A semantic-based community model for high-fidelity tuning of olfactory mixture distances
Vahid Satarifard, Laura Sisson, Yikun Han, Pedro Ilídio, Matej Hladiš, Maxence Lalis, Xuebo Song, Tiffany Yang, Wenjie Yin, Aharon Ravia, CiCi Xingyu Zheng, Gaia Andreoletti, Jake Albrecht, Robert Pellegrino, Zehua Wang, Stephen Yang, Robbe D’hondt, Achilleas Ghinis, Jasper de Boer, Felipe Kenji Nakano, Alireza Gharahighehi, Jose M. G. Vilar, Leonor Saiz, DREAM Olfactory Mixtures Prediction Consortium, Benjamin Sanchez-Lengeling, Andreas Keller, Leslie B. Vosshall, Sébastien Fiorucci, Ambuj Tewari, Jérémie Topin, Celine Vens, Mårten Björkman, Danica Kragic, Noam Sobel, Nicholas A. Christakis, Joel D. Mainland, Pablo Meyer, Ruhallah Amandi, Nicola Amoroso, Loredana Bellantuono, Michele Dibattista, Alfonso Monaco, Ester Pantaleo, Sabina Tangaro, Gary Tom, Ella Rajaonson, Cher-Tian Ser, Sean Park, Stanley Lo, Brian K Lee, Gautam Ahuja, Siddhant Poudyal, Bableen Kaur, Douluri Pushkala Devi, Rintu Kutum, Grant McConachie, Soroush Arabshahi, Saeed Karimimehr, Jeremy Kotlyar, Faraz Yazdani, Réka Böröcz, Bence Szalai, Adomas Malaiska, Victor Tarca, Connor Fong, Hugo Talibart, Dimitri Gilis, Chih-Han Huang, Tsai-Min Chen, Hsuan-Kai Wang, Jhih-Yu Chen, Edward S.C. Shih, Chih-Hsun Wu, Wei-Quan Fang, Sz-Hau Chen, Kuei-Lin Huang, Srijeet Bhattacharjee, Malay Bhattacharyya, Sergey Shuvaev, Cyrille Mascart, Khue Tran, Alexei KoulakovA central goal in sensory science is to establish quantitative mappings between physical stimuli and perceptual experience. Although such mappings are well defined in vision and audition, they remain elusive in olfaction, particularly for complex odor mixtures. Here, we show that perceptual distances between odor mixtures can be predicted with high fidelity and are unexpectedly well captured by a compact semantic space derived from single-molecule representations. In the Dialogue for Reverse Engineering Assessment and Methods Olfactory Mixtures Prediction Challenge, we assembled a unified dataset of odor-mixture pairs, benchmarked predictions on a hidden test set of 46 pairs, and integrated the top-performing models into a postchallenge ensemble. This model outperformed existing state-of-the-art approaches on the hidden test set, reducing RMSE by about 33% to 0.08 and increasing Pearson correlation by 53% to 0.57, and maintained strong performance on an independent validation set of 50 newly designed mixture pairs. An ensemble, retaining only olfactory semantic features for each model included, further improved predictions, raising the Pearson correlation by 7% to 0.61 on the test set and by 15% to 0.54 on the validation set. Given that semantic features were extracted from pure molecules, it suggests that mixture perception may not require fundamentally different representational principles from single-molecule olfaction. Together, these results establish a reproducible quantitative framework for olfactory mixture perception and advance efforts to measure, model, and engineer smell.