# Machine Learning Approach for Predicting Drug-Like Molecules Targeting Calmodulin Pathway Proteins

## TL;DR

This paper introduces a machine learning model to predict drug efficacy for calmodulin-related diseases, validated with new riluzole derivatives.

## Contribution

A novel machine learning framework (IFPTML-XGB) is proposed for accurate prediction of drug efficacy targeting calmodulin pathway proteins.

## Key findings

- The IFPTML-XGB model achieved 89.1% test accuracy and 89.0% sensitivity in predicting drug efficacy.
- The model successfully predicted the bioactivity of novel riluzole derivatives confirmed by experimental and computational studies.

## Abstract

Recently, numerous models have been developed to predict
drug interactions
with molecules. However, integrating diverse data sources and improving
the accuracy of biological activity predictions remains a challenge.
This work proposes a novel solution that addresses these limitations.
Here, we have developed a machine learning model to predict the efficacy
of different assays and drugs for diseases related to calmodulin.
To achieve this, we have compiled a comprehensive data set including
commercialized drugs and experimental compounds targeting CaM complexes.
The IFPTML-XGB model achieved high predictive performance, with a
test accuracy of 89.1% and a sensitivity of 89.0%, demonstrating its
robustness for assay efficacy prediction. We have used the IFPTML
modeling technique to identify key factors influencing these activities.
We have also synthesized novel riluzole derivatives and have tested
them both experimentally and computationally. Biological assays and
molecular docking studies have been performed to provide a molecular-scale
picture of the molecule–CaM interaction. To validate the model’s
utility, we tested it on these derivatives. We have found that the
model correctly predicts which derivatives were the most bioactive,
indicating that this framework can be used to identify promising candidates
for new drug formulations. This research not only improves our understanding
of CaM-related diseases, but also provides an effective framework
for developing new treatments based on predictive modeling.