schrodinger.application.desmond.correlation_tau module

This program computes the average correlation coefficient and the kendall tau rank coefficient between experiment and prediction samples. Samples are randomly drawn from gaussian distribution centered on each experimental data point with given error.

The algorithm used in this program is based on the work by Scott P. Brown, Steven W. Muchmore, Philip J. Hajduk Drug Discovery Today, Vol. 14, No. 7-8., pp. 420-427

Copyright Schrodinger, LLC. All rights reserved.

schrodinger.application.desmond.correlation_tau.predict_kendall_tau(experiment, experiment_sigma=0.3, prediction_sigma=0.3, num_sample=1000)

Computes the average Kendall tau rank correlation coefficient between experiment and prediction samples. num_sample independent data for each experiment and prediction are sampled from gaussian distribution with experiment_sigma and prediction_sigma error.

Parameters:
  • experiment – sequence of experiment data
  • experiment_sigma – experimental error
  • prediction_sigma – prediction error
  • num_sample – number of samples
Returns:

average_tau, sigma_tau

schrodinger.application.desmond.correlation_tau.predict_correlation(experiment, experiment_sigma=0.3, prediction_sigma=0.3, num_sample=1000)

Computes the average correlation coefficient between experiment and prediction samples. num_sample independent data for each experiment and prediction are sampled from gaussian distribution with experiment_sigma and prediction_sigma error.

Parameters:
  • experiment – sequence of experiment data
  • experiment_sigma – experimental error
  • prediction_sigma – prediction error
  • num_sample – number of samples
Returns:

<R>, sigma_R, <R^2>, sigma_R^2, <R^2_signed>, sigma_R^2_signed

schrodinger.application.desmond.correlation_tau.compute_rmse(experiment, prediction)

Computes root mean square error between experiment and prediction. Averages of experiment and prediction are aligned before RMSE computation.

Parameters:
  • experiment – sequence of experiment data
  • prediction – sequence of prediction data
Returns:

root_mean_square_error between experiment and prediction