Experts at the Center for Artificial Intelligence in Pharmacy at Sechenov University are working on artificial intelligence that will generate technical “recipes” for drugs tailored to a specific production line, as explained by a key leader from the Institute of Translational Medicine and Biotechnology at Moscow State Medical University. The insights were shared in a public interview. At Sechenov, the initiative is led by professor Vadim Tarasov.
“The goal is not to invent new molecules here but to speed up the creation of finished dosage forms for both generic and original medicines,” Tarasov stated. The effort focuses on accelerating development timelines while maintaining or improving quality, ultimately helping ensure a steady supply of effective therapies.
Current work involves collecting data on a drug from diverse sources, generating multiple technology options, and producing prototype formulations for testing. When a viable production approach is identified, the next step is to scale the process, integrating learnings from each iteration to refine outcomes.
Tarasov noted that the team intends to train a neural network model to support this technology, with the aim of enabling rapid deployment and scalable production. This approach could shorten the path to market for both import substitutions and needed generics, as well as support the delivery of original medicines where appropriate. The overarching goal is to make finished dosage forms available more quickly and with consistent quality for patients.
The expert anticipates initial results within one and a half to two years, with the broader project designed as a three-year program that builds on successive milestones and validation studies. The work is presented as a forward-looking step in the use of artificial intelligence to optimize pharmaceutical development and manufacturing processes.
Further details on the scientists’ work are available in their ongoing material on the topic. Additionally, prior research has explored neural networks for analyzing speech patterns in patients with depression, illustrating the broad potential of AI in healthcare.