Full
Outline
To build a more stable, reliable and practical model for the probability prediction of a lead compound becoming a drug based on the deep learning method.
The positive and negative sample data sets were collected from Iintegrity, Chembl and Drugbank databases firstly. After cleaning the large data set of positive and negative samples and solving the problem of data imbalance, the compounds' SMILES were further encoded. Then, Stacked AutoEncoder (SAE) and Fully Connected Neural Network (FCNN) were used to construct and train the deep neural network model to extract the features of the compounds and predict the probability of a lead compound becoming a drug.
The model finally converged stably, the ACC value and AUC value reached 0.995 3 and 0.992 9 respectively on the validation set, which improved the prediction accuracy by about 3% compared with the previously reported model based on machine learning.
The prediction model based on large data set and deep neural network technology has certain practicability, and can improve the accuracy of the probability prediction of a lead compound becoming a drug.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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