The supervision of novel psychoactive substances (NPSs) is a global problem, and the regulation of NPSs was heavily relied on identifying structural matches in established NPSs databases. However, violators could circumvent legal oversight by altering the side chain structure of recognized NPSs and the existing methods cannot overcome the inaccuracy and lag of supervision. In this study, we propose a scaffold and transformer-based NPS generation and Screening (STNGS) framework to systematically identify and evaluate potential NPSs. A scaffold-based generative model and a rank function with four parts are contained by our framework. Our generative model shows excellent performance in the design and optimization of general molecules and NPS-like molecules by chemical space analysis and property distribution analysis. The rank function includes synthetic accessibility score and frequency score, as well as confidence score and affinity score evaluated by a neural network, which enables the precise positioning of potential NPSs. Applied STNGS framework with molecular docking and a G protein-coupled receptor (GPCR) activation-based sensor (GRAB), we successfully identify three novel synthetic cannabinoids with activity. STNGS constrains the chemical space to generate NPS-like molecules database with diversity and novelty, which assists in the ex-ante regulation of NPSs.

Security bugs like memory errors are constantly introduced to software programs, and recent years have witnessed an increasing number of reported security bugs. Traditional detection approaches are mainly specification-based---detecting violations against a specified rule as security bugs. This often does not work well in practice because specifications are difficult to specify and generalize, leaving complicated and new types of bugs undetected. Recent research thus leans toward deviation-based detection which finds a substantial number of similar cases and detects deviating cases as potential bugs. This, however, suffers from two other problems. First, it requires enough similar cases to find deviations and thus cannot work for custom code that does not have similar cases. Second, code-similarity analysis is probabilistic and challenging, so the detection can be unreliable. Sometimes, similar cases can normally have deviating behaviors under different contexts.

Missing a security operation such as a bound check has been a major cause of security-critical bugs. Automatically checking whether the code misses a security operation in large programs is challenging since it has to understand whether the security operation is indeed necessary in the context. Recent methods typically employ cross-checking to identify deviations as security bugs, which collects functionally similar program slices and infers missed security operations through majority-voting. An inherent limitation of such approaches is that they heavily rely on a substantial number of similar code pieces to enable cross-checking. In practice, many code pieces are unique, and thus we may be unable to find adequate similar code snippets to utilize cross-checking.