https://scholars.lib.ntu.edu.tw/handle/123456789/598297
標題: | Deep learning for predictions of hydrolysis rates and conditional molecular design of esters | 作者: | Chiu P.-H Yang Y.-L Tsao H.-K Sheng Y.-J. YU-JANE SHENG |
關鍵字: | Biodegradable esters;Conditional molecular design;Deep learning;Hydrolysis rates;SMILES enumeration technique;Activation energy;Esters;Forecasting;Hydrolysis;Learning systems;Mean square error;Rate constants;Attention mechanisms;Desired conditions;Enumeration techniques;Hydrolysis rate;Molecular design;Partial charges;Root mean square errors;Structural similarity;Structural design | 公開日期: | 2021 | 卷: | 126 | 起(迄)頁: | 1-13 | 來源出版物: | 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. ? 2021 Taiwan Institute of Chemical Engineers |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109098126&doi=10.1016%2fj.jtice.2021.06.045&partnerID=40&md5=0bf246aa01c234c919453c8b431cf6e3 https://scholars.lib.ntu.edu.tw/handle/123456789/598297 |
ISSN: | 18761070 | DOI: | 10.1016/j.jtice.2021.06.045 |
顯示於: | 化學工程學系 |
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