Creating the Next Generation of Neurotherapeutics

Machine Learning Accelerated Target Identification

We are uploading terabytes of patient data each day into our platform to train our machine learning target identification models. Using tens of thousands of genomic, transcriptomic, and proteomic sequences, we are working to uncover new biological drivers of disease.

De Novo Targeted Compound Design

Traditional in silico drug screening requires sifting through billions of compounds in chemical libraries to find molecules with a high probability of clinical efficacy. Our novel approach employs deep reinforcement learning to design molecules that already meet our requirements. Our de novo generation prototype dreams up new compounds with specified parameters suitable for neurotherapeutics.

In Silico Pre-Clinical Testing

By leveraging computational tools and our team's chemistry and pharmacology expertise, we can rigorously screen candidate molecules before reaching the lab and increase the probability of success in clinical trials.