Phenothiazine derivatives can unselectively inhibit the trypanothione-dependent antioxidant system enzyme trypanothione reductase (TR). A virtual screening of 2163 phenothiazine derivatives from the ZINC15 and PubChem databases docked on the active site of T. cruzi TR showed that 285 compounds have higher affinity than the natural ligand trypanothione disulfide. 244 compounds showed higher affinity toward the parasite's enzyme than to its human homolog glutathione reductase. Protein-ligand interaction profiling predicted that the main interactions for the top scored compounds were with residues important for trypanothione disulfide binding: Phe396, Pro398, Leu399, His461, Glu466, and Glu467, particularly His461, which participates in catalysis. Two compounds with the desired profiles, ZINC1033681 (Zn_C687) and ZINC10213096 (Zn_C216), decreased parasite growth by 20 % and 50 %, respectively. They behaved as mixed-type inhibitors of recombinant TR, with Ki values of 59 and 47 μM, respectively. This study provides a further understanding of the potential of phenothiazine derivatives as TR inhibitors.

Conjugated QSPR models for reactions integrate fundamental chemical laws expressed by mathematical equations with machine learning algorithms. Herein we present a methodology for building conjugated QSPR models integrated with the Arrhenius equation. Conjugated QSPR models were used to predict kinetic characteristics of cycloaddition reactions related by the Arrhenius equation: rate constant logk , pre-exponential factor logA , and activation energy Ea . They were benchmarked against single-task (individual and equation-based models) and multi-task models. In individual models, all characteristics were modeled separately, while in multi-task models logk , logA and Ea were treated cooperatively. An equation-based model assessed logk using the Arrhenius equation and logA and Ea values predicted by individual models. It has been demonstrated that the conjugated QSPR models can accurately predict the reaction rate constants at extreme temperatures, at which reaction rate constants hardly can be measured experimentally. Also, in the case of small training sets conjugated models are more robust than related single-task approaches.