Design, synthesize and antiurease activity of novel thiazole derivatives: Machine learning, molecular docking and biological investigation

Machine Learning-Driven Classification of Urease Inhibitors Leveraging Physicochemical Properties as Effective Filter Criteria

Urease, a pivotal enzyme in nitrogen metabolism, plays a crucial role in various microorganisms, including the pathogenic Helicobacter pylori. Inhibiting urease activity offers a promising approach to combating infections and associated ailments, such as chronic kidney diseases and gastric cancer. However, identifying potent urease inhibitors remains challenging due to resistance issues that hinder traditional approaches. Recently, machine learning (ML)-based models have demonstrated the ability to predict the bioactivity of molecules rapidly and effectively. In this study, we present ML models designed to predict urease inhibitors by leveraging essential physicochemical properties. The methodological approach involved constructing a dataset of urease inhibitors through an extensive literature search. Subsequently, these inhibitors were characterized based on physicochemical properties calculations. An exploratory data analysis was then conducted to identify and analyze critical features. Ultimately, 252 classification models were trained, utilizing a combination of seven ML algorithms, three attribute selection methods, and six different strategies for categorizing inhibitory activity. The investigation unveiled discernible trends distinguishing urease inhibitors from non-inhibitors. This differentiation enabled the identification of essential features that are crucial for precise classification. Through a comprehensive comparison of ML algorithms, tree-based methods like random forest, decision tree, and XGBoost exhibited superior performance. Additionally, incorporating the "chemical family type" attribute significantly enhanced model accuracy. Strategies involving a gray-zone categorization demonstrated marked improvements in predictive precision. This research underscores the transformative potential of ML in predicting urease inhibitors. The meticulous methodology outlined herein offers actionable insights for developing robust predictive models within biochemical systems.

Clodronic Acid has Strong Inhibitory Interactions with the Urease Enzyme of Helicobacter pylori: Computer-aided Design and in vitro Confirmation

BACKGROUND

Helicobacter pylori (HP) infection could lead to various gastrointestinal diseases. Urease is the most important virulence factor of HP. It protects the bacterium against gastric acid.

OBJECTIVE

Therefore, we aimed to design urease inhibitors as drugs against HP infection.

METHODS

The DrugBank-approved library was assigned with 3D conformations and the structure of the urease was prepared. Using a re-docking strategy, the proper settings were determined for docking by PyRx and GOLD software. Virtual screening was performed to select the best inhibitory drugs based on binding affinity, FitnessScore, and binding orientation to critical amino acids of the active site. The best inhibitory drug was then evaluated by IC50 and the diameter of the zone of inhibition for bacterial growth.

RESULTS

The structures of prepared drugs were screened against urease structure using the determined settings. Clodronic acid was determined to be the best-identified drug, due to higher PyRx binding energy, better GOLD FitnessScore, and interaction with critical amino acids of urease. In vitro results were also in line with the computational data. IC50 values of Clodronic acid and Acetohydroxamic Acid (AHA) were 29.78 ± 1.13 and 47.29 ± 2.06 μg/ml, respectively. Diameters of the zones of inhibition were 18 and 15 mm for Clodronic acid and AHA, respectively.

CONCLUSION

Clodronic acid has better HP urease inhibition potential than AHA. Given its approved status, the development of a repurposed drug based on Clodronic acid would require less time and cost. Further, in vivo studies would unveil the efficacy of Clodronic acid as a urease inhibitor.

Using Machine Learning and Molecular Docking to Leverage Urease Inhibition Data for Virtual Screening

Urease is a metalloenzyme that catalyzes the hydrolysis of urea, and its modulation has an important role in both the agricultural and medical industry. Even though numerous molecules have been tested against ureases of different species, their clinical translation has been limited due to chemical and metabolic stability as well as side effects. Therefore, screening new compounds against urease would be of interest in part due to rising concerns regarding antibiotic resistance. In this work, we collected and curated a diverse set of 2640 publicly available small-molecule inhibitors of jack bean urease and developed a classifier using a random forest machine learning method with high predictive performance. In addition, the physicochemical features of compounds were paired with molecular docking and protein-ligand fingerprint analysis to gather insight into the current activity landscape. We observed that the docking score could not differentiate active from inactive compounds within each chemical family, but scores were correlated with compound activity when all compounds were considered. Additionally, a decision tree model was built based on 2D and 3D Morgan fingerprints to mine patterns of the known active-class compounds. The final machine learning model showed good prediction performance against the test set (81% and 77% precision for active and inactive compounds, respectively). Finally, this model was employed, as a proof-of-concept, on an in-house library to predict new hits that were then tested against urease and found to be active. This is, to date, the largest, most diverse dataset of compounds used to develop predictive in silico models. Overall, the results highlight the usefulness of using machine learning classifiers and molecular docking to predict novel urease inhibitors.

The role of machine learning method in the synthesis and biological ınvestigation of heterocyclic compounds

Machine learning (ML) methods have attracted increasing interest in chemistry as in all fields of science in recent years. This method is of great importance for the design of targeted bioactive compounds, especially by avoiding loss of time, money, and chemicals. There are lots of online web-based platforms such as LibSVM and OCHEM for the application of ML methods. In this paper, it has been examined the literature data on the activity predictions of heterocyclic compounds, biological activity results such as antiurease, HIV-1 Integrase, E. Coli DNA Gyrase B, and antifungal, pharmacophore-based studies, synthesis, and finding possible inhibitors using different machine learning methods.