 0.9953, lr: 0.000066, 65.2s
2023-09-04 19:28:50 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [43/50] train_loss: 0.0016, val_loss: 0.0043, val_r2: 0.9957, lr: 0.000058, 66.1s
2023-09-04 19:29:54 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [44/50] train_loss: 0.0016, val_loss: 0.0043, val_r2: 0.9957, lr: 0.000049, 63.2s
2023-09-04 19:31:00 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [45/50] train_loss: 0.0015, val_loss: 0.0044, val_r2: 0.9956, lr: 0.000041, 66.3s
2023-09-04 19:32:06 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [46/50] train_loss: 0.0016, val_loss: 0.0047, val_r2: 0.9953, lr: 0.000033, 65.5s
2023-09-04 19:33:12 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [47/50] train_loss: 0.0014, val_loss: 0.0047, val_r2: 0.9953, lr: 0.000025, 66.2s
2023-09-04 19:34:19 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [48/50] train_loss: 0.0013, val_loss: 0.0045, val_r2: 0.9954, lr: 0.000016, 66.8s
2023-09-04 19:35:24 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [49/50] train_loss: 0.0013, val_loss: 0.0042, val_r2: 0.9958, lr: 0.000008, 65.2s
2023-09-04 19:36:28 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [50/50] train_loss: 0.0013, val_loss: 0.0043, val_r2: 0.9957, lr: 0.000000, 64.0s
2023-09-04 19:36:30 | unimol/tasks/trainer.py | 213 | INFO | Uni-Mol(QSAR) | load model success!
2023-09-04 19:36:35 | unimol/models/nnmodel.py | 129 | INFO | Uni-Mol(QSAR) | fold 3, result {'r2': 0.9957527174453936, 'mae': 0.059466276, 'pearsonr': 0.9979272804072913, 'spearmanr': 0.9974385157147599, 'mse': 0.007279518}
2023-09-04 19:36:35 | unimol/models/unimol.py | 114 | 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
2023-09-04 19:37:41 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [1/50] train_loss: 0.1902, val_loss: 0.1165, val_r2: 0.8797, lr: 0.000267, 65.1s
2023-09-04 19:38:48 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [2/50] train_loss: 0.0543, val_loss: 0.0351, val_r2: 0.9634, lr: 0.000396, 66.5s
2023-09-04 19:39:52 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [3/50] train_loss: 0.0259, val_loss: 0.0187, val_r2: 0.9806, lr: 0.000388, 63.4s
2023-09-04 19:40:57 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [4/50] train_loss: 0.0194, val_loss: 0.0160, val_r2: 0.9834, lr: 0.000379, 63.8s
2023-09-04 19:42:02 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [5/50] train_loss: 0.0176, val_loss: 0.0225, val_r2: 0.9766, lr: 0.000371, 64.2s
2023-09-04 19:43:07 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [6/50] train_loss: 0.0151, val_loss: 0.0120, val_r2: 0.9876, lr: 0.000363, 65.4s
2023-09-04 19:44:19 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [7/50] train_loss: 0.0133, val_loss: 0.0108, val_r2: 0.9888, lr: 0.000355, 70.9s
2023-09-04 19:45:24 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [8/50] train_loss: 0.0129, val_loss: 0.0108, val_r2: 0.9888, lr: 0.000346, 65.0s
2023-09-04 19:46:31 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [9/50] train_loss: 0.0118, val_loss: 0.0100, val_r2: 0.9896, lr: 0.000338, 66.4s
2023-09-04 19:47:39 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [10/50] train_loss: 0.0100, val_loss: 0.0093, val_r2: 0.9904, lr: 0.000330, 67.6s
2023-09-04 19:48:43 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [11/50] train_loss: 0.0102, val_loss: 0.0106, val_r2: 0.9890, lr: 0.000322, 62.4s
2023-09-04 19:49:46 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [12/50] train_loss: 0.0094, val_loss: 0.0090, val_r2: 0.9907, lr: 0.000313, 62.7s
2023-09-04 19:50:53 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [13/50] train_loss: 0.0081, val_loss: 0.0093, val_r2: 0.9904, lr: 0.000305, 66.8s
2023-09-04 19:51:57 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [14/50] train_loss: 0.0079, val_loss: 0.0080, val_r2: 0.9917, lr: 0.000297, 64.3s
2023-09-04 19:53:04 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [15/50] train_loss: 0.0076, val_loss: 0.0078, val_r2: 0.9919, lr: 0.000289, 65.7s
2023-09-04 19:54:10 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [16/50] train_loss: 0.0068, val_loss: 0.0080, val_r2: 0.9918, lr: 0.000280, 65.2s
2023-09-04 19:55:15 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [17/50] train_loss: 0.0063, val_loss: 0.0078, val_r2: 0.9919, lr: 0.000272, 65.4s
2023-09-04 19:56:19 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [18/50] train_loss: 0.0067, val_loss: 0.0099, val_r2: 0.9898, lr: 0.000264, 62.9s
2023-09-04 19:57:21 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [19/50] train_loss: 0.0055, val_loss: 0.0080, val_r2: 0.9917, lr: 0.000256, 62.7s
2023-09-04 19:58:25 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [20/50] train_loss: 0.0055, val_loss: 0.0064, val_r2: 0.9934, lr: 0.000247, 64.2s
