An HHO-Optimized LSTM Framework for Predicting Adverse Effects Associated with Reproductive and Breast Disorders

ANGEL METANOSA AFINDA, IGA NARENDRA PRAMAWIJAYA, FAUZAN FIRDAUS

Abstract


Reproductive toxicity prediction is a major challenge in drug development as side effects are often difficult to detect early. SMILES representations provide a compact sequential format suitable for deep learning. This study proposes an HHO-optimized LSTM model to predict reproductive and breast-related side effects. Four architectural schemes were evaluated including L (LSTM only), CL (Convolution + LSTM), LD (LSTM + Dense), and CLD (Convolution + LSTM + Dense). Results show that the tuned L scheme achieved the best performance with accuracy increasing from 0.6304 to 0.6739 and F1-score from 0.6792 to 0.7097. These findings highlight the effectiveness of metaheuristic optimization in computational toxicology modeling.

Keywords


reproductive system; SMILES; LSTM; Harris Hawks Optimization; side effects prediction

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References


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DOI: https://doi.org/10.26760/elkomika.v14i3.358

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ISSN (print) : 2338-8323 | ISSN (electronic) : 2459-9638

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