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Artificial intelligence (AI) has permeated various sectors, including the pharmaceutical industry and research, where it has been utilized to efficiently identify new chemical entities with desirable properties. The application of AI algorithms to drug discovery presents both remarkable opportunities and challenges. This review article focuses on the transformative role of AI in medicinal chemistry. We delve into the applications of machine learning and deep learning techniques in drug screening and design, discussing their potential to expedite the early drug discovery process. In particular, we provide a comprehensive overview of the use of AI algorithms in predicting protein structures, drug–target interactions, and molecular properties such as drug toxicity. While AI has accelerated the drug discovery process, data quality issues and technological constraints remain challenges. Nonetheless, new relationships and methods have been unveiled, demonstrating AI’s expanding potential in predicting and understanding drug interactions and properties. For its full potential to be realized, interdisciplinary collaboration is essential. This review underscores AI’s growing influence on the future trajectory of medicinal chemistry and stresses the importance of ongoing synergies between computational and domain experts.

Metformin (MetF) is used worldwide as a first-line therapy for type 2 diabetes. Recently, interest in the pleiotropic effects of MetF, such as its anticancer and antiaging properties, has increased. However, the molecular target of MetF and the detailed mechanism underlying its ability to inhibit cell growth through autophagy induction remain incompletely understood. In this study, using an innovative label-free drug affinity responsive target stability (DARTS)-LC-MS/MS method, we discovered that mitochondrial voltage-dependent anion channel 1 (VDAC1) is a novel binding protein involved in the induction of autophagy-related cell death by high-dose MetF in hepatocellular carcinoma (HCC). Computational alanine scanning mutagenesis revealed that MetF and VDAC1 (D9, E203) interact electrostatically. MetF disrupts the IP 3 R-GRP75-VDAC1 complex, which plays a key role in stabilizing mitochondria-associated ER membranes (MAMs), by binding to VDAC1. This disruption leads to increased cytosolic calcium levels, thereby contributing to autophagy induction. MetF also decreased the AMP/ATP ratio and activated the AMPK pathway. Cells with genetic knockdown of VDAC1 mimicked the activity of MetF. In conclusion, this study provides new insights into the involvement of MetF in ionic interactions with VDAC1, contributing to its anticancer effects in HCC. These findings help elucidate the diverse biological and pharmacological effects of MetF, particularly its influence on autophagy, as well as the potential of MetF as a therapeutic agent for diseases characterized by VDAC1 overexpression. Metformin Targets VDAC1 to Induce Autophagy in Cancer Metformin, a common type 2 diabetes drug, is known for its glucose-lowering effects. The study used several cell lines and advanced techniques to investigate how Metformin induces cancer cell death. This experimental research included cell cultures and molecular analysis. They found that Metformin targets a mitochondrial protein called VDAC1. This interaction disrupts energy production and increases autophagy, leading to cancer cell death. The results showed that Metformin binds to VDAC1, reducing mitochondrial calcium and ATP levels, which activates autophagy and kills cancer cells. The researchers concluded that the ionic interaction of Metformin with VDAC1 is critical for its anticancer effects. Future studies could explore Metformin as a treatment for cancers with high VDAC1 expression. This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.

Our recent studies have identified p21-activated kinase 4 (PAK4) as a key regulator of lipid catabolism in the liver and adipose tissue, but its role in glucose homeostasis in skeletal muscle remains to be explored. In this study, we find that PAK4 levels are highly upregulated in the skeletal muscles of diabetic humans and mice. Skeletal muscle-specific Pak4 ablation or administering the PAK4 inhibitor in diet-induced obese mice retains insulin sensitivity, accompanied by AMPK activation and GLUT4 upregulation. We demonstrate that PAK4 promotes insulin resistance by phosphorylating AMPKα2 at Ser491, thereby inhibiting AMPK activity. We additionally show that skeletal muscle-specific expression of a phospho-mimetic mutant AMPKα2 S491D impairs glucose tolerance, while the phospho-inactive mutant AMPKα2 S491A improves it. In summary, our findings suggest that targeting skeletal muscle PAK4 may offer a therapeutic avenue for type 2 diabetes. While AMPKα is known for its role in regulating GLUT4 trafficking and subsequent glucose uptake into cells, its upstream regulators are not well understood. The authors provide evidence that PAK4 acts as an inhibitory kinase for AMPKα, thus impairing glucose uptake in skeletal muscle.

