Carbazole derivatives containing chalcone analogues targeting topoisomerase II inhibition: First principles characterization and QSAR modelling

Evaluation of the Antiparasitic and Antifungal Activities of Synthetic Piperlongumine-Type Cinnamide Derivatives: Booster Effect by Halogen Substituents

A series of synthetic N-acylpyrrolidone and -piperidone derivatives of the natural alkaloid piperlongumine were prepared and tested for their activities against Leishmania major and Toxoplasma gondii parasites. Replacement of one of the aryl meta-methoxy groups by halogens such as chlorine, bromine and iodine led to distinctly increased antiparasitic activities. For instance, the new bromo- and iodo-substituted compounds 3 b/c and 4 b/c showed strong activity against L. major promastigotes (IC50 =4.5-5.8 μM). Their activities against L. major amastigotes were moderate. In addition, the new compounds 3 b, 3 c, and 4 a-c exhibited high activity against T. gondii parasites (IC50 =2.0-3.5 μM) with considerable selectivities when taking their effects on non-malignant Vero cells into account. Notable antitrypanosomal activity against Trypanosoma brucei was also found for 4 b. Antifungal activity against Madurella mycetomatis was observed for compound 4 c at higher doses. Quantitative structure-activity relationship (QSAR) studies were carried out, and docking calculations of test compounds bound to tubulin revealed binding differences between the 2-pyrrolidone and 2-piperidone derivatives. Microtubules-destabilizing effects were observed for 4 b in T. b. brucei cells.

Improved Machine Learning Models by Data Processing for Predicting Life-Cycle Environmental Impacts of Chemicals

Machine learning (ML) provides an efficient manner for rapid prediction of the life-cycle environmental impacts of chemicals, but challenges remain due to low prediction accuracy and poor interpretability of the models. To address these issues, we focused on data processing by using a mutual information-permutation importance (MI-PI) feature selection method to filter out irrelevant molecular descriptors from the input data, which improved the model interpretability by preserving the physicochemical meanings of original molecular descriptors without generation of new variables. We also applied a weighted Euclidean distance method to mine the data most relevant to the predicted targets by quantifying the contribution of each feature, thereby the prediction accuracy was improved. On the basis of above data processing, we developed artificial neural network (ANN) models for predicting the life-cycle environmental impacts of chemicals with R² values of 0.81, 0.81, 0.84, 0.75, 0.73, and 0.86 for global warming, human health, metal depletion, freshwater ecotoxicity, particulate matter formation, and terrestrial acidification, respectively. The ML models were interpreted using the Shapley additive explanation method by quantifying the contribution of each input molecular descriptor to environmental impact categories. This work suggests that the combination of feature selection by MI-PI and source data selection based on weighted Euclidean distance has a promising potential to improve the accuracy and interpretability of the models for predicting the life-cycle environmental impacts of chemicals.

Implicitly perturbed Hamiltonian as a class of versatile and general-purpose molecular representations for machine learning

Unraveling challenging problems by machine learning has recently become a hot topic in many scientific disciplines. For developing rigorous machine-learning models to study problems of interest in molecular sciences, translating molecular structures to quantitative representations as suitable machine-learning inputs play a central role. Many different molecular representations and the state-of-the-art ones, although efficient in studying numerous molecular features, still are suboptimal in many challenging cases, as discussed in the context of the present research. The main aim of the present study is to introduce the Implicitly Perturbed Hamiltonian (ImPerHam) as a class of versatile representations for more efficient machine learning of challenging problems in molecular sciences. ImPerHam representations are defined as energy attributes of the molecular Hamiltonian, implicitly perturbed by a number of hypothetic or real arbitrary solvents based on continuum solvation models. We demonstrate the outstanding performance of machine-learning models based on ImPerHam representations for three diverse and challenging cases of predicting inhibition of the CYP450 enzyme, high precision, and transferrable evaluation of non-covalent interaction energy of molecular systems, and accurately reproducing solvation free energies for large benchmark sets.

Molecular representations are fundamental tools for machine-learning models. The current work introduces a new set of molecular representations demonstrated to enable accurate predictions of molecular conformational energy and solvation free energy.

p-Trifluoromethyl- and p-pentafluorothio-substituted curcuminoids of the 2,6-di[(E)-benzylidene)]cycloalkanone type: Syntheses and activities against Leishmania major and Toxoplasma gondii parasites

A series of the title curcuminoids with structural variance in the heteroatom of the cycloalkanone and the p-substituents of the phenyl rings were tested for their activities against Leishmania major and Toxoplasma gondii parasites. The majority of them showed high activities against both parasite forms with EC₅₀ values in the sub-micromolar concentration range. Bis(p-pentafluorothio)-substituted 3,5-di[(E)-benzylidene]piperidin-4-one 1b was not just noticeable antiparasitic, but also exhibited a considerable selectivity for L. major promastigotes over normal Vero cells. While derivatives differing only in the p-phenyl substituents being CF₃ or SF₅ showed similar antiparasitic activities, the cyclic ketone hub was more decisive both for the anti-parasitic activities and the selectivities for the parasites vs. normal cells. QSAR calculations confirmed the observed structure-activity relations and suggested structural variations for a further improvement of the antiparasitic activity. Docking studies based on DFT calculations revealed L. major pteridine reductase 1 as a likely molecular target protein of the title compounds.

Structural, QSAR, machine learning and molecular docking studies of 5-thiophen-2-yl pyrazole derivatives as potent and selective cannabinoid-1 receptor antagonists

We performed a structural study followed by theoretical analysis of the chemical descriptors and biological activity of a series of 5-thiophen-2-yl pyrazole derivatives as potent and selective cannabinoid-1 (CB1) receptor antagonists.