AI approaches face significant challenges, especially in regards to data. AI algorithms need experimental data and previous knowledge on the system to train, parameterize and build the predictive models. On the contrary, SEE-TxTM methodology is based on first principles, meaning it doesn't need any training data or previous knowledge on the system, which makes it applicable to any target with an available 3D structure. Additionally there is no uncertainty in the datasets with SEE-TxTM . The 3D structures have been well documented in literature and the protein’s role in disease has been validated. As data sets improve, AI approaches will no doubt as well-but there will always be some degree of uncertainty in the training site.
SEE-TxTM  is a solution to the inefficiencies of the drug discovery and development process by greatly reducing the time and cost in discovering novel targets. Currently, there is no known AI algorithm able to induce the opening of hidden or "cryptic" binding sites. This requires a complex and specific coordination of ligand effects on the protein surface which can only be done with SEE-TxTM.