About Us

We are strong proponents of Bayesian modeling in our AI-driven drug discovery platform. By using Bayesian methods, we can model uncertainty and integrate prior knowledge with new data to improve the accuracy and reliability of our virtual screening results.

In drug discovery, virtual screening is a critical step in identifying potential drug candidates. However, with the vast number of possible compounds to test, it is impossible to experimentally test them all. Our platform, AíChemy, uses AI and machine learning to predict the probability of compounds binding to target proteins and narrows down the search space.

By using Bayesian methods, we can continuously update our predictive models as new data becomes available, accounting for uncertainty and improving the accuracy of our predictions. This approach saves time and resources by reducing the number of false positives and enables us to discover drugs faster, cheaper, and more sustainably.