Assessment of molecular dynamics time series descriptors in protein-ligand affinity prediction.

17 October 2024, Version 2
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

The advancement of computational methods in drug discovery, particularly through the use of machine learning (ML) and deep learning (DL), has significantly enhanced the precision of binding affinity predictions. Despite progress in computer-aided drug discovery (CADD) accurate prediction of binding affinity remains a challenge due to the complex, non-linear character of molecular interactions. Generalizability continues to limit these models, with performance discrepancies noted between training datasets and external test conditions. This study explores the integration of molecular dynamics (MD) simulations with ML to assess its predictive performance and limitations. In particular MD simulations offer a dynamic perspective by depicting the temporal interactions within protein-ligand complexes, potentially bringing additional information for affinity and specificity estimates. By generating and analyzing over 800 unique protein-ligand MD simulations, we evaluate the utility of MD-derived descriptors based on time series in enhancing predictive accuracies. The findings suggest specific and generalizable features derived from MD data and propose approaches to augment the current in silico affinity prediction methods.

Keywords

molecular dynamics
time series
ligand binding
affinity prediction
machine learning

Supplementary materials

Title
Description
Actions
Title
Supplementary material for the Methods and Results sections.
Description
Supplementary file for the Materials and Methods, and Results sections of “Assessment of molecular dynamics time series descriptors in protein-ligand affinity prediction.”
Actions
Title
Details of the functional composition of MDD targets.
Description
Details of the functional composition of MDD targets.
Actions

Comments

Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.