The application of artificial intelligence(AI)in medicine,particularly through machine learning(ML),marked a significant progression in drug discovery.AI acts as a powerful catalyst in narrowing the gap between diseas...The application of artificial intelligence(AI)in medicine,particularly through machine learning(ML),marked a significant progression in drug discovery.AI acts as a powerful catalyst in narrowing the gap between disease understanding and the identification of potential therapeutic agents.This review provides an inclusive summary of the latest advancements in AI and its application in drug discovery.We examine the various stages of the drug discovery process,starting from disease identification and encompassing diagnosis,target identification,screening,and lead discovery.AI's capability to analyze extensive datasets and discern patterns is essential in these stages,enhancing predictions and efficiencies in disease identification,drug discovery,and clinical trial management.The role of AI in expediting drug development is emphasized,highlighting its potential to analyze vast data volumes,thus reducing the time and costs associated with new drug market introduction.The importance of data quality,algorithm training,and ethical considerations,especially in patient data handling during clinical trials,is addressed.By considering these factors,AI promises to transform drug development,offering significant benefits to patients and society.展开更多
The present research paper presents the synthesis, characterization, biological and computational studies of 4-(benzylideneamino) benzoic acid derivatives(3a~3g). Derivatives 3a~3c displayed best antidiabetic potentia...The present research paper presents the synthesis, characterization, biological and computational studies of 4-(benzylideneamino) benzoic acid derivatives(3a~3g). Derivatives 3a~3c displayed best antidiabetic potential with a glucose-lowering effect compared to the reference drug Glibenclamide. Biochemical parameters including plasma glucose, serum triglycerides, cholesterol, alanine amino transferase and aspartate amino transferase levels showed significant alterations in concentrations relative to the control. Similarly, the derivatives 3a, 3d and 3e displayed potent in vitro antibacterial potential. Molecular docking simulations delineated that the ligands and complexes were stabilized at the active site by electrostatic and hydrophobic forces, consistent with the corresponding experimental results. In silico study of the binding pattern predicted that the synthesized ligands, 3d and 3a could serve as a potential surrogate for hit-to-lead generation and the design of novel antibacterial drugs.展开更多
基金supported by the National Key R&D Program of China(2023YFF1205103),National Natural Science Foundation of China(81925034,22237005)the Key Research and Development Program of Ningxia Hui Autonomous Region(2022CMG01002)+1 种基金the Starry Night Science Fund of Zhejiang University Shanghai Institute for Advanced Study(SN-ZJU-SIAS-007)the innovative research team of high-level local universities in Shanghai(SHSMU-ZDCX20212700).
文摘The application of artificial intelligence(AI)in medicine,particularly through machine learning(ML),marked a significant progression in drug discovery.AI acts as a powerful catalyst in narrowing the gap between disease understanding and the identification of potential therapeutic agents.This review provides an inclusive summary of the latest advancements in AI and its application in drug discovery.We examine the various stages of the drug discovery process,starting from disease identification and encompassing diagnosis,target identification,screening,and lead discovery.AI's capability to analyze extensive datasets and discern patterns is essential in these stages,enhancing predictions and efficiencies in disease identification,drug discovery,and clinical trial management.The role of AI in expediting drug development is emphasized,highlighting its potential to analyze vast data volumes,thus reducing the time and costs associated with new drug market introduction.The importance of data quality,algorithm training,and ethical considerations,especially in patient data handling during clinical trials,is addressed.By considering these factors,AI promises to transform drug development,offering significant benefits to patients and society.
基金Financial support of the Higher Education Commission(HEC)Pakistan,by awarding indigenous fellowship batch-1 phase-ΙΙfor M.Phil leading to Ph.D。
文摘The present research paper presents the synthesis, characterization, biological and computational studies of 4-(benzylideneamino) benzoic acid derivatives(3a~3g). Derivatives 3a~3c displayed best antidiabetic potential with a glucose-lowering effect compared to the reference drug Glibenclamide. Biochemical parameters including plasma glucose, serum triglycerides, cholesterol, alanine amino transferase and aspartate amino transferase levels showed significant alterations in concentrations relative to the control. Similarly, the derivatives 3a, 3d and 3e displayed potent in vitro antibacterial potential. Molecular docking simulations delineated that the ligands and complexes were stabilized at the active site by electrostatic and hydrophobic forces, consistent with the corresponding experimental results. In silico study of the binding pattern predicted that the synthesized ligands, 3d and 3a could serve as a potential surrogate for hit-to-lead generation and the design of novel antibacterial drugs.
