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Inadequate bioavailability is one of the most critical reasons for the failure of oral drug development. However, the way that substructures affect bioavailability remains largely unknown. Serotonin transporter (SERT) inhibitors are first-line drugs for major depression disorder, and improving their bioavailability may be able to decrease side-effects by reducing daily dose. Thus, it is an excellent model to probe the relationship between substructures and bioavailability. Here, we proposed the concept of "nonbioavailable substructures", referring to substructures that are unfavorable to bioavailability. A machine learning model was developed to identify nonbioavailable substructures based on their molecular properties and shows the accuracy of 83.5%. A more potent SERT inhibitor

In the title compound, C12H16N4O3, all bond lengths and angles are within normal ranges. An O—H⋯N and an N—H⋯O intra­molecular hydrogen bond contribute to the approximately planar mol­ecular conformation. In the crystal structure, inter­molecular N—H⋯O and N—H⋯N hydrogen bonds link the mol­ecules into ribbons parallel to the [1\overline{1}0] direction.