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Prediction of pharmacokinetic profiles of new chemical entities is essential in drug development to minimize the risks of potential withdrawals. The excretion of unchanged compounds by the kidney constitutes a major route in drug elimination and plays an important role in pharmacokinetics. Herein, we created  in silico  prediction models of the fraction of drug excreted unchanged in the urine ( f   e  ) and renal clearance ( CL   r  ), with datasets of 411 and 401 compounds using freely available software; notably, all models require chemical structure information alone. The binary classification model for  f   e   demonstrated a balanced accuracy of 0.74. The two-step prediction system for  CL   r   was generated using a combination of the classification model to predict excretion-type compounds and regression models to predict the  CL   r   value for each excretion type. The accuracies of the regression models increased upon adding a descriptor, which was the observed and predicted fraction unbound in plasma ( f   u,p  ); 78.6% of the samples in the higher range of renal clearance fell within 2-fold error with predicted  f   u,p   value. Our prediction system for renal excretion is freely available to the public and can be used as a practical tool for prioritization and optimization of compound synthesis in the early stage of drug discovery.

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Development of an in silico prediction system of human renal excretion and clearance from chemical structure information incorporating fraction unbound in plasma as a descriptor