BAGIM is an active community of Boston area scientists bringing together people from diverse fields of modeling and informatics to impact life and health sciences. BAGIM strives to create a forum for great scientific discussions covering a wide range of topics including data management, visualization, computational chemistry, drug discovery, protein structure, molecular modeling, structure-based drug design, data mining, software tools, and the sharing of goals and experiences. Our community is made up of participants from academia, government, and industry whose goal is to engage in the discussion of science involving a synthesis of theory and technology. Discussions sponsored by BAGIM are targeted to the needs and interests of informatics scientists, computational chemists, medicinal chemists, and statisticians. BAGIM also provides opportunities for networking within these disciplines as well as an arena for the dissemination of information of specific interest to the membership.

Friday, December 2, 2022

Alex Wiltschko - Digitizing olfaction for health and happiness

BAGIM is excited to welcome members back for our first in-person (CONFIRMED) event since 2020!

Date: December 14, 2022

Time: 6 pm ET

Location: Google - 150 Broadway, Cambridge, MA 02142

Sign-upBAGIM Meetup

Alex Wiltschko will present "Digitizing olfaction for health and happiness: progress and opportunities"

Abstract: Computers can see and hear, but they cannot smell. We know living noses can smell COVID-19, people trapped in rubble, cancer, and Parkinson's disease. If we could digitize smell, people would live longer, happier lives. But how can we measure a smell? Smells are produced by molecules that waft through the air, enter our noses, and bind to sensory receptors. Potentially billions of molecules can produce a smell, so figuring out which ones produce which smells is difficult to catalog or predict. Sensory maps can help us solve this problem. Color vision has the most familiar examples of these maps, from the color wheel we each learn in primary school to more sophisticated variants used to perform color correction in video production. While these maps have existed for centuries, useful maps for smell have been missing, because smell is a harder problem to crack: molecules vary in many more ways than photons do; data collection requires physical proximity between the smeller and smell (we don’t have good smell “cameras” and smell “monitors”); and the human eye only has three sensory receptors for color while the human nose has > 300 for odor. As a result, previous efforts to produce odor maps have failed to gain traction.

We introduce the “Principal Odor Map” (POM), which identifies the vector representation of each odorous molecule in the model’s embedding space as a single point in a high-dimensional space. The POM has the properties of a sensory map: first, pairs of perceptually similar odors correspond to two nearby points in the POM (by analogy, red is nearer to orange than to green on the color wheel). Second, the POM enables us to predict and discover new odors and the molecules that produce them. We demonstrate that the map can be used to prospectively predict the odor properties of molecules, understand these properties in terms of fundamental biology, and tackle pressing global health problems. We discuss each of these promising applications of the POM and how we test them.

Digitizing Smell: Using Molecular Maps to Understand Odor

Digitizing Smell: Using Molecular Maps to Understand Odor – Google AI Blog (googleblog.com)

Machine Learning Highlights a Hidden Order in Scents

AI Model Links Smell Molecules With Metabolic Processes | Quanta Magazine

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