TY - JOUR
T1 - Open access in silico tools to predict the ADMET profiling of drug candidates
AU - Kar, Supratik
AU - Leszczynski, Jerzy
N1 - Publisher Copyright:
© 2020 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2020/12
Y1 - 2020/12
N2 - Introduction: We are in an era of bioinformatics and cheminformatics where we can predict data in the fields of medicine, the environment, engineering and public health. Approaches with open access in silico tools have revolutionized disease management due to early prediction of the absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles of the chemically designed and eco-friendly next-generation drugs. Areas covered: This review meticulously encompasses the fundamental functions of open access in silico prediction tools (webservers and standalone software) and advocates their use in drug discovery research for the safety and reliability of any candidate-drug. This review also aims to help support new researchers in the field of drug design. Expert opinion: The choice of in silico tools is critically important for drug discovery and the accuracy of ADMET prediction. The accuracy largely depends on the types of dataset, the algorithm used, the quality of the model, the available endpoints for prediction, and user requirement. The key is to use multiple in silico tools for predictions and comparing the results, followed by the identification of the most probable prediction.
AB - Introduction: We are in an era of bioinformatics and cheminformatics where we can predict data in the fields of medicine, the environment, engineering and public health. Approaches with open access in silico tools have revolutionized disease management due to early prediction of the absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles of the chemically designed and eco-friendly next-generation drugs. Areas covered: This review meticulously encompasses the fundamental functions of open access in silico prediction tools (webservers and standalone software) and advocates their use in drug discovery research for the safety and reliability of any candidate-drug. This review also aims to help support new researchers in the field of drug design. Expert opinion: The choice of in silico tools is critically important for drug discovery and the accuracy of ADMET prediction. The accuracy largely depends on the types of dataset, the algorithm used, the quality of the model, the available endpoints for prediction, and user requirement. The key is to use multiple in silico tools for predictions and comparing the results, followed by the identification of the most probable prediction.
KW - ADMET
KW - drug
KW - in silico
KW - open access
KW - prediction
UR - http://www.scopus.com/inward/record.url?scp=85089002621&partnerID=8YFLogxK
U2 - 10.1080/17460441.2020.1798926
DO - 10.1080/17460441.2020.1798926
M3 - Review article
C2 - 32735147
AN - SCOPUS:85089002621
SN - 1746-0441
VL - 15
SP - 1473
EP - 1487
JO - Expert Opinion on Drug Discovery
JF - Expert Opinion on Drug Discovery
IS - 12
ER -