2023-09-04 20:02:46 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [24/50] train_loss: 0.0042, val_loss: 0.0056, val_r2: 0.9941, lr: 0.000214, 65.8s
2023-09-04 20:03:52 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [25/50] train_loss: 0.0040, val_loss: 0.0062, val_r2: 0.9936, lr: 0.000206, 65.2s
val:  14%|█▍        | 22/159 [00:00<00:04, 30.91it/s, Epoch=Epoch 26/50, loss=0.0053]              IOPub message rate exceeded.
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2023-09-04 20:07:06 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [28/50] train_loss: 0.0035, val_loss: 0.0053, val_r2: 0.9945, lr: 0.000181, 63.6s
2023-09-04 20:08:09 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [29/50] train_loss: 0.0034, val_loss: 0.0057, val_r2: 0.9941, lr: 0.000173, 62.9s
2023-09-04 20:09:18 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [30/50] train_loss: 0.0031, val_loss: 0.0051, val_r2: 0.9947, lr: 0.000165, 68.4s
2023-09-04 20:10:31 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [31/50] train_loss: 0.0030, val_loss: 0.0049, val_r2: 0.9950, lr: 0.000157, 72.6s
2023-09-04 20:11:42 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [32/50] train_loss: 0.0027, val_loss: 0.0057, val_r2: 0.9941, lr: 0.000148, 70.5s
2023-09-04 20:12:50 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [33/50] train_loss: 0.0032, val_loss: 0.0055, val_r2: 0.9943, lr: 0.000140, 67.8s
2023-09-04 20:13:53 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [34/50] train_loss: 0.0024, val_loss: 0.0048, val_r2: 0.9950, lr: 0.000132, 63.0s
2023-09-04 20:14:58 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [35/50] train_loss: 0.0024, val_loss: 0.0045, val_r2: 0.9953, lr: 0.000124, 64.7s
2023-09-04 20:16:03 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [36/50] train_loss: 0.0023, val_loss: 0.0054, val_r2: 0.9944, lr: 0.000115, 64.3s
2023-09-04 20:17:09 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [37/50] train_loss: 0.0021, val_loss: 0.0045, val_r2: 0.9953, lr: 0.000107, 65.6s
2023-09-04 20:18:15 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [38/50] train_loss: 0.0021, val_loss: 0.0047, val_r2: 0.9951, lr: 0.000099, 66.3s
2023-09-04 20:19:19 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [39/50] train_loss: 0.0021, val_loss: 0.0046, val_r2: 0.9952, lr: 0.000091, 63.6s
2023-09-04 20:20:23 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [40/50] train_loss: 0.0019, val_loss: 0.0051, val_r2: 0.9947, lr: 0.000082, 64.6s
2023-09-04 20:21:36 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [41/50] train_loss: 0.0018, val_loss: 0.0044, val_r2: 0.9955, lr: 0.000074, 72.3s
2023-09-04 20:22:41 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [42/50] train_loss: 0.0017, val_loss: 0.0042, val_r2: 0.9956, lr: 0.000066, 64.7s
2023-09-04 20:23:45 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [43/50] train_loss: 0.0016, val_loss: 0.0045, val_r2: 0.9953, lr: 0.000058, 63.4s
2023-09-04 20:24:49 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [44/50] train_loss: 0.0016, val_loss: 0.0043, val_r2: 0.9955, lr: 0.000049, 64.0s
2023-09-04 20:25:55 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [45/50] train_loss: 0.0016, val_loss: 0.0041, val_r2: 0.9957, lr: 0.000041, 65.8s
2023-09-04 20:27:01 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [46/50] train_loss: 0.0015, val_loss: 0.0042, val_r2: 0.9957, lr: 0.000033, 65.3s
2023-09-04 20:28:04 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [47/50] train_loss: 0.0014, val_loss: 0.0046, val_r2: 0.9953, lr: 0.000025, 63.3s
2023-09-04 20:29:07 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [48/50] train_loss: 0.0014, val_loss: 0.0042, val_r2: 0.9957, lr: 0.000016, 62.8s
2023-09-04 20:30:10 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [49/50] train_loss: 0.0013, val_loss: 0.0042, val_r2: 0.9956, lr: 0.000008, 63.1s
2023-09-04 20:31:21 | unimol/tasks/trainer.py | 169 | INFO | Uni-Mol(QSAR) | Epoch [50/50] train_loss: 0.0013, val_loss: 0.0041, val_r2: 0.9957, lr: 0.000000, 71.6s
2023-09-04 20:31:24 | unimol/tasks/trainer.py | 213 | INFO | Uni-Mol(QSAR) | load model success!
2023-09-04 20:31:29 | unimol/models/nnmodel.py | 129 | INFO | Uni-Mol(QSAR) | fold 4, result {'r2': 0.995722167871576, 'mae': 0.058697157, 'pearsonr': 0.997918921152862, 'spearmanr': 0.9975153439068033, 'mse': 0.007057018}
2023-09-04 20:31:29 | unimol/models/nnmodel.py | 144 | INFO | Uni-Mol(QSAR) | Uni-Mol metrics score: 
{'r2': 0.9958098504114381, 'mae': 0.05926784082913989, 'pearsonr': 0.9979403073106544, 'spearmanr': 0.997427024789587, 'mse': 0.0071642844934711545}
2023-09-04 20:31:29 | unimol/models/nnmodel.py | 145 | INFO | Uni-Mol(QSAR) | Uni-Mol & Metric result saved!

