Summary: New artificial intelligence technology can accurately predict how any chemical is going to smell to humans. A pair of researchers at the University of California, Riverside, has used machine learning to understand what a chemical smells like -- a research breakthrough with potential applications in the food flavor and fragrance industries. "We now can use artificial intelligence to predict how any chemical is going to smell to humans," said Anandasankar Ray, a professor of molecular, cell and systems biology, and the senior author of the study that appears in iScience. "Chemicals that are toxic or harsh in, say, flavors, cosmetics, or household products can be replaced with natural, softer, and safer chemicals." Humans sense odors when some of their nearly 400 odorant receptors, or ORs, are activated in the nose.

A drug screen using machine learning has identified hundreds of potential drugs that could be used to treat COVID-19, researchers say. Researchers have used machine learning to identify hundreds of new potential drugs that could help treat COVID-19, the disease caused by SARS-CoV-2. The study was conducted at the University of California, Riverside, US. "There is an urgent need to identify effective drugs that treat or prevent COVID-19," said Professor Anandasankar Ray, who led the research. "We have developed a drug discovery pipeline that identified several candidates… Existing US Food and Drug Administration (FDA)-approved drugs that target one or more human proteins important for viral entry and replication are currently high priority for repurposing as new COVID-19 drugs. The demand is high for additional drugs or small molecules that can interfere with both entry and replication of SARS-CoV-2 in the body. Our drug discovery pipeline can help."

"There is an urgent need to identify effective drugs that treat or prevent COVID-19," said Anandasankar Ray, a professor of molecular, cell, and systems biology who led the research. "We have developed a drug discovery pipeline that identified several candidates." The drug discovery pipeline is a type of computational strategy linked to artificial intelligence -- a computer algorithm that learns to predict activity through trial and error, improving over time. With no clear end in sight, the COVID-19 pandemic has disrupted lives, strained health care systems, and weakened economies. Efforts to repurpose drugs, such as Remdesivir, have achieved some success.

Scientists at the University of California, Riverside, have used machine learning to identify hundreds of new potential drugs that could help treat COVID-19, the disease caused by the novel coronavirus, or SARS-CoV-2. "There is an urgent need to identify effective drugs that treat or prevent COVID-19," said Anandasankar Ray, a professor of molecular, cell, and systems biology who led the research. "We have developed a drug discovery pipeline that identified several candidates." The drug discovery pipeline is a type of computational strategy linked to artificial intelligence -- a computer algorithm that learns to predict activity through trial and error, improving over time. With no clear end in sight, the COVID-19 pandemic has disrupted lives, strained health care systems, and weakened economies.

A pair of researchers at the University of California, Riverside, has used machine learning to understand what a chemical smells like--a research breakthrough with potential applications in the food flavor and fragrance industries. "We now can use artificial intelligence to predict how any chemical is going to smell to humans," said Anandasankar Ray, a professor of molecular, cell and systems biology, and the senior author of the study that appears in iScience. "Chemicals that are toxic or harsh in, say, flavors, cosmetics, or household products can be replaced with natural, softer, and safer chemicals." Humans sense odors when some of their nearly 400 odorant receptors, or ORs, are activated in the nose. Each OR is activated by a unique set of chemicals; together, the large OR family can detect a vast chemical space.

IMAGE: Anandasankar Ray is a professor of molecular, cell and systems biology at UC Riverside. "We now can use artificial intelligence to predict how any chemical is going to smell to humans," said Anandasankar Ray, a professor of molecular, cell and systems biology, and the senior author of the study that appears in iScience. "Chemicals that are toxic or harsh in, say, flavors, cosmetics, or household products can be replaced with natural, softer, and safer chemicals." Humans sense odors when some of their nearly 400 odorant receptors, or ORs, are activated in the nose. Each OR is activated by a unique set of chemicals; together, the large OR family can detect a vast chemical space.

A pair of researchers at the University of California, Riverside, has used machine learning to understand what a chemical smells like -- a research breakthrough with potential applications in the food flavor and fragrance industries. "We now can use artificial intelligence to predict how any chemical is going to smell to humans," said Anandasankar Ray, a professor of molecular, cell and systems biology, and the senior author of the study that appears in iScience. "Chemicals that are toxic or harsh in, say, flavors, cosmetics, or household products can be replaced with natural, softer, and safer chemicals." Humans sense odors when some of their nearly 400 odorant receptors, or ORs, are activated in the nose. Each OR is activated by a unique set of chemicals; together, the large OR family can detect a vast chemical space.