Explainable artificial intelligence-assisted virtual screening and bioinformatics approaches for effective bioactivity prediction of phenolic cyclooxygenase-2 (COX-2) inhibitors using PubChem molecular fingerprints

Current methods in explainable artificial intelligence and future prospects for integrative physiology

Explainable artificial intelligence (XAI) is gaining importance in physiological research, where artificial intelligence is now used as an analytical and predictive tool for many medical research questions. The primary goal of XAI is to make AI models understandable for human decision-makers. This can be achieved in particular through providing inherently interpretable AI methods or by making opaque models and their outputs transparent using post hoc explanations. This review introduces XAI core topics and provides a selective overview of current XAI methods in physiology. It further illustrates solved and discusses open challenges in XAI research using existing practical examples from the medical field. The article gives an outlook on two possible future prospects: (1) using XAI methods to provide trustworthy AI for integrative physiological research and (2) integrating physiological expertise about human explanation into XAI method development for useful and beneficial human-AI partnerships.

Comparison of new secondgeneration H1 receptor blockers with some molecules; a study involving DFT, molecular docking, ADMET, biological target and activity

Although the antiallergic properties of compounds such as CAPE, Melatonin, Curcumin, and Vitamin C have been poorly discussed by experimental studies, the antiallergic properties of these famous molecules have never been discussed with calculations. The histamine-1 receptor (H1R) belongs to the family of rhodopsin-like G-protein-coupled receptors expressed in cells that mediate allergies and other pathophysiological diseases. In this study, pharmacological activities of FDA-approved second generation H1 antihistamines (Levocetirizine, desloratadine and fexofenadine) and molecules such as CAPE, Melatonin, Curcumin, Vitamin C, ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) profiles, density functional theory (DFT), molecular docking, biological targets and activities were compared by calculating. Since drug development is an extremely risky, costly and time-consuming process, the data obtained in this study will facilitate and guide future studies. It will also enable researchers to focus on the most promising compounds, providing an effective design strategy. Their pharmacological activity was carried out using computer-based computational techniques including DFT, molecular docking, ADMET analysis, biological targeting, and activity methods. The best binding sites of Desloratadine, Levocetirizine, Fexofenadine, CAPE, Quercetin, Melatonin, curcumin, Vitamin C ligands to Desmoglein 1, Human Histamine H1 receptor, IgE and IL13 protons were determined by molecular docking method and binding energy and interaction states were analyzed. Fexofenadine and Quercetin ligand showed the most effective binding affinity. Melatonin had the best Caco-2 permeability PPB values of Quercetin, CAPE and Curcumin were at optimal levels. On the OATP1B1 and OATP1B3 of curcumin and CAPE, Quercetin was found to have strong inhibition effects on BCRP. Melatonin and CAPE were found to have the highest inhibition values on CYP1A2, while CAPE had the highest inhibition values on CYP2C19 and CYP2C9. Vitamin C and Quercetin were found to be safer in terms of cardiac toxicity and mutagenic risks, while Desloratadine and Levocetirizine carried high risks of neurotoxicity and hematotoxicity, while CAPE was noted for its high enzyme inhibitory activities and low toxicity profiles, while the hERG blockade, DILI, and cytotoxicity values of other compounds pointed to various safety concerns. This study demonstrated the potential of machine learning methods in understanding and discovering H1 receptor blockers. The results obtained provide important clues in the development of important strategies in the clinical use of H1 receptor blockers. In the light of these data, CAPE and Quercetin are remarkable molecules.

Mechanism of Lian Hua Qing Wen capsules regulates the inflammatory response caused by M1 macrophage based on cellular experiments and computer simulations

PURPOSE

The polarization of macrophages with the resulting inflammatory response play a crucial part in tissue and organ damage due to inflammatory. Study has proved Lian Hua Qing Wen capsules (LHQW) can reduce activation of inflammatory response and damage of tissue derived from the inflammatory reactions. However, the mechanism of LHQW regulates the macrophage-induced inflammatory response is unclear. Therefore, we investigated the mechanism of LHQW regulated the inflammatory response of M1 macrophages by cellular experiments and computer simulations.

