SYBA (SYnthetic Bayesian Accessibility) is a fragment-based method for the rapid classification of organic compounds as easy-(ES) or hard-to-synthesize (HS). It is based on a Bernoulli naïve Bayes classifier that is used to assign SYBA score contributions to individual fragments based on their frequencies in the database of ES and HS molecules. SYBA was trained on ES molecules available in the ZINC15 database and on HS molecules generated by the Nonpher methodology. SYBA was compared with a random forest, that was utilized as a baseline method, as well as with other two methods for synthetic accessibility assessment: SAScore and SCScore. When used with their suggested thresholds, SYBA improves over random forest classification, albeit marginally, and outperforms SAScore and SCScore. However, upon the optimization of SAScore threshold (that changes from 6.0 to-4.5), SAScore yields similar results as SYBA. Because SYBA is based merely on fragment contributions, it can be used for the analysis of the contribution of individual molecular parts to compound synthetic accessibility. SYBA is publicly available at https ://githu b.com/lich-uct/ syba under the GNU General Public License.

SYBA (SYnthetic Bayesian Accessibility) is a fragment-based method for the rapid classification of organic compounds as easy- (ES) or hard-to-synthesize (HS). It is based on a Bernoulli naïve Bayes classifier that is used to assign SYBA score contributions to individual fragments based on their frequencies in the database of ES and HS molecules. SYBA was trained on ES molecules available in the ZINC15 database and on HS molecules generated by the Nonpher methodology. SYBA was compared with a random forest, that was utilized as a baseline method, as well as with other two methods for synthetic accessibility assessment: SAScore and SCScore. When used with their suggested thresholds, SYBA improves over random forest classification, albeit marginally, and outperforms SAScore and SCScore. However, upon the optimization of SAScore threshold (that changes from 6.0 to ~4.5), SAScore yields similar results as SYBA. Because SYBA is based merely on fragment contributions, it can be used for the analysis of the contribution of individual molecular parts to compound synthetic accessibility. SYBA is publicly available at https://github.com/lich-uct/syba under the GNU General Public License.

SYBA (SYnthetic Bayesian Accessibility) is a fragment based method for the rapid classification of organic compounds as easy- (ES) or hard-to-synthesize (HS). SYBA is based on the Bayesian analysis of the frequency of molecular fragments in the database of ES and HS molecules. It was trained on ES molecules available in the ZINC15 database and on HS molecules generated by the Nonpher methodology. SYBA was compared with a random forest, that was utilized as a baseline method, as well as with other two methods for synthetic accessibility assessment: SAScore and SCScore. When used with their suggested thresholds, SYBA improves over random forest classification, albeit marginally, and outperforms SAScore and SCScore. However, with thresholds optimized by the analysis of ROC curves, SAScore improves considerably and yields similar results as SYBA. Because SYBA is based merely on fragment contributions, it can be used for the analysis of the contribution of individual molecular parts to compound synthetic accessibility. Though SYBA was developed to quickly assess compound synthetic accessibility, its underlying Bayesian framework is a general approach that can be applied to any binary classification problem. Therefore, SYBA can be easily re-trained to classify compounds by other physico-chemical or biological properties. SYBA is publicly available at https://github.com/lich-uct/syba under the GNU General Public License.

The dark web scene has been drawing the attention of law enforcement agencies and researchers alike. To date, most of the published works on the dark web are based on data gained by passive observation. To gain a more contextualized perspective, a study was conducted in which three vendors were selected on the "Dream Market" dark web marketplace, from whom subsequently several new psychoactive substances (NPS) were ordered. All transactions were documented from the initial drug deal solicitation to the final qualitative analysis of all received samples. From the selected vendors, a total of nine NPS samples was obtained, all of which were analyzed by NMR, HRMS, LC-UV, and two also by x-ray diffraction. According to our analyses, four of the five substances offered under already known NPS names contained a different NPS. The selected vendors therefore either did not know about their product, or deliberately deceived the buyers. Furthermore, two of three obtained samples of purportedly novel NPS were identified as already documented substances sold under a different name. However, the third characterized substance sold as "MPF-47700" was a novel, yet uncharacterized, NPS. Finally, we received a single undeclared substance, later identified as 5F-ADB. In addition to chemical analysis of the nine obtained NPS samples, the methodology used also yielded contextual information about the accessibility of NPS on the dark web, the associated purchase process, and the modus operandi of three NPS vendors. Direct participation in dark web marketplaces seems to provide additional layers of information useful for forensic studies.