A tokamak, a torus-shaped nuclear fusion device, needs an electric current in the plasma to produce magnetic field in the poloidal direction for confining fusion plasmas. Plasma current is conventionally generated by electromagnetic induction. However, for a steady-state fusion reactor, minimizing the inductive current is essential to extend the tokamak operating duration. Several non-inductive current drive schemes have been developed for steady-state operations such as radio-frequency waves and neutral beams. However, commercial reactors require minimal use of these external sources to maximize the fusion gain, Q, the ratio of the fusion power to the external power. Apart from these external current drives, a self-generated current, so-called bootstrap current, was predicted theoretically and demonstrated experimentally. Here, we reveal another self-generated current that can exist in a tokamak and this has not yet been discussed by present theories. We report conclusive experimental evidence of this self-generated current observed in the KSTAR tokamak. Fusion devices like tokamaks require plasma current to generate magnetic field for plasma confinement. Here the authors report an observation of a self-generated anomalous current that contributes up to 30% of the total current in the fusion plasma at KSTAR.

Artificial intelligence is rapidly transforming quality assurance in healthcare, driving advancements in diagnostics, surgery, and patient care. This review presents a comprehensive analysis of artificial intelligence integration—particularly convolutional and recurrent neural networks—across key clinical domains, significantly enhancing diagnostic accuracy, surgical performance, and pathology evaluation. Artificial intelligence-based approaches have demonstrated clear superiority over conventional methods: convolutional neural networks achieved 91.56% accuracy in scanner fault detection, surpassing manual inspections; endoscopic lesion detection sensitivity rose from 2.3% to 6.1% with artificial intelligence assistance; and gastric cancer invasion depth classification reached 89.16% accuracy, outperforming human endoscopists by 17.25%. In pathology, artificial intelligence achieved 93.2% accuracy in identifying out-of-focus regions and an F1 score of 0.94 in lymphocyte quantification, promoting faster and more reliable diagnostics. Similarly, artificial intelligence improved surgical workflow recognition with over 81% accuracy and exceeded 95% accuracy in skill assessment classification. Beyond traditional diagnostics and surgical support, AI-powered wearable sensors, drug delivery systems, and biointegrated devices are advancing personalized treatment by optimizing physiological monitoring, automating care protocols, and enhancing therapeutic precision. Despite these achievements, challenges remain in areas such as data standardization, ethical governance, and model generalizability. Overall, the findings underscore artificial intelligence’s potential to outperform traditional techniques across multiple parameters, emphasizing the need for continued development, rigorous clinical validation, and interdisciplinary collaboration to fully realize its role in precision medicine and patient safety.

Fusarium head blight (FHB) caused by infection with leads to enormous losses to crop growers, and may contaminate grains with a number of Fusarium mycotoxins that pose serious risks to human and animal health. Antagonistic bacteria that are used to prevent FHB offer attractive alternatives or supplements to synthetic fungicides for controlling FHB without the negative effects of chemical management. Out of 500 bacterial strains isolated from soil, JCK-12 showed strong antifungal activity and was considered a potential source for control strategies to reduce FHB. JCK-12 produces several cyclic lipopeptides (CLPs) including iturin A, fengycin, and surfactin. Iturin A inhibits spore germination of Fengycin or surfactin alone did not display any inhibitory activity against spore germination at concentrations less than 30 μg/ml, but a mixture of iturin A, fengycin, and surfactin showed a remarkable synergistic inhibitory effect on spore germination. The fermentation broth and formulation of JCK-12 strain reduced the disease incidence of FHB in wheat. Furthermore, co-application of JCK-12 and chemical fungicides resulted in synergistic antifungal effects and significant disease control efficacy against FHB under greenhouse and field conditions, suggesting that JCK-12 has a strong chemosensitizing effect. The synergistic antifungal effect of JCK-12 and chemical fungicides in combination may result from the cell wall damage and altered cell membrane permeability in the phytopathogenic fungi caused by the CLP mixtures and subsequent increased sensitivity of to fungicides. In addition, JCK-12 showed the potential to reduce trichothecenes mycotoxin production. The results of this study indicate that JCK-12 could be used as an available biocontrol agent or as a chemosensitizer to chemical fungicides for controlling FHB disease and as a strategy for preventing the contamination of harvested crops with mycotoxins.

