Molecular docking-based classification and systematic QSAR analysis of indoles as Pim kinase inhibitors

Developing reliable classification of dual IDO1/TDO inhibitors using data fusion and majority voting

Indoleamine 2,3-dioxygenase 1 (IDO1) and tryptophan 2,3-dioxygenase (TDO) are promising dual-targeting inhibitors in cancer and neurodegenerative diseases treatment. Data fusion of receptor-based and ligand-based information of dual IDO1/TDO inhibitors were employed for active/inactive classification performance. A reliable decision making procedure was used here to identify active/inactive dual IDO1/TDO inhibitors using majority voting method and pools of individual classifications instead of individual models. All classification models were validated using prediction set, cross-validation and y-scrambling tests. The classification outcomes indicate that the sensitivity, specificity, precision, accuracy, G-mean and F1 score values increases up to ∼90% using data fusion and majority voting method. Compare to individual classification models with a single prediction point, the majority voting method has more reliable results due to the integration of the pool of individual classification models. This classification strategy may lead to more reliable identification of active/inactive dual-targeting inhibitors in cancer immunotherapy.Communicated by Ramaswamy H. Sarma.

Hybrid consensus and k-nearest neighbours (kNN) strategies to classify dual BRD4/PLK1 inhibitors

A novel decision-making procedure is proposed here for the first time to identify active/inactive and selective/non-selective dual inhibitors using consensus approaches and pools of k-nearest neighbours (kNN) classifications instead of individual models. Dual BRD4/PLK1 inhibition with adequate selectivity is a potential therapeutic strategy for targeting tumour cells in high-risk patients. We report the unique way to identify both active and selective dual BRD4/PLK1 inhibitors using consensus and kNN strategies together with two sources of receptor-based and ligand-based information which are the ranked binding energies of residues and important molecular features, respectively. The results of consensus approaches were compared with the results of individual kNN models. The chemical space similarity was measured using three different distance functions to increase the reliability. All activity and selectivity classification models were validated using cross-validation and y-randomization tests. The outcomes show that consensus approaches can increase the reliability and accuracy of active/inactive or selective/non-selective detections up to 90%. Consensus approaches also reached more balanced values of sensitivity and specificity compared to the individual kNN models because of the compensation in the integration of diverse sources of information.

Discovery of benzothiazole-based thiazolidinones as potential anti-inflammatory agents: anti-inflammatory activity, soybean lipoxygenase inhibition effect and molecular docking studies

Despite the greatest achievement in the development of anti-inflammatory agents in the last two decades, the current clinical drugs suffer from a variety of complications in community settings and hospital. There is still an urgent need to design novel molecules with better safety profile and with different molecular targets from those in current clinical use. The aim of this research was to discover a series of benzothiazole-based thiazolidinones with lipoxygenase (LOX) inhibitory activity as a mechanism of anti-inflammatory action. Carrageenan-induced mouse foot paw oedema assay was carried out to determine the anti-inflammatory activity, while LOX inhibition was examined through the conversion of sodium linoleate to 13-hydroperoxylinoleic acid. Molecular docking studies were performed using AutoDock 4.2 software. The anti-inflammatory activity of the title compounds was determined in a range of 18.4%-69.57%, where compound #3 was found to be the most potent (69.57%) and also to be more active than the reference drug indomethacin (47%). Moreover, compound #3 showed the highest LOX inhibitory activity with IC50 of 13 μM being less potent to that of the reference NDGA (IC50 = 1.3 μM). Compound #3 has been identified as lead compound for further modification in an attempt to improve anti-inflammatory and LOX inhibitory activities.

Caffeic and 3-(3,4-dihydroxyphenyl)glyceric acid derivatives as antimicrobial agent: biological evaluation and molecular docking studies

Herein we report the evaluation of the antimicrobial activity of some previously synthesized 3-(3,4-dihydroxyphenyl)glyceric acid in benzylated and in free 3,4 hydroxy groups in catechol moiety along with some caffeic and 3-(3,4-dihydroxyphenyl)glyceric acid amides using the microdilution method. The evaluation revealed that compounds showed in general moderate to low activity with MIC in range of 0.36-4.5 mg/mL. Compounds were also studied against three resistant bacteria strains MRSA (Methicillin-resistant Staphylococcus aureus), E. coli and P. aeruginosa. Seven out of ten compounds were more potent than reference drugs ampicillin and streptomycin against MRSA, while against another two resistant strains seven compounds showed low activity and the rest were inactive. Antifungal activity of the tested compounds was much better than antibacterial, with MIC in the range of 0.019-3.0 mg/mL. Compounds #7 and 15 showed good activity against all fungi tested, being more potent than ketoconazole and in some case even better than bifonazole used as reference drugs. Docking studies revealed that the most active compound #7 binds to the haem group of the enzyme in the same way as ketoconazole.

Oncological drug discovery: AI meets structure-based computational research

The integration of machine learning and structure-based methods has proven valuable in the past as a way to prioritize targets and compounds in early drug discovery. In oncological research, these methods can be highly beneficial in addressing the diversity of neoplastic diseases portrayed by the different hallmarks of cancer. Here, we review six use case scenarios for integrated computational methods, namely driver prediction, computational mutagenesis, (off)-target prediction, binding site prediction, virtual screening, and allosteric modulation analysis. We address the heterogeneity of integration approaches and individual methods, while acknowledging their current limitations and highlighting their potential to bring drugs for personalized oncological therapies to the market faster.

Ranked binding energies of residues and data fusion to identify the active and selective pyrimidine-based Janus kinases 3 (JAK3) inhibitors

The idea of using ranked binding energies of residues and data fusion are presented here for the first time as a valuable tool to classify active and selective inhibitors. Selective inhibitors of JAK3 can inhibit inflammatory cytokine while preventing targeting other subtypes of JAK1 and JAK2. Herein, we report a novel way to identify both active JAK3 and selective JAK1/JAK3 and JAK2/JAK3 inhibitors using the effective activity and selectivity classifications. The most important residues (top 10) responsible for the inhibition mechanism are sorted from high to low energies, which are considered as variables in the classification process. In addition, the ranked energies of ligands' heteroatoms (top 5), ranked energies of hydrogen bonds (top 5) and important molecular descriptors (top 10) were used to construct different data fusion possibilities. It is shown that the proposed data fusion strategy can increase the accuracy of the activity classification to 100% and the selectivity classification to 96.4%. The proposed strategies represented in this paper can help medicinal or pharmaceutical chemist in evaluation of both active and selective inhibitors before synthesizing new pharmaceuticals.