Our platform

A computational and experimental platform for targeted polypharmacology

We are building a computational-experimental platform to design therapeutics that can target several disease-causing proteins at once. By designing multi-specific drugs, we are able to create therapeutics that are more efficacious and safer than existing medicines

A focus on kinase therapeutics

We focus on creating a new class of drugs targeted against the human kinome, a family of 500 proteins associated with diseases such as cancer, auto-immunity and neurodegeneration

Abstract image

Kinase Synergies

We combine genetic screening, biochemical assays and bioinformatics to predict kinase targets that synergize when inhibited together and kinase anti-targets that when inhibited cause toxicity

Chemical structure design

Given a set of kinase targets and anti-targets, we use generative machine learning, chemical informatics and medicinal chemistry to rationally design a molecule with the desired binding profile

Kinome binding

Our generative machine learning model is powered by our best-in-class kinome binding predictor, our machine learning model to predict the entire kinome profile of a molecule

Multi-target hit series and lead optimization

From initial hits, our computational platform can rapidly iterate through designs for the next series of compounds, optimizing for binding profile, selectivity and ADME properties