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Abstract Conventional drug discovery is expensive, time-consuming, and prone to failure. Artificial intelligence has become a potent substitute over the last decade, providing strong answers to challenging biological issues in this field. Among these difficulties, drug-target binding (DTB) is a key component of drug discovery techniques. In this context, drug-target affinity and drug–target interaction are complementary and essential frameworks that work together to improve our comprehension of DTB dynamics. In this work, we thoroughly analyze the most recent deep learning models, popular benchmark datasets, and assessment metrics for DTB prediction. We look at the paradigm shift in the development of drug discovery research since researchers started using deep learning as a potent tool for DTB prediction. In particular, we examine how methodologies have evolved, starting with early heterogeneous network-based approaches, progressing to graph-based approaches that were widely accepted, followed by modern attention-based architectures, and finally, the most recent multimodal approaches. We also provide case studies utilizing an extensive compound library against specific protein targets implicated in critical cancer pathways to demonstrate the usefulness of these approaches. In addition to summarizing the latest developments in DTB prediction models, this review also identifies their drawbacks. It also highlights the outlook for the DTB prediction domain and future research directions. Combined, these studies present a more comprehensive view of how deep learning offers a quantitative framework for researching drug-target relationships, speeding up the identification of new drug candidates and making it easier to identify possible DTBs. 

Drug–target affinity (DTA) prediction is a critical aspect of drug discovery. The meaningful representation of drugs and targets is crucial for accurate prediction. Using 1D string-based representations for drugs and targets is a common approach that has demonstrated good results in drug–target affinity prediction. However, these approach lacks information on the relative position of the atoms and bonds. To address this limitation, graph-based representations have been used to some extent. However, solely considering the structural aspect of drugs and targets may be insufficient for accurate DTA prediction. Integrating the functional aspect of these drugs at the genetic level can enhance the prediction capability of the models. To fill this gap, we propose GramSeq-DTA, which integrates chemical perturbation information with the structural information of drugs and targets. We applied a Grammar Variational Autoencoder (GVAE) for drug feature extraction and utilized two different approaches for protein feature extraction as follows: a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN). The chemical perturbation data are obtained from the L1000 project, which provides information on the up-regulation and down-regulation of genes caused by selected drugs. This chemical perturbation information is processed, and a compact dataset is prepared, serving as the functional feature set of the drugs. By integrating the drug, gene, and target features in the model, our approach outperforms the current state-of-the-art DTA prediction models when validated on widely used DTA datasets (BindingDB, Davis, and KIBA). This work provides a novel and practical approach to DTA prediction by merging the structural and functional aspects of biological entities, and it encourages further research in multi-modal DTA prediction. 

Abstract This study examined the effects of 24R,25‐dihydroxyvitamin D₃(24R,25(OH)₂D₃) in estrogen‐responsive laryngeal cancer tumorigenesis in vivo, the mechanisms involved, and whether the ability of the tumor cells to produce 24R,25(OH)₂D₃locally is estrogen‐dependent. Estrogen receptor alpha‐66 positive (ER+) UM‐SCC‐12 cells and ER− UM‐SCC‐11A cells responded differently to 24R,25(OH)₂D₃in vivo; 24R,25(OH)₂D₃enhanced tumorigenesis in ER+ tumors but inhibited tumorigenesis in ER− tumors. Treatment with 17β‐estradiol (E₂) for 24 h reduced levels of CYP24A1 protein but increased 24R,25(OH)₂D₃production in ER+ cells; treatment with E₂for 9 min reduced CYP24A1 at 24 h and reduced 24R,25(OH)₂D₃production in ER− cells. These findings suggest the involvement of E₂receptor(s) in addition to ERα66. To investigate if 24R,25(OH)₂D₃can act locally, ER+ and ER− cells were treated with 24R,25(OH)₂D₃after inhibiting putative 24R,25(OH)₂D₃receptors, and the cells were assessed for effects on DNA synthesis (proliferation) and p53 production (apoptosis). Specific inhibitors were used to assess downstream secondary messenger signaling pathways and requirements for palmitoylation and caveolae in both cell lines. The results show that 24R,25(OH)₂D₃binds to a complex of receptors, including TLCD3B2, VDR, and protein disulfide‐isomerase A3 (PDIA3) in ER+ UM‐SCC‐12 cells. The mechanism requires palmitoylation, and PLD, PI3K, and LPAR are involved. The anti‐tumorigenic effects of 24R,25(OH)₂D₃in ER− UM‐SCC‐11A cells involve a membrane‐receptor complex consisting of VDR, PDIA3, and ROR2 within caveolae to activate a yet‐to‐be‐elucidated downstream signaling cascade. This work demonstrates a driving mechanism for the therapeutic agent 24R,25(OH)₂D₃that may be used for laryngeal cancer patients.