Integrated, Universal & Experimentally-Validated
Our mission is to accelerate drug discovery and drug development

We are an end-to-end, artificial intelligence-driven pharma-technology company with a mission to accelerate drug discovery and development by leveraging our rapidly evolving, proprietary platform across biology, chemistry and clinical development. Our Pharma.AI platform has the potential to rapidly bring novel breakthrough medicines to patients while decreasing costs and increasing probabilities of success.
Enabling multi-omics target discovery and deep biology analysis engine to considerably reduce required time from several months to the span of just a few clicks
Find novel lead-like molecules in a week through this automated, machine learning de-novo drug design and scalable engineering platform
Predict clinical trials success rate, recognize the weak points in trial design, while adopting the best practices in the industry
Discover and Prioritize
Design and predict
Novel Targets
Novel Molecules
Clinical Trials
Nobel Laureate Michael Levitt: How Early Protein Modeling Advanced to AI-Driven Drug Discovery with Insilico Medicine
Stanford professor Michael Levitt, PhD, a member of Insilico Medicine's Scientific Advisory Board, won the Nobel Prize in Chemistry in 2013 for his groundbreaking work in protein structure and protein folding using computer modeling. In this lecture, Dr. Levitt describes how the work he began over 50 years ago has been vastly improved and expanded through the massive increase in computer speed and incredible advances in machine learning. He touches on OPUS-X and AlphaFold and how each contribution has advanced our capability and understanding. Now, says Dr. Levitt, Insilico Medicine is using AI to create an entirely new AI-driven drug discovery pipeline from A to Z. Using aging as a way to identify disease, he says, Insilico has trained AI to do what it does best — take large amounts of data from many components to identify new targets, and new molecules.
"Uncertainty is a good thing," Levitt says. "By combining data with clever filtering we get certainty and options from uncertainty." Dr. Levitt sees massive possibilities ahead. "The protein-folding problem that was a very difficult problem for 50 years, and drug design, are all being dealt with in this global, all-encompassing way," he says. "And I am personally very, very optimistic."