Deep learning for predictions of hydrolysis rates and conditional molecular design of esters
Journal
Journal of the Taiwan Institute of Chemical Engineers
Journal Volume
126
Pages
1-13
Date Issued
2021
Author(s)
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. ? 2021 Taiwan Institute of Chemical Engineers
Subjects
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
Type
journal article