Odor detection involves hundreds of olfactory receptors from diverse families, making modeling of hedonic valence of an odorant difficult, even in Drosophila melanogaster where most receptors have been deorphanised. We demonstrate that a broadly tuned heteromeric receptor that detects CO (Gr21a, Gr63a) and other odorants is a key determinant of valence along with a few members of the Odorant receptor family in a T-maze, but not in a trap assay. Gr21a and Gr63a have atypically high amino acid conservation in Dipteran insects, and they use both inhibition and activation to convey positive or negative valence for numerous odorants. Inhibitors elicit a robust Gr63a-dependent attraction, while activators, strong aversion. The attractiveness of inhibitory odorants increases with increasing background CO levels, providing a mechanism for behavior modulation in odor blends. In mosquitoes, valence is switched and activation of the orthologous receptor conveys attraction. Reverse chemical ecology enables the identification of inhibitory odorants to reduce attraction of mosquitoes to skin.

There is an urgent need for the identification of effective therapeutics for COVID-19 and we have developed a machine learning drug discovery pipeline to identify several drug candidates. First, we collect assay data for 65 target human proteins known to interact with the SARS-CoV-2 proteins, including the ACE2 receptor. Next, we train machine learning models to predict inhibitory activity and use them to screen FDA registered chemicals and approved drugs (~100,000) and ~14 million purchasable chemicals. We filter predictions according to estimated mammalian toxicity and vapor pressure. Prospective volatile candidates are proposed as novel inhaled therapeutics since the nasal cavity and respiratory tracts are early bottlenecks for infection. We also identify candidates that act across multiple targets as promising for future analyses. We anticipate that this theoretical study can accelerate testing of two categories of therapeutics: repurposed drugs suited for short-term approval, and novel efficacious drugs suitable for a long-term follow up.

Alzheimer's disease (AD) is a progressive, neurodegenerative disorder that impairs memory and semantic processing. AD patients and MCI patients at risk for AD show altered N400 ERP responses to incongruent visual and verbal stimuli. AD patients exhibit neuropathology in olfactory brain areas before cognitive symptoms, suggesting the potential for olfactory processing to reflect early pathology. Despite this, odor congruency has not been examined. We investigated odor-image congruency in older adults at genetic risk for AD. ApoE ε4 carriers and non-carriers were screened for anosmia, severe hyposmia, and dementia. Olfactory ERPs were measured 600-1300ms following odor-image pairs. Odors were each presented once congruently and once incongruently via olfactometer. Right dorsal and ventral sites were vulnerable to the ε4 allele, consistent with a compensation hypothesis. Pz amplitude differences on congruous and incongruous trials were larger in non carriers. Regression indicated that congruency showed very high sensitivity and specificity for correctly classifying ε4 carriers from non-carriers.

Machine learning predicted activity of 34 human ORs for~0.5 million chemicals Activities of human ORs could predict odor character using machine learning Few OR activities were needed to optimize predictions of each odor percept Behavior predictions in Drosophila also need few olfactory receptor activities

The fundamental units of olfactory perception are discrete 3D structures of volatile chemicals that each interact with specific subsets of a very large family of hundreds of odorant receptor proteins, in turn activating complex neural circuitry and posing a challenge to understand. We have applied computational approaches to analyze olfactory perceptual space from the perspective of odorant chemical features. We identify physicochemical features associated with ~150 different perceptual descriptors and develop machine learning models. Validation of predictions shows a high success rate for test set chemicals within a study, as well as across studies more than 30 years apart in time. Due to the high success rates we are able to map ~150 percepts onto a chemical space of nearly 0.5 million compounds, predicting numerous percept-structure combinations. The chemical structure-to-percept prediction provides a systems-level view of human olfaction and opens the door for comprehensive computational discovery of fragrances and flavors.