An Overview of Computer-aided Drug Design Tools and Recent Applications in Designing of Anti-diabetic Agents

Artificial intelligence in antidiabetic drug discovery: The advances in QSAR and the prediction of α-glucosidase inhibitors

Artificial Intelligence is transforming drug discovery, particularly in the hit identification phase of therapeutic compounds. One tool that has been instrumental in this transformation is Quantitative Structure-Activity Relationship (QSAR) analysis. This computer-aided drug design tool uses machine learning to predict the biological activity of new compounds based on the numerical representation of chemical structures against various biological targets. With diabetes mellitus becoming a significant health challenge in recent times, there is intense research interest in modulating antidiabetic drug targets. α-Glucosidase is an antidiabetic target that has gained attention due to its ability to suppress postprandial hyperglycaemia, a key contributor to diabetic complications. This review explored a detailed approach to developing QSAR models, focusing on strategies for generating input variables (molecular descriptors) and computational approaches ranging from classical machine learning algorithms to modern deep learning algorithms. We also highlighted studies that have used these approaches to develop predictive models for α-glucosidase inhibitors to modulate this critical antidiabetic drug target.

Combatting Antibiotic-Resistant Staphylococcus aureus: Discovery of TST1N-224, a Potent Inhibitor Targeting Response Regulator VraRC, through Pharmacophore-Based Screening and Molecular Characterizations

Staphylococcus aureus (S. aureus) is a major global health concern, causing various infections and presenting challenges due to antibiotic resistance. In particular, methicillin-resistant S. aureus, vancomycin-intermediate S. aureus (VISA), and vancomycin-resistant S. aureus pose significant obstacles in treating S. aureus infections. Therefore, the critical need for novel drugs to counter these resistant forms is pressing. Two-component systems (TCSs), integral to bacterial regulation, offer promising targets for disruption. In this study, a comprehensive approach, involving pharmacophore-based inhibitor screening, along with biochemical and biophysical analyses were conducted to identify, characterize, and validate potential inhibitors targeting the response regulator VraRC of S. aureus. The constructed pharmacophore model, Phar-VRPR-N3, demonstrated effectiveness in identifying a potent inhibitor, TST1N-224 (IC50 = 60.2 ± 4.0 μM), against the formation of the VraRC-DNA complex. Notably, TST1N-224 exhibited strong binding to VraRC (KD = 23.4 ± 1.2 μM) using a fast-on-fast-off binding mechanism. Additionally, NMR-based molecular modeling revealed that TST1N-224 predominantly interacts with the α9- and α10-helixes of the DNA-binding domain of VraR, where the interactive and functionally essential residues (N165, K180, S184, and R195) act as hotspots for structure-based inhibitor optimization. Furthermore, TST1N-224 evidently enhanced the susceptibility of VISA to both vancomycin and methicillin. Importantly, TST1N-224 distinguished by 1,2,5,6-tetrathiocane with the 3 and 8 positions modified with ethanesulfonates holds significant potential as a lead compound for the development of new antimicrobial agents.

Current perspectives and trend of computer-aided drug design: a review and bibliometric analysis

AIM

Computer-aided drug design (CADD) is a drug design technique for computing ligand-receptor interactions and is involved in various stages of drug development. To better grasp the frontiers and hotspots of CADD, we conducted a review analysis through bibliometrics.

METHODS

A systematic review of studies published between 2000 and 20 July 2023 was conducted following the PRISMA guidelines. Literature on CADD was selected from the Web of Science Core Collection. General information, publications, output trends, countries/regions, institutions, journals, keywords, and influential authors were visually analyzed using software such as Excel, VOSviewer, RStudio, and CiteSpace.

RESULTS

A total of 2031 publications were included. These publications primarily originated from 99 countries or regions led by the U.S. and China. Among the contributors, MacKerell AD had the highest number of articles and the greatest influence. The Journal of Medicinal Chemistry was the most cited journal, whereas the Journal of Chemical Information and Modeling had the highest number of publications.

CONCLUSIONS

Influential authors in the field were identified. Current research shows active collaboration between countries, institutions, and companies. CADD technologies such as homology modeling, pharmacophore modeling, quantitative conformational relationships, molecular docking, molecular dynamics simulation, binding free energy prediction, and high-throughput virtual screening can effectively improve the efficiency of new drug discovery. Artificial intelligence-assisted drug design and screening based on CADD represent key topics that will influence future development. Furthermore, this paper will be helpful in better understanding the frontiers and hotspots of CADD.

