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Computational prediction of ADMET properties and adverse effects is an effective method to minimize the risk of late-stage attrition and reduce the number of safety issues. This method is now well established as a reliable and cost-effective approach to assist the drug discovery process. Computational models are used to focus medicinal chemistry efforts into the suitable chemical space; to connect, use and extend experimental data; to minimize the number of compounds to be synthesized; as well as to obtain a favorable biochemical and/or physicochemical profile (Dearden, 2007; Gleeson et al., 2011; Gleeson et al., 2012; Moroy et al., 2012; Raunio, 2011; Gleeson, 2008; Leeson and Springthorpe, 2007; Lipinski, 2000; Price et al., 2009).
(Dearden, 2007) In silico prediction of ADMET properties: how far have we come? Expert Opin Drug Metab Toxicol.
(Gleeson et al., 2011) In-silico ADME models: a general assessment of their utility in drug discovery applications. Curr Top Med Chem.
(Gleeson et al., 2012) The challenges involved in modeling toxicity data in silico: a review. Curr Pharm Des.
(Moroy et al., 2012) Toward in silico structure-based ADMET prediction in drug discovery. Drug Discov Today.
(Raunio, 2011) In silico toxicology - non-testing methods. Front Pharmacol.
(Gleeson, 2008) Generation of a set of simple, interpretable ADMET rules of thumb. J Med Chem.
(Leeson and Springthorpe, 2007) The influence of drug-like concepts on decision-making in medicinal chemistry. Nat Rev Drug Discov.
(Lipinski, 2000) Drug-like properties and the causes of poor solubility and poor permeability. J Pharmacol Toxicol Methods.
(Price et al., 2009) Physicochemical drug properties associated with in vivo toxicological outcomes: a review. Expert Opin Drug Metab Toxicol.
(Legehar et al., 2016) IDAAPM: integrated database of ADMET and adverse effects of predictive modeling based on FDA approved drug data. J Cheminform.