Hit Dexter 2.0: Machine-Learning Models for the Prediction of Frequent Hitters

Assay interference caused by small molecules continues to pose a significant challenge for early drug discovery. A number of rule-based and similarity-based approaches have been derived that allow the flagging of potentially “badly behaving compounds”, “bad actors”, or “nuisance compounds”. These compounds are typically aggregators, reactive compounds, and/or pan-assay interference compounds (PAINS), and many of them are frequent hitters. Hit Dexter is a recently introduced machine learning approach that predicts frequent hitters independent of the underlying physicochemical mechanisms (including also the binding of compounds based on “privileged scaffolds” to multiple binding sites). Here we report on the development of a second generation of machine learning models which now covers both primary screening assays and confirmatory dose–response assays. Protein sequence clustering was newly introduced to minimize the overrepresentation of structurally and functionally related proteins. The models correctly classified compounds of large independent test sets as (highly) promiscuous or nonpromiscuous with Matthews correlation coefficient (MCC) values of up to 0.64 and area under the receiver operating characteristic curve (AUC) values of up to 0.96. The models were also utilized to characterize sets of compounds with specific biological and physicochemical properties, such as dark chemical matter, aggregators, compounds from a high-throughput screening library, drug-like compounds, approved drugs, potential PAINS, and natural products. Among the most interesting outcomes is that the new Hit Dexter models predict the presence of large fractions of (highly) promiscuous compounds among approved drugs. Importantly, predictions of the individual Hit Dexter models are generally in good agreement and consistent with those of Badapple, an established statistical model for the prediction of frequent hitters. The new Hit Dexter 2.0 web service, available at http://hitdexter2.zbh.uni-hamburg.de, not only provides user-friendly access to all machine learning models presented in this work but also to similarity-based methods for the prediction of aggregators and dark chemical matter as well as a comprehensive collection of available rule sets for flagging frequent hitters and compounds including undesired substructures.acceptedVersio

Scheduling with AND/OR precedence constraints

In many scheduling applications it is required that the processing of some job must be postponed until some other job, which can be chosen from a pre-given set of alternatives, has been completed. The traditional concept of precedence constraints fails to model such restrictions. Therefore, the concept has been generalized to so-called AND/OR precedence constraints which can cope with this kind of requirement

An EMF cell with a nitrogen solid electrolyte-on the transference of nitrogen ions in yttria-stabilized zirconia

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The mobility and electrochemical activity of nitrogen inside and/or at the surface of ionic compounds is of fundamental, as well as of possibly practical, relevance. In order to better understand the role of nitrogen anions in solid electrolytes, we measured the transference number of nitrogen in yttria-stabilized zirconia (YSZ) by a concentration cell technique as a function of oxygen activity at different temperatures in the range of 1023 ≤ T/K ≤ 1123. YSZ doped with 1.9 wt% of N (YSZ:N) turned out to have an appreciable nitrogen transference number, which increased from 0 to 0.1 with decreasing oxygen activity in the range of −20 < logaO2 < −14. The stability of N in YSZ:N, however, has yet to be elucidated under oxidizing conditions.DFG, SPP 1136, Substitutionseffekte in ionischen Festkörper

An EMF cell with a nitrogen solid electrolyte-on the transference of nitrogen ions in yttria-stabilized zirconia

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The mobility and electrochemical activity of nitrogen inside and/or at the surface of ionic compounds is of fundamental, as well as of possibly practical, relevance. In order to better understand the role of nitrogen anions in solid electrolytes, we measured the transference number of nitrogen in yttria-stabilized zirconia (YSZ) by a concentration cell technique as a function of oxygen activity at different temperatures in the range of 1023 ≤ T/K ≤ 1123. YSZ doped with 1.9 wt% of N (YSZ:N) turned out to have an appreciable nitrogen transference number, which increased from 0 to 0.1 with decreasing oxygen activity in the range of −20 < logaO2 < −14. The stability of N in YSZ:N, however, has yet to be elucidated under oxidizing conditions.DFG, SPP 1136, Substitutionseffekte in ionischen Festkörper