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The Melting Point Profile of Organic Molecules: A Chemoinformatic Approach

The Melting Point Profile of Organic Molecules: A Chemoinformatic Approach The combination of the generical molecular maps of atom‐level properties (MOLMAPs) encoding approach and the Random Forest algorithm (RF) is applied in order to model, predict, and interpret the structural motifs responsible for a certain organic molecule's melting point (mp) profile. A high‐quality database is used for model build‐up and evaluation of predictive ability. The obtained results for the complete independent test set (R2 = 0.811, MAE = 31.99 K, RMS = 43.98 K) are comparable or better than reference works. The form of codification represents implicitly the structure of a given molecule and highlights the interactions responsible for a certain melting point profile. This generical encoding approach groups different structural motifs based on its calculated atomic‐based properties leading to good predictive ability for structurally different chemical systems not contained in the training set. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Advanced Theory and Simulations Wiley

The Melting Point Profile of Organic Molecules: A Chemoinformatic Approach

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References (31)

Publisher
Wiley
Copyright
© 2022 Wiley‐VCH GmbH
eISSN
2513-0390
DOI
10.1002/adts.202200503
Publisher site
See Article on Publisher Site

Abstract

The combination of the generical molecular maps of atom‐level properties (MOLMAPs) encoding approach and the Random Forest algorithm (RF) is applied in order to model, predict, and interpret the structural motifs responsible for a certain organic molecule's melting point (mp) profile. A high‐quality database is used for model build‐up and evaluation of predictive ability. The obtained results for the complete independent test set (R2 = 0.811, MAE = 31.99 K, RMS = 43.98 K) are comparable or better than reference works. The form of codification represents implicitly the structure of a given molecule and highlights the interactions responsible for a certain melting point profile. This generical encoding approach groups different structural motifs based on its calculated atomic‐based properties leading to good predictive ability for structurally different chemical systems not contained in the training set.

Journal

Advanced Theory and SimulationsWiley

Published: Nov 1, 2022

Keywords: chemoinformatics; codification; kohonen neural‐networks; melting points; organic molecules; qspr; random forests

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