Predicting Sour or Sweet: Exploring Advance DL Methods for Odor Perception Based on Molecular Properties

Abstract

In the realm of sensory perception, the olfactory system plays a pivotal role in guiding human experiences and interactions with the environment. The intricate relationship between molecular structures and olfactory sensations has long intrigued scientists, prompting a quest for accurate predictive models that decipher the multifaceted nature of odor perception. This research paper delves into the domain of odor prediction through the lens of modern machine learning (ML) and deep learning (DL) methodologies, scrutinizing their efficacy in capturing the complex interplay between molecular properties and perceived odors. Employing a meticulously curated dataset of molecular structures and corresponding odor descriptors, we embark on a comprehensive analysis of ML and DL algorithms. We propose a novel hybrid model that amalgamates the Bidirectional Gated Recurrent Unit (BI-GRU) with the robustness of the Random Forest Classifier. This synergy capitalizes on the temporal dependencies inherent in molecular sequences while harnessing the ensemble learning process of random forests to extract intricate patterns hidden within the data. Through a rigorous evaluation process, our proposed BI-GRU + Random Forest Classifier emerges as a formidable contender, showcasing superior predictive capabilities when compared to an array of benchmark models. Leveraging advanced techniques in feature engineering, hyperparameter optimization, and cross-validation, we ascertain the model's capacity to discern between sweet and sour odors with remarkable accuracy. Its performance eclipses alternative algorithms, manifesting a discernible advancement in the realm of odor perception prediction. Here conducts an in-depth analysis to unravel the pivotal molecular features that wield maximal influence on odor prediction. Our findings shed light on the nuanced molecular attributes that underlie the perceptions of sweetness and sourness, contributing to a deeper understanding of the intricate nexus between chemistry and human sensory experiences. In summation, this research paper not only presents a significant stride in the field of olfactory prediction but also emphasize the potency of hybrid models in transcending the limitations of individual algorithmic paradigms. The confluence of DL's sequence comprehension and ensemble learning's pattern extraction showcases the promise of interdisciplinary approaches in unraveling the mysteries of human sensory perception. As our proposed model spearheads a new era of odor prediction accuracy, it beckons further exploration into the uncharted territories of olfaction and computational sensory analysis.