Skoltech is an international graduate research-focused university that was founded by the group of world-renowned scientists in 2011. Skoltech's curriculum focuses on technology and innovation, offering Master's programs in 11 technological disciplines. Students receive rigorous theoretical and practical training, design their own research projects, participate in internships and gain entrepreneurial skills in English. The faculty is comprised of current researchers with international accreditation and achievements.

Product recommendation systems can help with search of antiviral drugs

Scientists from Skoltech and the Chumakov Federal Scientific Center for Research and Development of Immune-and-Biological Products of RAS checked the ability of artificial intelligence that suggest products to buy, recommend new antiviral compounds. The researchers found that advanced algorithms can effectively suggest both music, movies to buy, and compounds with antiviral activity. The results of the study were published in the high-rated journal ACS Omega.

Every internet user knows contextual advertising that suggests products to buy along with already purchased ones. Online retail use recommender systems that analyze user’s preferences and purchase history to suggest a new product, a movie, or music.  Can these algorithms “recommend” a new antiviral drug or “recommend” a well-known approved drug for a new disease?

figure1

Active compounds search in broad chemical space / Skoltech

 

A multidisciplinary team from the Skoltech Center for Computational and Data-Intensive Science and Engineering (CDISE) (Ekaterina Sosnina, Sergey Sosnin, Ivan Nazarov, and Maxim Fedorov) and the Chumakov Federal Scientific Center for Research and Development of Immune-and-Biological Products of RAS (Anastasia Nikitina and Dmitry Osolodkin) has checked this idea. The researchers carried out computational experiments and compared the performance of different recommender algorithms for the selection of small molecules active toward viruses. They showed that recommender systems could effectively pinpoint antiviral compounds and find promising drug candidates based on latent relationships in chemical and biological data. The key to success was Big Data: the team used the extensive ViralCHEMBL database containing antiviral activity data of about 250,000 molecules against 158 viral species.

“The success of this project is based on both significant progress in the mathematical algorithms and deep expertise in the subject area, such as medicinal chemistry, biology, and machine learning. We have launched this project long before the coronavirus outbreak and hope that our findings will help researchers to find new molecules with anti-SARS-CoV-2 activity” says Ekaterina Sosnina, a Ph.D. student at Skoltech and the first author of the paper.

The scientists believe that their study will help chemists to find new antiviral drug candidates and provide a way for the repurposing of the existing drugs to combat SARS-CoV-2 and other potential viral outbreaks.

Contact information:
Skoltech Communications
+7 (495) 280 14 81

Share on VK