期刊論文
| 學年 | 109 |
|---|---|
| 學期 | 2 |
| 出版（發表）日期 | 2021-07-04 |
| 作品名稱 | Deep learning for predictions of hydrolysis rates and conditional molecular design of esters |
| 作品名稱（其他語言） | |
| 著者 | Po-Hao Chiua; Yan-LinYang; Heng-KwongTsao; Yu-Jane Sheng |
| 單位 | |
| 出版者 | |
| 著錄名稱、卷期、頁數 | Journal of the Taiwan Institute of Chemical Engineers |
| 摘要 | Background The hydrolysis rate of an ester is essential for the choice of materials in sustainable and eco-friendly applications. Methods In this work, the autoencoder (AE) model has been constructed to predict the hydrolysis rate by inputting SMILES and partial charges. Moreover, the conditional autoencoder (CAE) model has been developed to design chemical structures of esters that possess hydrolysis rates close to the desired value. Significant Findings By implementing the SMILES enumeration technique and the attention mechanism, our AE model exhibits significantly better performance than SPARC based on the root mean square error. For six biodegradable esters that have no experimental rate constants, the predictions of our AE model are in agreement with those based on the activation energies calculated from Dmol3. To design an ester satisfying the desired conditions, our CAE model demonstrates its capability of providing the best candidates of esters and their rate constants based on structural similarity and the least difference of hydrolysis rates. The derived structures are similar to the desired structure and their rate constants are close to the targeted value. |
| 關鍵字 | Deep learning;Biodegradable esters;Hydrolysis rates;Conditional molecular design;SMILES enumeration technique |
| 語言 | en |
| ISSN | |
| 期刊性質 | 國內 |
| 收錄於 | SCI |
| 產學合作 | |
| 通訊作者 | |
| 審稿制度 | 是 |
| 國別 | TWN |
| 公開徵稿 | |
| 出版型式 | ,電子版 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/121010 ) |