التفاصيل البيبلوغرافية
| العنوان: |
Machine Learning Based Modelling of Human and Insect Olfaction Screens Millions of compounds to Identify Pleasant Smelling Insect Repellents |
| المؤلفون: |
Kowalewski, Joel, Boyle, Sean M., Arvidson, Ryan, Ejercito, Jadrian, Ray, Anandasankar |
| بيانات النشر: |
eLife Sciences Publications, Ltd |
| سنة النشر: |
2024 |
| المجموعة: |
eLife (E-Journal - via CrossRef) |
| الوصف: |
The rational discovery of behaviorally active odorants is impeded by a lack of understanding on how the olfactory system generates percept or valence for a volatile chemical. In previous studies we showed that chemical informatics could be used to model prediction of ligands for a large repertoire of odorant receptors in Drosophila (Boyle et al., 2013). However, it remained difficult to predict behavioral valence of volatiles since the activities of a large ensembles of odor receptors encode odor information, and little is known of the complex information processing circuitry. This is a systems-level challenge well-suited for Machine-learning approaches which we have used to model olfaction in two organisms with completely unrelated olfactory receptor proteins: humans (∼400 GPCRs) and insects (∼100 ion-channels). We use chemical structure-based Machine Learning models for prediction of valence in insects and for 146 human odor characters. Using these predictive models, we evaluate a vast chemical space of >10 million compounds in silico. Validations of human and insect behaviors yield very high success rates. The discovery of desirable fragrances for humans that are highly repulsive to insects offers a powerful integrated approach to discover new insect repellents. |
| نوع الوثيقة: |
other/unknown material |
| اللغة: |
unknown |
| DOI: |
10.7554/elife.95532 |
| الإتاحة: |
https://doi.org/10.7554/elife.95532Test
https://elifesciences.org/reviewed-preprints/95532v1/pdfTest |
| حقوق: |
http://creativecommons.org/licenses/by/4.0Test/ |
| رقم الانضمام: |
edsbas.39886BE9 |
| قاعدة البيانات: |
BASE |