Dynamics and functions of G-protein coupled receptors (GPCRs) are accurately regulated by the type of ligands that bind to the orthosteric or allosteric binding sites. To glean the structural and dynamical origin of ligand-dependent modulation of GPCR activity, we performed total ~ 5 μsec molecular dynamics simulations of A2A adenosine receptor (A2AAR) in its apo, antagonist-bound, and agonist-bound forms in an explicit water and membrane environment, and examined the corresponding dynamics and correlation between the 10 key structural motifs that serve as the allosteric hotspots in intramolecular signaling network. We dubbed these 10 structural motifs \"binary switches\" as they display molecular interactions that switch between two distinct states. By projecting the receptor dynamics on these binary switches that yield 2(10) microstates, we show that (i) the receptors in apo, antagonist-bound, and agonist-bound states explore vastly different conformational space; (ii) among the three receptor states the apo state explores the broadest range of microstates; (iii) in the presence of the agonist, the active conformation is maintained through coherent couplings among the binary switches; and (iv) to be most specific, our analysis shows that W246, located deep inside the binding cleft, can serve as both an agonist sensor and actuator of ensuing intramolecular signaling for the receptor activation. Finally, our analysis of multiple trajectories generated by inserting an agonist to the apo state underscores that the transition of the receptor from inactive to active form requires the disruption of ionic-lock in the DRY motif.

Adenosine mediates various physiological activities in the body. Adenosine receptors (ARs) are widely expressed in tumors and the tumor microenvironment (TME), and they induce tumor proliferation and suppress immune cell function. There are four types of human adenosine receptor (hARs): hA1, hA2A, hA2B, and hA3. Both hA1 and hA3 AR play an important role in tumor proliferation. We designed and synthesized novel 1,3,5-triazine derivatives through amination and Suzuki coupling, and evaluated them for binding affinities to each hAR subtype. Compounds 9a and 11b showed good binding affinity to both hA1 and hA3 AR, while 9c showed the highest binding affinity to hA1 AR. In this study, we discovered that 9c inhibits cell viability, leading to cell death in lung cancer cell lines. Flow cytometry analysis revealed that 9c caused an increase in intracellular reactive oxygen species (ROS) and a depolarization of the mitochondrial membrane potential. The binding mode of 1,3,5-triazine derivatives to hA1 and hA3 AR were predicted by a molecular docking study.

The metabolic stability of a drug is a crucial determinant of its pharmacokinetic properties, including clearance, half-life, and oral bioavailability. Accurate predictions of metabolic stability can significantly streamline the drug discovery process. In this study, we present MetaboGNN , an advanced model for predicting liver metabolic stability based on Graph Neural Networks (GNNs) and Graph Contrastive Learning (GCL). Using a high-quality dataset from the 2023 South Korea Data Challenge for Drug Discovery, which comprises 3,498 training molecules and 483 test molecules, we presented molecular structures as graphs to capture the intricate structural relationships that influence metabolic stability. A GCL-driven pretraining step was employed to enhance model generalizability by learning robust, transferable graph-level representations. Notably, incorporating interspecies differences between human liver microsomes (HLM) and mouse liver microsomes (MLM) further improved predictive accuracy, achieving Root Mean Square Error (RMSE) values of 27.91 (HLM) and 27.86 (MLM), both expressed as the percentage of parent compound remaining after a 30-min incubation. Compared to traditional approaches, MetaboGNN demonstrates superior predictive performance and highlights the importance of considering interspecies enzymatic variations. In addition, attention-based analysis identified key molecular fragments associated with metabolic stability, highlighting chemically meaningful structural determinants. These findings establish MetaboGNN as a powerful tool for metabolic stability prediction, supporting more efficient lead optimization processes in drug discovery. Scientific contribution This study presents MetaboGNN , a novel predictive framework for liver metabolic stability that integrates Graph Neural Networks with Graph Contrastive Learning. By explicitly incorporating interspecies metabolic differences as a dedicated learning target, MetaboGNN achieved enhanced prediction accuracy. Attention-based analysis also identified molecular fragments that are strongly associated with stabilizing or destabilizing effects, facilitating chemical insights during lead optimization.