Integrated fragment-based drug design and virtual screening techniques for exploring the antidiabetic potential of thiazolidine-2,4-diones: Design, synthesis and in vivo studies

Diabetes mellitus is a metabolic disorder characterized by elevated blood sugar levels and related complications. This study focuses on harnessing and integrating fragment-based drug design and virtual screening techniques to explore the antidiabetic potential of newly synthesized thiazolidine-2,4-dione derivatives. The research involves the design of novel variations of thiazolidine-2,4-dione compounds by Fragment-Based Drug Design. The screening process involves pharmacophore based virtual screening through docking algorithms, and the identification of newly twelve top-scoring compounds. The molecular docking analysis revealed that compounds SP4e, SP4f showed highest docking scores of -9.082 and -10.345. The binding free energies of the compounds SP4e, SP4f and pioglitazone was found to be -19.9, -16.1 and -13 respectively, calculated using the Prime MM/GBSA approach. The molecular dynamic study validates the docking results. Furthermore, In the Swiss albino mice model, both SP4e and SP4f exhibited significant hypoglycaemic effects, comparable to the reference drug pioglitazone. Furthermore, these compounds demonstrated favorable effects on the lipid profile, reducing total cholesterol, triglycerides, and LDL levels while increasing HDL levels. In mice tissue, the disease control group showed PPAR-γ expression of 4.200 ± 0.24, while compound SP4f displayed higher activation at 7.84 ± 0.431 compared to compound SP4e with an activation of 7.68 ± 0.65. In zebrafish model, SP4e and SP4f showed significant reductions in blood glucose levels and lipid peroxidation, along with increased glutathione levels and catalase activity. These findings highlighted the potential of SP4e and SP4f as antidiabetic agents, warranting further exploration for therapeutic applications. The in vitro study was performed in HEK-2 cell line, the pioglitazone group demonstrated PPAR-γ expression of EC50 = 575.2, while compound SP4f exhibited enhanced activation at EC50 = 739.0 in contrast to compound SP4e activation of EC50 = 826.7.

Identification of quality markers for Cyanotis arachnoidea and analysis of its physiological mechanism based on chemical pattern recognition, network pharmacology, and experimental validation

Cyanotis arachnoidea C. B. Clarke is a traditional Chinese medicinal herb that has a limited clinical use in the treatment of diabetes mellitus (DM) in minority areas of Guizhou in China. However, few prior reports are available on the quality control of Cyanotis arachnoidea, and its quality markers and hypoglycemic mechanism are still unclear. The purpose of this study is to explore the quality markers (Q-markers) of Cyanotis arachnoidea and predict its hypoglycemic mechanism. In this study, ultra-high-performance liquid chromatography (UHPLC) fingerprint combined with chemical pattern recognition were performed, and four differential components were screened out as quality markers, including 20-Hydroxyecdysone, 3-O-acetyl-20-hydroxyecdysone, Ajugasterone C, and 2-O-acetyl-20-hydroxyecdysone. Network pharmacology analysis revealed 107 therapeutic target genes of Cyanotis arachnoidea in DM treatment, and the key targets were Akt1, TNF, IL-6, MAPK3, and JUN. The hypoglycemic mode of action of Cyanotis arachnoidea may be mediated by tumor necrosis factor (TNF) signaling, cancer, insulin resistance, and JAK-STAT pathways. Molecular docking analysis disclosed that the foregoing quality markers effectively bound their key target genes. An in vitro experiment conducted on pancreatic islet β-cells indicated that the forenamed active components of Cyanotis arachnoidea had hypoglycemic efficacy by promoting PI3K/Akt and inhibiting MAPK signaling. UHPLC also accurately quantified the quality markers. The identification and analysis of quality markers for Cyanotis arachnoidea is expected to provide references for the establishment of a quality control evaluation system and clarify the material basis and hypoglycemic mechanisms of this traditional Chinese medicine (TCM).

Virtual screening and activity evaluation of multitargeting inhibitors for idiopathic pulmonary fibrosis

Transforming growth factor β receptor (TGF-β1R) and receptor tyrosine kinases (RTKs), such as VEGFRs, PDGFRs and FGFRs are considered important therapeutic targets in blocking myofibroblast migration and activation of idiopathic pulmonary fibrosis (IPF). To screen and design innovative prodrug to simultaneously target these four classes of receptors, we proposed an approach based on network pharmacology combining virtual screening and machine learning activity prediction, followed by efficient in vitro and in vivo models to evaluate drug activity. We first constructed Collagen1A2-A549 cells with type I collagen as the main biomarker and evaluated the activity of compounds to inhibit collagen expression at the cellular level. The data from the first round of Collagen1A2-A549 cell screening were substituted into the machine learning model, and the model was optimized accordingly. As a result, the false positive rate of the model was reduced from 85.0% to 66.7%, and two prospective compounds, Z103080500 and Z104578368, were finally selected. Collagen levels were reduced effectively by both Z103080500 (67.88% reduction) and Z104578368 (69.54% reduction). Moreover, these two compounds showed low cellular cytotoxicity. Subsequently, the effect of Z103080500 and Z104578368 was evaluated in a bleomycin-induced C57BL/6 mouse IPF model. These results showed that 50 mg/kg Z103080500 and Z104578368 could effectively reduce the number of inflammatory cells and the expression level of α-SMA. Meanwhile, Z103080500 and Z104578368 reduced the expression of major markers and inflammatory factors of IPF, such as collagen, IFN-γ, IL-17 and HYP, indicating that these screened Z103080500 and Z104578368 effectively delayed lung tissue inflammation and had a potential therapeutic effect on IPF. Our findings demonstrate that a screening and evaluation model for prodrug against IPF has been successfully established. It is of great significance to further modify these compounds to enhance their potency and activity.