Artificial intelligence for drug discovery and aging research
We provide services to academia, pharmaceutical and cosmetics companies:
  • Advanced deep learning solutions
  • Custom drug discovery engines
  • Custom biomarkers discovery
  • New tools for aging research

We license 827 drug-disease predictions and biomarkers:

  • 573 antineoplastic agents
  • 114 metabolic regulators
  • 41 CVD disease candidates
  • 38 CNS agents
  • 37 anti-cancer immune response boosters
  • 24 senolytics
  • Aging biomarkers (biochemical and transcriptional)
  • Cancer biomarkers (biochemical and transcriptional)
Our mission is to extend healthy longevity

Some of our current projects
coming soon
coming soon
About Insilico Medicine
Basel Life Science Week: Practical Applications of Aging Research Forum
Embryonic AI: Analysis of Embryonic State using Deep Neural Networks (Property of Biotime, Inc)
Aging Researcher Insilico Medicine Takes Top Prize at PMWC 2015
Over 150 academic and industry collaborators worldwide
At the "Ageing Societies 2016," The Economist conference in London, LongeVC and Insilico Medicine entered into a research collaboration.
Biogerontology Research Foundation scientists announce collaboration to develop biomarkers of aging with Johns Hopkins University, Albert Einstein College of Medicine, Boston University, Novartis, Nestle & Biotime Inc.
A novel approach for precision medicine and drug discovery on gene expression data.
Comparing Aging.AI 1.0 using 41 blood biochemistry biomarkers, Aging.AI 2.0 uses just 33 parameters from the blood test and has slightly higher mean absolute error.
Advances in computational biology and artificial intelligence used to identify compounds with potential to extend human life.
Insilico Medicine and InSilicoScreen merge to take human aging research to the next level.
Our recent paper published in Molecular Pharmaceuticals received the American Chemical Society Editors' Choice Award.
Deep biomarkers of human aging based on a simple blood test.
Insilico Medicine to present a range of deep learned biomarkers of ageing and deep learned predictors of biological age at the RE-WORK Deep learning in Healthcare Summit.
BioTime and Insilico Medicine collaborate on embryonic artificial intelligence using deep learning to study embryonic development.
This tactic allows marketers to publish targeted ads in front of an interest category or a defined audience.
Recent deals
Selected publications
Alex Zhavoronkov, PhD
Qingsong Zhu, PhD
Alex Aliper
Gene Makarev, PhD
Ivan Ozerov, PhD
Director, Senolytics
Quentin Vanhaelen, PhD
Director, Simulations
We have 39 scientists hired through hackathons and competitions worldwide in the US, Poland, Belgium, and Russia.
30% of our staff are women.
Scientific advisory board
Charles Cantor, PhD
ex-Director of the Human Genome Project and the Center for Advanced Biotechnology at Boston University
Michael Levitt, PhD
2013 Nobel Laureate (Chemistry) Professor, Stanford University
Bud Mishra, PhD
Professor of computer science at Courant Institute, professor of biology at Mt. Sinai School of Medicine and NYU School of Medicine
Donald Small, MD, PhD
Director of pediatric oncology at the Johns Hopkins Kimmel Cancer Center
Yuri Nikolsky, PhD
Co-founder, GeneGo
ex-VP, Thomson Reuters
Director, Skolkovo Found
Kristen Fortney, PhD
Postdoctoral fellow of Ellison Medical Foundation/American Federation for Aging Research at Stanford University
Alexey Moskalev, PhD
Professor at Syktyvkar State University
Head of the laboratory of Molecular radiobiology and gerontology at the Institute of biology of Komi Science Center of Ural division of RAS
Upcoming conferences
4th Annual Life Science R&D
Intelligence Leaders Forum
Basel, Switzerland
January 24-26, 2017
Deep Learning in Healthcare Summit
London, UK
February 23-24, 2017
Deep Learning in Healthcare Summit
Boston, USA
May 11-12, 2017
Press about us
Insilico Medicine, Inc.

Emerging Technology Centers

Johns Hopkins University Eastern Campus

Suite B301, 1101 East 33rd St.

Baltimore, MD 21218

Phone: +1 443 451 7212

Fax: +1 443 451 7210

Email: zhu(at)