1910, the AI-native biotech company at the forefront of small and large molecule therapeutics discovery, has announced the publication of its CANDID-CNS™ AI model in the Journal of Chemical Information and Modeling (JCIM), a peer-reviewed journal of the American Chemical Society. The study, titled “CANDID-CNS™: AI Unlocks Stereochemistry and Beyond Rule of 5 to Predict CNS Penetration of Small Molecules,” introduces a groundbreaking approach to predicting blood–brain barrier (BBB) penetration, a critical challenge in developing central nervous system (CNS) therapeutics. CANDID-CNS™ is the first AI model to successfully integrate stereochemistry and molecular features beyond the traditional Rule of 5 criteria—long considered a benchmark in drug design—into a predictive framework for CNS drug development. By leveraging deep learning and a vast dataset of molecular properties, the model can identify compounds with a high likelihood of crossing the BBB, even when they fall outside conventional drug-likeness parameters. This advancement is particularly significant because the BBB acts as a protective barrier, preventing many potentially effective drugs from reaching the brain. Historically, this has limited the development of treatments for neurological and psychiatric disorders such as Alzheimer’s, Parkinson’s, and schizophrenia. CANDID-CNS™ enables researchers to explore a much broader chemical space, including complex, highly functionalized, and stereospecific molecules that were previously deemed too challenging or non-drug-like to pursue. The model was trained on a diverse set of experimental data, including in vivo and in vitro BBB penetration results, and has demonstrated high accuracy in prospective predictions. The publication details the model’s architecture, training methodology, and validation across multiple compound classes, underscoring its robustness and generalizability. 1910’s work with CANDID-CNS™ reflects its broader mission to use AI as a foundational tool in drug discovery, moving beyond traditional trial-and-error approaches. By expanding the boundaries of what is chemically feasible, the company aims to accelerate the development of novel therapies for unmet medical needs. The publication in JCIM marks a key milestone in the field of computational medicinal chemistry, offering a new standard for AI-driven prediction of CNS drug delivery. As 1910 continues to refine and expand its AI platform, CANDID-CNS™ stands as a testament to the transformative potential of artificial intelligence in unlocking previously inaccessible therapeutic avenues.