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Step3:调参（可跳过）

按照bbbp的教程调参（链接：https://nb.bohrium.dp.tech/detail/1511053944）

最优超参为Lr=0.0004，Batch size=32。

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[ ]

lr_ft = [1e-5,1e-4,4e-4,1e-3] # 学习率(们)

for i in range(len(lr_ft)): # 循环输入每个学习率

clf = MolTrain(task='regression',

data_type='molecule',

epochs=60,

batch_size=16,

early_stopping=5,

metrics='r2',

split='random',

save_path='./exp/learning_rate_'+str(lr_ft[i]),

learning_rate= lr_ft[i]

)

clf.fit("./datasets/RedDB_train.csv") # 训练模型

# Load the RedDB_train dataset

df_train = pd.read_csv("/bohr/aorfb-7iz1/v2/RedDB_train.csv")

# Convert the clf.data dict to a DataFrame

clf_data_df = pd.DataFrame({'pred':clf.cv_pred.flatten(),

'SMILES':clf.data['smiles']

})

# Join the datasets on the 'smiles' column

merged_df = pd.merge(df_train, clf_data_df, on='SMILES', how='inner')

# Save the merged data to a csv file, the name of the file will be learning rate + 'RedDB_train_with_predictions.csv'

merged_df.to_csv('./datasets/lr_'+str(lr_ft[i])+'_RedDB_train_with_predictions.csv', index=False)

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[ ]

# 获取最低 R2 值对应的学习率

min_r2_lr = lr_ft[r2_ft.index(min(r2_ft))]

bs_ft = [8,16,32,64] # 学习率(们)

for i in range(len(lr_ft)): # 循环输入每个学习率

clf = MolTrain(task='regression',

data_type='molecule',

epochs=60,

batch_size=bs_ft[i],

early_stopping=5,

metrics='r2',

split='random',

save_path='./batch_size'+str(bs_ft[i]),

learning_rate= min_r2_lr # 使用最低 R2 值对应的学习率

)

clf.fit('./RedDB_train.csv') # 训练模型

# Load the RedDB_train dataset

df_train = pd.read_csv("/bohr/aorfb-7iz1/v2/RedDB_train.csv")

