Transformer models have emerged as pivotal tools within the realm of drug discovery, distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes. Leveraging the innate capabilities of transformer architectures to comprehend intricate hierarchical dependencies inherent in sequential data, these models showcase remarkable efficacy across various tasks, including new drug design and drug target identification. The adaptability of pre-trained transformer-based models renders them indispensable assets for driving data-centric advancements in drug discovery, chemistry, and biology, furnishing a robust framework that expedites innovation and discovery within these domains. Beyond their technical prowess, the success of transformer-based models in drug discovery, chemistry, and biology extends to their interdisciplinary potential, seamlessly combining biological, physical, chemical, and pharmacological insights to bridge gaps across diverse disciplines. This integrative approach not only enhances the depth and breadth of research endeavors but also fosters synergistic collaborations and exchange of ideas among disparate fields. In our review, we elucidate the myriad applications of transformers in drug discovery, as well as chemistry and biology, spanning from protein design and protein engineering, to molecular dynamics (MD), drug target identification, transformer-enabled drug virtual screening (VS), drug lead optimization, drug addiction, small data set challenges, chemical and biological image analysis, chemical language understanding, and single cell data. Finally, we conclude the survey by deliberating on promising trends in transformer models within the context of drug discovery and other sciences.

In recent years,radiomics has been developed rapidly in the field of clinical medicine,and the artificial intelligence(AI)technology has been utilized to assist diagnosis.This paper introduced the background of radiomics,analyzed the basic research process of radiomics,and looked forward to its application in the identification of bone and joint injuries in the field of forensic medicine.Reviewing the three aspects is expected to provide a theoretical foundation of radiomics,which will be helpful to develop its application in forensic medicine.

Humans ; COVID-19 ; Quarantine ; Renal Dialysis ; SARS-CoV-2 ; Hospitals

Objective To analyze the clinic features and hereditary characteristics of three subtypes of porokeratosis, namely disseminated superficial actinic porokeratosis (DSAP), porokeratosis palmaris et plantaris disseminata(PPPD) and porokeratosis of Mibelli (PM) in five pedigrees with porokeratosis. Meth-ods After clinical and pathological diagnosis, every living family member of the five pedigrees with poro-kerotosis was undergoing medical examination and genetics analysis. These five pedigrees consisted of three DSAP pedigrees (totally 266 family members including 100 patients), and one PPPD pedigree (composing of 90 members including 26 patients), one PM pedigree (cornposing of 34 members including 17 patients). Results While diagnosed as porokeratosis, the five pedigrees included three distinctive variants, each with its own clinic characteristics. The lesions was initiated on the face in DSAP subtype, on palms and the flex-ion side of fingers in PPPD subtype; or involving sun-covered areas in PM subtype. Of the three subtypes of porokeratosis, the onset age in DSAP subtype was earliest, usually about 8-20 years old, about 14-20 years old in PPPD subtype, but PM subtype about 20-30 years old. Conclusions As a group of autosomal dominant genodermatosis, porokeratosis presented various clinic variants with different genetic basis. And, different subtype could be seen in a same patient or same pedigree.