Machine Learning-based Analysis of Electronic Properties as Predictors of Anticholinesterase Activity in Chalcone Derivatives
In this study, we investigated the correlation between the electronic properties of anticholinesterase compounds and their biological activity. While the methodology of such correlation is well-established and has been effectively utilized in previous studies, we employed a more sophisticated approa...
Saved in:
Main Authors | , , , , |
---|---|
Format | Journal Article |
Language | English |
Published |
13.09.2023
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | In this study, we investigated the correlation between the electronic
properties of anticholinesterase compounds and their biological activity. While
the methodology of such correlation is well-established and has been
effectively utilized in previous studies, we employed a more sophisticated
approach: machine learning. Initially, we focused on a set of $22$ molecules
sharing a common chalcone skeleton and categorized them into two groups based
on their IC50 indices: active and inactive. Utilizing the open-source software
Orca, we conducted calculations to determine the geometries and electronic
structures of these molecules. Over a hundred parameters were collected from
these calculations, serving as the foundation for the features used in machine
learning. These parameters included the Mulliken and Lowdin electronic
populations of each atom within the skeleton, molecular orbital energies, and
Mayer's free valences. Through our analysis, we developed numerous models and
identified several successful candidates for effectively distinguishing between
the two groups. Notably, the most informative descriptor for this separation
relied solely on electronic populations and orbital energies. By understanding
which computationally calculated properties are most relevant to specific
biological activities, we can significantly enhance the efficiency of drug
development processes, saving both time and resources. |
---|---|
DOI: | 10.48550/arxiv.2309.07312 |