# Convert the clf.data dict to a DataFrame

clf_data_df = pd.DataFrame({'pred':clf.cv_pred.flatten(),

'SMILES':clf.data['smiles']

})

# Join the datasets on the 'smiles' column

merged_df = pd.merge(df_train, clf_data_df, on='SMILES', how='inner')

# Save the merged data to a csv file, the name of the file will be learning rate + 'RedDB_train_with_predictions.csv'

merged_df.to_csv('./bs_'+str(bs_ft[i])+'_RedDB_train_with_predictions.csv', index=False)

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Step4:读入测试集用于预测溶解度

测试集的数据类型与训练集一致。最佳的训练权重已经提前保存到/bohr/aorfb-7iz1/v2/best_exp中，可以直接运行以下代码。

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[2]

clf_ll = MolPredict(load_model='/bohr/aorfb-7iz1/v2/best_exp')

test_pred = clf_ll.predict('/bohr/aorfb-7iz1/v2/RedDB_test.csv')

2023-09-07 13:43:56 | unimol/data/conformer.py | 62 | INFO | Uni-Mol(QSAR) | Start generating conformers...
6324it [00:36, 174.39it/s]
2023-09-07 13:44:32 | unimol/data/conformer.py | 66 | INFO | Uni-Mol(QSAR) | Failed to generate conformers for 0.00% of molecules.
2023-09-07 13:44:32 | unimol/data/conformer.py | 68 | INFO | Uni-Mol(QSAR) | Failed to generate 3d conformers for 0.03% of molecules.
2023-09-07 13:44:33 | unimol/models/unimol.py | 114 | 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
2023-09-07 13:44:34 | unimol/models/nnmodel.py | 154 | INFO | Uni-Mol(QSAR) | start predict NNModel:unimolv1
2023-09-07 13:44:37 | unimol/tasks/trainer.py | 213 | INFO | Uni-Mol(QSAR) | load model success!
2023-09-07 13:44:54 | unimol/tasks/trainer.py | 213 | INFO | Uni-Mol(QSAR) | load model success!
2023-09-07 13:45:08 | unimol/tasks/trainer.py | 213 | INFO | Uni-Mol(QSAR) | load model success!
2023-09-07 13:45:23 | unimol/tasks/trainer.py | 213 | INFO | Uni-Mol(QSAR) | load model success!
2023-09-07 13:45:37 | unimol/tasks/trainer.py | 213 | INFO | Uni-Mol(QSAR) | load model success!
2023-09-07 13:45:51 | unimol/predict.py | 66 | INFO | Uni-Mol(QSAR) | final predict metrics score: 
{'r2': 0.996431347888573, 'mae': 0.05341669959421024, 'pearsonr': 0.9982373718024908, 'spearmanr': 0.99800209666078, 'mse': 0.006212046942459196}

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Step5:保存训练及测试数据为CSV文件（可跳过）

可以直接采用/bohr/aorfb-7iz1/v2/RedDB_test_with_predictions.csv和/bohr/aorfb-7iz1/v2/RedDB_train_with_predictions.csv

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[3]

import pandas as pd

import numpy as np

# 加载数据集

df = pd.read_csv('/bohr/aorfb-7iz1/v2/RedDB_test.csv')

# 将 test_pred 数组添加为数据框的新列

df['test_pred'] = test_pred

# 将更新后的数据框保存为新的 CSV 文件

df.to_csv('./RedDB_test_with_predictions.csv', index=False)

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[6]

# 将训练集预测结果与原TARGET值保存为csv文件

# 加载 RedDB_train 数据集

df_train = pd.read_csv("/bohr/aorfb-7iz1/v2/RedDB_train.csv")

# 将 clf.data 字典转化为 DataFrame

clf_data_df = pd.DataFrame({'pred':clf.cv_pred.flatten(),

'SMILES':clf.data['smiles']

})

# 通过 'smiles' 列合并数据集

merged_df = pd.merge(df_train, clf_data_df, on='SMILES', how='inner')

# 将合并后的数据框保存为新的 CSV 文件

merged_df.to_csv('./RedDB_train_with_predictions.csv', index=False)

