Thermodynamics-Based Model Construction for the Accurate Prediction of Molecular Properties From Partition Coefficients

Abstract

Developing models for predicting molecular properties of organic compounds is imperative for drug development and environmental safety; however, development of such models that have high predictive power and are independent of the compounds used is challenging. To overcome the challenges, we used a thermodynamics-based theoretical derivation to construct models for accurately predicting molecular properties. The free energy change that determines a property equals the sum of the free energy changes (ΔGFs) caused by the factors affecting the property. By developing or selecting molecular descriptors that are directly proportional to ΔGFs, we built a general linear free energy relationship (LFER) for predicting the property with the molecular descriptors as predictive variables. The LFER can be used to construct models for predicting various specific properties from partition coefficients. Validations show that the models constructed according to the LFER have high predictive power and their performance is independent of the compounds used, including the models for the properties having little correlation with partition coefficients. The findings in this study are highly useful for applications in drug development and environmental safety.

Deep Learning Based Methods for Molecular Similarity Searching: A Systematic Review

“…One of the important properties of molecular structures that are used to determine the activity and transport of drugs is hydrophobicity, which is frequently approximated by the logarithms (log P) of the partitioning coefficients between n-octanol and water. The determination of log P values can be predicted either by experiments, which can often be difficult, or by developing various methods to predict the values of hydrophobicity, such as atom-based methods or property-based methods [31].…”

“…Due to their broad adaptability, compound structures can be used in conjunction with a variety of data comprising fingerprints, transcriptomes, and molecular characteristics [67,68]. For instance, Chen et al [31] integrated the target protein information with the PseAAC, PsePSSM, NMBroto, and structural features of the MLP with four hidden layers for drug-target interaction predictions, while the fingerprints were employed for the compound [69].…”