METHODS

This study has analysed the targets and mechanisms of macrophage regulating inflammatory response at gene and protein levels through bioinformatics. The monomeric components of LHQW were analyzed by High Performance Liquid Chromatography (HPLC). We established the in vitro cell model by M1 macrophages (Induction of THP-1 cells into M1 macrophages). RT-qPCR and immunofluorescence were used to detect changes in gene and protein levels of key targets after LHQW treatment. Computer simulations were utilized to verify the binding stability of monomeric components and protein targets.

RESULTS

Macrophages had 140,690 gene targets, inflammatory response had 12,192 gene targets, intersection gene targets were 11,772. Key monomeric components (including: Pinocembrin, Fargesone-A, Nodakenin and Bowdichione) of LHQW were screened by HPLC. The results of cellular experiments indicated that LHQW could significantly reduce the mRNA expression of CCR5, CSF2, IFNG and TNF, thereby alleviating the inflammatory response caused by M1 macrophage. The computer simulations further validated the binding stability and conformation of key monomeric components and key protein targets, and IFNG/Nodakenin was able to form the most stable binding conformation for its action.

CONCLUSION

In this study, the mechanism of LHQW inhibits the polarization of macrophages and the resulting inflammatory response was investigated by computer simulations and cellular experiments. We found that LHQW may not only reduce cell damage and death by acting on TNF and CCR5, but also inhibit the immune recognition process and inflammatory response by regulating CSF2 and IFNG to prevent polarization of macrophages. Therefore, these results suggested that LHQW may act through multiple targets to inhibit the polarization of macrophages and the resulting inflammatory response.

Exploring the Promise and Challenges of Artificial Intelligence in Biomedical Research and Clinical Practice

ABSTRACT

Artificial intelligence (AI) is poised to revolutionize how science, and biomedical research in particular, are done. With AI, problem-solving and complex tasks using massive data sets can be performed at a much higher rate and dimensionality level compared with humans. With the ability to handle huge data sets and self-learn, AI is already being exploited in drug design, drug repurposing, toxicology, and material identification. AI could also be used in both basic and clinical research in study design, defining outcomes, analyzing data, interpreting findings, and even identifying the most appropriate areas of investigation and funding sources. State-of-the-art AI-based large language models, such as ChatGPT and Perplexity, are positioned to change forever how science is communicated and how scientists interact with one another and their profession, including postpublication appraisal and critique. Like all revolutions, upheaval will follow and not all outcomes can be predicted, necessitating guardrails at the onset, especially to minimize the untoward impact of the many drawbacks of large language models, which include lack of confidentiality, risk of hallucinations, and propagation of mainstream albeit potentially mistaken opinions and perspectives. In this review, we highlight areas of biomedical research that are already being reshaped by AI and how AI is likely to affect it further in the near future. We discuss the potential benefits of AI in biomedical research and address possible risks, some surrounding the creative process, that warrant further reflection.

Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery

Drug development is expensive, time-consuming, and has a high failure rate. In recent years, artificial intelligence (AI) has emerged as a transformative tool in drug discovery, offering innovative solutions to complex challenges in the pharmaceutical industry. This manuscript covers the multifaceted role of AI in drug discovery, encompassing AI-assisted drug delivery design, the discovery of new drugs, and the development of novel AI techniques. We explore various AI methodologies, including machine learning and deep learning, and their applications in target identification, virtual screening, and drug design. This paper also discusses the historical development of AI in medicine, emphasizing its profound impact on healthcare. Furthermore, it addresses AI's role in the repositioning of existing drugs and the identification of drug combinations, underscoring its potential in revolutionizing drug delivery systems. The manuscript provides a comprehensive overview of the AI programs and platforms currently used in drug discovery, illustrating the technological advancements and future directions of this field. This study not only presents the current state of AI in drug discovery but also anticipates its future trajectory, highlighting the challenges and opportunities that lie ahead.