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Step6:可视化

通过画出实验值和预测值来可视化我们的模型训练结果,比对测试集的实验值和预测值,

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[3]

import pandas as pd

import matplotlib.pyplot as plt

# 从CSV文件中读取数据

# train_data = pd.read_csv("./RedDB_train_with_predictions.csv")

# test_data = pd.read_csv("./RedDB_test_with_predictions.csv")

train_data = pd.read_csv("/bohr/aorfb-7iz1/v2/RedDB_train_with_predictions.csv")

test_data = pd.read_csv("/bohr/aorfb-7iz1/v2/RedDB_test_with_predictions.csv")

# 提取需要的列

y_true_train = train_data['TARGET']

y_predict_train = train_data['pred']

y_true_test = test_data['TARGET']

y_predict_test = test_data['test_pred']

# 创建散点图

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

# train数据集的散点图

plt.scatter(y_true_train, y_predict_train, color='orange', label='Train Set')

# test数据集的散点图

plt.scatter(y_true_test, y_predict_test, color='blue', label='Test Set')

# 设置图例

plt.legend()

plt.xlabel('Actual Target Values of Electroactive Molecules')

plt.ylabel('Predicted Target Values of Electroactive Molecules')

plt.title('Actual vs Predicted Target Values of Electroactive Molecules')

# 设置x和y坐标范围

plt.xlim(-7.5, 7.5)

plt.ylim(-7.5, 7.5)

# 添加y=x的虚线

plt.plot([-7.5, 7.5], [-7.5, 7.5], 'k--')

plt.show()

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Step7.基于Uni-Mol的原子向量表征可视化

原理：以下代码的目标是利用UniMolRepr中表示原子信息的高维向量，计算这个向量的L2范数能得到一个单一的数值，这个数值在某种程度上综合了这个原子的所有属性信息。L2范数是一个函数，可以从向量空间中的向量映射到非负实数，用来度量向量的长度或大小，公式如下：

$∣∣ x ∣ ∣_{2} = x 1^{2} + x 2^{2} + ... + x n^{2}$

然后，我们进一步利用rdkit中rdMolDraw2D将每个原子权重映射为颜色。

环境配置：注意，镜像首先需要完善环境配置：利用https://www.jianshu.com/p/3152bd92a1b8 和 https://blog.csdn.net/weixin_42696356/article/details/104769746 两篇文章的方法，依次在命令行运行：

sudo apt-get update

sudo apt-get install libxrender-dev 

apt install libxext6

代码方可顺利运行。

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[6]

!sudo apt-get update

!sudo apt-get install libxrender-dev -y

!apt install libxext6

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[7]

# 1)导入必要的库

import torch as th

from unimol import UniMolRepr

import pandas as pd

from rdkit import Chem

from rdkit.Chem import Draw,rdMolDescriptors

from rdkit.Chem.Draw import SimilarityMaps,rdMolDraw2D

from matplotlib import cm

import matplotlib.pyplot as plt

import numpy as np

import os

import io

import torch.nn as nn

from PIL import Image as PILImage

from matplotlib.colorbar import ColorbarBase

from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas

from matplotlib.figure import Figure

# 2）定义一个画原子的函数，数据路径作为输入

def draw_atom(data_path):

# 读取CSV数据，取出SMILES列的值

data = pd.read_csv(data_path,header=0)

smiles = data.SMILES.values.tolist()

# 实例化UniMolRepr对象

clf = UniMolRepr(data_type='molecule')

# 获取SMILES的分子表示

reprs = clf.get_repr(smiles)

# 获取原子表示

atomic_reprs = reprs['atomic_reprs']

# 计算每个原子表示的范数作为权重

atomic_weights = [th.norm(th.tensor(x), dim=1) for x in reprs["atomic_reprs"]]

# 将SMILES转换为分子对象

mols = [Chem.MolFromSmiles(smi) for smi in smiles]

# 创建一个Softmax对象

soft = nn.Softmax()

# 对于每个分子，按照原子权重绘制彩色的分子图像

for i, mol in enumerate(mols):

# 获取原子的权重，并将它们转换为0-1范围的值，以便后续转换为颜色

weights = atomic_weights[i]

weights = soft(weights).numpy().tolist()

