CYPlebrity: Machine learning models for the prediction of inhibitors of cytochrome P450 enzymes.

The vast majority of approved drugs are metabolized by the five major cytochrome P450 (CYP) isozymes, 1A2, 2C9, 2C19, 2D6 and 3A4. Inhibition of CYP isozymes can cause drug-drug interactions with severe pharmacological and toxicological consequences. Computational methods for the fast and reliable prediction of the inhibition of CYP isozymes by small molecules are therefore of high interest and relevance to pharmaceutical companies and a host of other industries, including the cosmetics and agrochemical industries. Today, a large number of machine learning models for predicting the inhibition of the major CYP isozymes by small molecules are available. With this work we aim to go beyond the coverage of existing models, by combining data from several major public and proprietary sources. More specifically, we used up to 18815 compounds with measured bioactivities to train random forest classification models for the individual CYP isozymes. A major advantage of the new data collection over existing ones is the better representation of the minority class, the CYP inhibitors. With the new data collection we achieved inhibitor-to-non-inhibitor ratios in the order of 1:1 (CYP1A2) to 1:3 (CYP2D6). We show that our models reach competitive performance on external data, with Matthews correlation coefficients (MCCs) ranging from 0.62 (CYP2C19) to 0.70 (CYP2D6), and areas under the receiver operating characteristic curve (AUCs) between 0.89 (CYP2C19) and 0.92 (CYPs 2D6 and 3A4). Importantly, the models show a high level of robustness, reflected in a good predictivity also for compounds that are structurally dissimilar to the compounds represented in the training data. The best models presented in this work are freely accessible for academic research via a web service.

Machine Learning augmented docking studies of aminothioureas at the SARS-CoV-2-ACE2 interface.

The current pandemic outbreak clearly indicated the urgent need for tools allowing fast predictions of bioactivity of a large number of compounds, either available or at least synthesizable. In the computational chemistry toolbox, several such tools are available, with the main ones being docking and structure-activity relationship modeling either by classical linear QSAR or Machine Learning techniques. In this contribution, we focus on the comparison of the results obtained using different docking protocols on the example of the search for bioactivity of compounds containing N-N-C(S)-N scaffold at the S-protein of SARS-CoV-2 virus with ACE2 human receptor interface. Based on over 1800 structures in the training set we have predicted binding properties of the complete set of nearly 600000 structures from the same class using the Machine Learning Random Forest Regressor approach.

Docking and QSAR of Aminothioureas at the SARS-CoV-2 S-Protein-Human ACE2 Receptor Interface.

Docking of over 160 aminothiourea derivatives at the SARS-CoV-2 S-protein-human ACE2 receptor interface, whose structure became available recently, has been evaluated for its complex stabilizing potency and subsequently subjected to quantitative structure-activity relationship (QSAR) analysis. The structural variety of the studied compounds, that include 3 different forms of the N-N-C(S)-N skeleton and combinations of 13 different substituents alongside the extensive length of the interface, resulted in the failure of the QSAR analysis, since different molecules were binding to different parts of the interface. Subsequently, absorption, distribution, metabolism, and excretion (ADME) analysis on all studied compounds, followed by a toxicity analysis using statistical models for selected compounds, was carried out to evaluate their potential use as lead compounds for drug design. Combined, these studies highlighted two molecules among the studied compounds, i.e., 5-(pyrrol-2-yl)-2-(2-methoxyphenylamino)-1,3,4-thiadiazole and 1-(cyclopentanoyl)-4-(3-iodophenyl)-thiosemicarbazide, as the best candidates for the development of future drugs.

Antibacterial Activity of Fluorobenzoylthiosemicarbazides and Their Cyclic Analogues with 1,2,4-Triazole Scaffold.

The development of drug-resistant bacteria is currently one of the major challenges in medicine. Therefore, the discovery of novel lead structures for the design of antibacterial drugs is urgently needed. In this structure-activity relationship study, a library of ortho-, meta-, and para-fluorobenzoylthiosemicarbazides, and their cyclic analogues with 1,2,4-triazole scaffold, was created and tested for antibacterial activity against Gram-positive bacteria strains. While all tested 1,2,4-triazoles were devoid of potent activity, the antibacterial response of the thiosemicarbazides was highly dependent on substitution pattern at the N4 aryl position. The optimum activity for these compounds was found for trifluoromethyl derivatives such as 15a, 15b, and 16b, which were active against both the reference strains panel, and pathogenic methicillin-sensitive and methicillin-resistant Staphylococcus aureus clinical isolates at minimal inhibitory concentrations (MICs) ranging from 7.82 to 31.25 µg/mL. Based on the binding affinities obtained from docking, the conclusion can be reached that fluorobenzoylthiosemicarbazides can be considered as potential allosteric d-alanyl-d-alanine ligase inhibitors.

Assessment of Nonnucleoside Inhibitors Binding to HIV-1 Reverse Transcriptase Using HYDE Scoring.

In this study, 48 inhibitors were docked to 107 allosteric centers of human immunodeficiency virus 1 (HIV-1) reverse transcriptase from the Protein Data Bank (PDB). Based on the average binding scores, quantitative structure-activity relationship (QSAR) equations were constructed in order to elucidate directions of further development in the design of inhibitors. Such developments, informed by structural data, must have a focus on activity against mutated forms of the enzyme, which are the cause of the emergence of multidrug-resistant viral strains. Docking studies employed the HYDE scoring function. Two types of QSARs have been considered: One based on topological descriptors and the other on structural fragments of the inhibitors. Both methods gave similar results, indicating substructures favoring binding to mutated forms of the enzyme.

What do docking and QSAR tell us about the design of HIV-1 reverse transcriptase nonnucleoside inhibitors?

Despite vigorous studies, effective nonnucleoside inhibitors of HIV-1 reverse transcriptase (NNRTIs) are still in demand, not only due to toxicity and detrimental side effects of currently used drugs but also because of the emergence of multidrug-resistant viral strains. In this contribution, we present results of docking of 47 inhibitors to 107 allosteric centers of HIV-1 reverse transcriptase. Based on the average binding scores, we have constructed QSAR equations to elucidate directions of further developments in the inhibitor design that come from this structural data.

[Cestode infections in Poland in 2000]. / Tasiemczyce w 2000 roku.

In 2000, 439 intestinal cestode infections were registered in Poland. Among them 359 were caused by Taenia saginata, three by T. solium, 52 by Taenia sp., two by Hymenolepis nana, two by Diphyllobothrium latum, and one by Dipylidium caninum. Moreover, 29 cases of echinococcosis were also registered. The obtained results confirmed the low frequency of intestinal cestodoses in Poland.