ABSTRACT
Multifunctional therapeutics have emerged as a solution to the constraints imposed by drugs with singular or insufficient therapeutic effects. The primary challenge is to integrate diverse pharmacophores within a single-molecule framework. To address this, we introduced DeepSA, a novel edit-based generative framework that utilizes deep simulated annealing for the modification of articaine, a well-known local anesthetic. DeepSA integrates deep neural networks into metaheuristics, effectively constraining molecular space during compound generation. This framework employs a sophisticated objective function that accounts for scaffold preservation, anti-inflammatory properties, and covalent constraints. Through a sequence of local editing to navigate the molecular space, DeepSA successfully identified AT-17, a derivative exhibiting potent analgesic properties and significant anti-inflammatory activity in various animal models. Mechanistic insights into AT-17 revealed its dual mode of action: selective inhibition of NaV1.7 and 1.8 channels, contributing to its prolonged local anesthetic effects, and suppression of inflammatory mediators via modulation of the NLRP3 inflammasome pathway. These findings not only highlight the efficacy of AT-17 as a multifunctional drug candidate but also highlight the potential of DeepSA in facilitating AI-enhanced drug discovery, particularly within stringent chemical constraints.
ABSTRACT
Objective@#To study the effect of artificial intelligence in the pathological diagnosis of periapical cysts and to explore the application of artificial intelligence in the field of oral pathology.@*Methods@#Pathological images of eighty-seven periapical cysts were selected as subjects to read, and a neural network with a U-net structure was constructed. The 87 HE images and labeled images of periapical cysts were divided into a training set (72 images) and a test set (15 images), which were used in the training model and test model, respectively. Finally, the target level index F1 score, pixel level index Dice coefficient and receiver operating characteristic (ROC) curve were used to evaluate the ability of the U-net model to recognize periapical cyst epithelium.@*Results @# The F1 score of the U-net network model for recognizing periapical cyst epithelium was 0.75, and the Dice index and the areas under the ROC curve were 0.685 and 0.878, respectively.@*Conclusion@#The U-net network model constructed by artificial intelligence has a good segmentation result in identifying periapical cyst epithelium, which can be preliminarily applied in the pathological diagnosis of periapical cysts and is expected to be gradually popularized in clinical practice after further verification with large samples.
ABSTRACT
Emerging evidence shows a striking link between periodontal diseases and various human cancers including oral cancer. And periodontal pathogens, leading to periodontal diseases development, may serve a crucial role in oral cancer. This review elucidated the molecular mechanisms of periodontal pathogens in oral cancer. The pathogens directly engage in their own unique molecular dialogue with the host epithelium to acquire cancer phenotypes, and indirectly induce a proinflammatory environment and carcinogenic substance in favor of cancer development. And functional, rather than compositional, properties of oral microbial community correlated with cancer development are discussed. The effect of periodontal pathogens on periodontal diseases and oral cancer will further detail the pathogenesis of oral cancer and intensify the need of maintaining oral hygiene for the prevention of oral diseases including oral cancer.