Moscow, Russia

Yan A Ivanenkov

USPTO Granted Patents = 7 

Average Co-Inventor Count = 5.6

ph-index = 1

Forward Citations = 2(Granted Patents)


Company Filing History:


Years Active: 2022-2025

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7 patents (USPTO):

Title: Innovations of Yan A Ivanenkov

Introduction

Yan A Ivanenkov is a prominent inventor based in Moscow, Russia. He has made significant contributions to the field of pharmaceuticals and machine learning, holding a total of four patents. His work focuses on developing innovative solutions for complex problems, particularly in the realm of drug discovery and molecular generation.

Latest Patents

One of his latest patents is titled "Subset conditioning using variational autoencoder with a learnable tensor train induced prior." This model is a Variational Autoencoder (VAE) that incorporates a learnable prior, which is parametrized with a Tensor Train (VAE-TTLP). The VAE-TTLP is capable of generating new objects, such as molecules, that possess specific properties and biological activity. This model can be trained to produce objects with desired properties, even when some data may be missing. Another significant patent is "SARS-CoV-2 inhibitors for treating coronavirus infections." This patent provides compounds, pharmaceutical compositions, and methods aimed at treating SARS-CoV-2 infections, showcasing Ivanenkov's commitment to addressing pressing health challenges.

Career Highlights

Yan A Ivanenkov is currently associated with Insilico Medicine IP Limited, where he continues to push the boundaries of innovation in drug development. His expertise in machine learning and molecular design has positioned him as a key player in the industry.

Collaborations

He has collaborated with notable colleagues, including Aleksandr M Aliper and Daniil Polykovskiy, contributing to a dynamic research environment that fosters innovation and creativity.

Conclusion

Yan A Ivanenkov's work exemplifies the intersection of technology and medicine, driving forward the development of new therapeutic solutions. His patents reflect a deep understanding of both machine learning and pharmaceutical needs, making him a valuable contributor to the field.

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