# 创建一个原子到颜色的映射，*10000是为了所有的颜色都集中在plasma颜色映射的某一小部分范围内，造成颜色的差异不大，难以分辨；

# 通过乘以一个较大的缩放因子，可以把这些权重值分散到更大的范围内，从而使得颜色的差异更加明显。

atom_colors = {j: cm.plasma(weights[j]*10000) for j in range(mol.GetNumAtoms())}

# 使用rdkit库的函数绘制分子图像

d = rdMolDraw2D.MolDraw2DCairo(500, 500)

rdMolDraw2D.PrepareAndDrawMolecule(d, mol, highlightAtoms=range(mol.GetNumAtoms()),

highlightAtomColors=atom_colors)

d.FinishDrawing()

# 将绘制的图像转换为PNG数据

png_data = d.GetDrawingText()

stream = io.BytesIO(png_data)

# 使用PIL库打开图像数据并保存

image = PILImage.open(stream)

image.save(f'molecule_{i}.png')

# 生成并保存颜色条图像

fig, ax = plt.subplots(figsize=(0.15, 3.5))

plt.subplots_adjust(left=0.5, right=2)

norm = plt.Normalize(vmin=0, vmax=1)

cmap = cm.plasma

cb = ColorbarBase(ax, cmap=cmap, norm=norm, orientation='vertical')

cb.set_ticks([0, 1])

cb.set_ticklabels(['Low', 'High'])

for label in cb.ax.get_yticklabels():

label.set_family('sans-serif')

label.set_fontsize(15)

plt.savefig(f'colorbar_{i}.png', bbox_inches='tight')

plt.close(fig)

# 打开分子图像和颜色条图像

mol_image = PILImage.open(f'molecule_{i}.png')

cb_image = PILImage.open(f'colorbar_{i}.png')

# 裁剪分子图像以移除顶部的空白

bbox = mol_image.getbbox()

mol_image_cropped = mol_image.crop(bbox)

# 将裁剪后的分子图像和颜色条合并

combined = PILImage.new('RGBA', (mol_image_cropped.width + cb_image.width, max(mol_image_cropped.height, cb_image.height)))

combined.paste(mol_image_cropped, (0, 0))

combined.paste(cb_image, (mol_image_cropped.width, (mol_image_cropped.height - cb_image.height) // 2))

# 保存合并后的图像

combined.save(f'combined_{i}.png')

# 显示合并后的图像

display(combined)

# 在主函数中运行画原子的函数

if __name__ == "__main__":

data_path = "/bohr/aorfb-7iz1/v2/RedDB_small.csv"

draw_atom(data_path)

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Step8:结果与讨论

从以上结果分析看到，Uni-Mol拟合效果良好，R²在0.996以上，说明Uni-Mol能够通过smiles文本数据较好地预测有机电活性分子的溶解度。进一步结合可视化分子图对官能团的种类、键合的位置和数量对原子权重的影响进行分析。发现Uni-Mol对于亲水性官能团如—OH、—NH2、—COOH 和 —SO3H等赋予的权重值相对较低。更为有趣的是，即便是相同类型的官能团，其在不同键合位置上的权重值也会有所不同。

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参考文献

[1]Fei Ai, Zengyue Wang, Nien-Chu Lai,, et al. Heteropoly acid negolytes for high-power-density aqueous redox flow batteries at low temperatures, 2022, Nature Energy

[2]Hu B, DeBruler C, Rhodes Z, et al. Long-Cycling Aqueous Organic Redox Flow Battery (AORFB) toward Sustainable and Safe Energy Storage. 2017, Journal of the American Chemical Society.

[3]Sorkun, E., Zhang, Q., Khetan, A. et al. RedDB, a computational database of electroactive molecules for aqueous redox flow batteries. Sci Data 9, 718 (2022).

[4]Sorkun, M. C., Koelman, J. V. A. & Er, S. Pushing the limits of solubility prediction via quality-oriented data selection. iScience 24, 101961 (2021).

[5]Sorkun, M. C., Khetan, A. & Er, S. Aqsoldb, a curated reference set of aqueous solubility and 2d descriptors for a diverse set of compounds. Scientific data 6, 1–8 (2019).

[6]Huuskonen, J. Estimation of aqueous solubility for a diverse set of organic compounds based on molecular topology. Journal of Chemical Information and Computer Sciences 40, 773–777 (2000).

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