Deep learning for predictions of hydrolysis rates and conditional molecular design of esters

doi:10.1016/j.jtice.2021.06.045

機構典藏 > College of Engineering > Graduate Institute & Department of Chemical and Materials Engineering > Journal Article > Item 987654321/121010

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/121010

Title:	Deep learning for predictions of hydrolysis rates and conditional molecular design of esters
Authors:	Chiua, Po-Hao;Yan-LinYang;Heng-KwongTsao;Sheng, Yu-Jane
Keywords:	Deep learning;Biodegradable esters;Hydrolysis rates;Conditional molecular design;SMILES enumeration technique
Date:	2021-07-04
Issue Date:	2021-08-24 12:13:21 (UTC+8)
Abstract:	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.
Relation:	Journal of the Taiwan Institute of Chemical Engineers
DOI:	10.1016/j.jtice.2021.06.045
Appears in Collections:	[Graduate Institute & Department of Chemical and Materials Engineering] Journal Article

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	122	View/Open

Loading...