Artificial intelligence in Pharmacology and practice

Background: In recent years artificial intelligence (AI), and especially machine learning (ML),has rapidly considered for applications in healthcare which pharmacology is a field of its’ applicationwhere AI and ML approaches are particularly useful to analyze data according to differentaspects as chemical structure to clinical informations of patinets or from genomic data to diseasestages. For several days ,AI has being applied in drug discovery and identification of drug targets.Morever, AI models which help to describe character pf patients and predict an drug response ofindividulas are being shown, which occupy from drug discovery to personalized medicine .In thisminireview we discuss about some of these applicants.Methods: By exploring some reseach data motors as Pubmed,Medline,Elsevier,Google Scolar,Ovids, we have assayed some AI pracices and applications in Pharmacology .as we accumulatedmore than ۲۰ articles which describing use of AI in pharmacology learning and ۱۵ articlesin chemical structure identifications and dicoveries and more than ۱۰ articles in personalisedmedicne and extacted their methods in these assaysResults:We have reached to some informations about AI in drug discovery, Clinical trials andreal-world evidence and optimising of drug treatment, The routes of AI ‘s applications in drugdiscovery is multifaceted and if the target is known, AI can be used to predict chemical structuresfor desired targets. Finally ,a novel compound ‘s specifications can be reached by in vivo ,can bepredictable by knowledge of known drug compounds and pharmacokinetics and pharmadynamicsspecifications . Applications of AI are standard practice in many pharmaceutical companieswhich already have been used as (Cytoreason) which has develpoped and offered models to somepharmaceutical companies and helped them for studying disease and drug pathways. Secondly,the structure of chemicals which known as efficient drugs or endogenic factors can be used foridentifying the targets, so that revealing the structure of a target of a potential drug. Finally , an invivo specifications of a new compound can be predicted through knowledges of known drugs, aspharmacokinetics and –dynamics informations. As in the early stages discovery of drug it can bepredicted if a chemical is probably to fail, either by not binding to desirable target or potentiallyby undesirable ADME specifications, so investment in the development can be prevented beforehigh costs trials have begun. These applications of AI can be considered as standard practice inmany pharmaceutical companies which can help them for studying disease and drug pathwaysCoclusion: In this mini review we have tried to show that AI approaches are widely used in manyaspects of pharmacology,from drug discovery to real-world evidence and indiviualized medicine.In recent years, So we can predict that AI is rapidly progressing to become as a standard analyticaltool in drug development.