1 research outputs found
Comparative Study of Machine Learning-Based QSAR Modeling of Anti-inflammatory Compounds from Durian Extraction
Quantitative structure–activity
relationship (QSAR) analysis,
an in silico methodology, offers enhanced efficiency
and cost effectiveness in investigating anti-inflammatory activity.
In this study, a comprehensive comparative analysis employing four
machine learning algorithms (random forest (RF), gradient boosting
regression (GBR), support vector regression (SVR), and artificial
neural networks (ANNs)) was conducted to elucidate the activities
of naturally derived compounds from durian extraction. The analysis
was grounded in the exploration of structural attributes encompassing
steric and electrostatic properties. Notably, the nonlinear SVR model,
utilizing five key features, exhibited superior performance compared
to the other models. It demonstrated exceptional predictive accuracy
for both the training and external test datasets, yielding R2 values of 0.907 and 0.812, respectively; in
addition, their RMSE resulted in 0.123 and 0.097, respectively. The
study outcomes underscore the significance of specific structural
factors (denoted as shadow ratio, dipole z, methyl,
ellipsoidal volume, and methoxy) in determining anti-inflammatory
efficacy. Thus, the findings highlight the potential of molecular
simulations and machine learning as alternative avenues for the rational
design of novel anti-inflammatory agents