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.

Chronic constipation is a common gastrointestinal disease severely affecting the patient׳s quality of life. The traditional treatment of constipation is the use of laxatives. Recently, several new drugs including lubiprostone, linaclotide and prucalopride have been approved for treatment of chronic constipation. However, a significant unmet medical need still remains, particularly among those patients achieving poor results by current therapies. The 5-HT4 receptor modulators velusetrag and naronapride, the guanylate cyclase C agonist plecanatide and the ileal bile acid transporter inhibitor elobixibat are recognized as the most promising drugs under investigation. Herein, we give a comprehensive review on the pharmacological therapeutics for the treatment of chronic constipation, with the purpose of reflecting the drug development trends in this field.

Objective To observe the effects of Najia method of midday-midnight point selection for acute ischemic stroke (AIS) model rats onthe contents of NSE and S100B protein in serum. Methods SPF SD male rats were chosen to establish the models by middle cerebral artery bolt method. Rats were divided into blank group, sham-operation group, model group, channel-point group, and Najia method group by random number table method. Blank group, sham-operation group, and model group were in the absence of treatment, while the channel-point group received acupuncture treatment according to differentiation syndrome. Najia method group used Najia method of midday-midnight point selection to conduct acupuncture treatment once a day. Improvement of neural function and cerebral infarction volume were observed. The contents of NSE and S100B protein in serum were detected. Results Compared with model group, neurological function score, infarct volume and infarct volume percentage, and the contents of NSE and S100B protein in serum decreased in Najia method group and channel-point group (P<0.05, P<0.01). The effects of Najia group were generally better than the channel-point group. Conclusion Najia method of midday-midnight point selection can decrease the content of NSE and S100B protein in serum of AIS model rats, so as to achieve the effects of neuroprotection and treatment.