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.

class schrodinger.application.desmond.correlation_tau.ConfidenceInterval(val: float, lower_bound: float, upper_bound: float)

Bases: object

val: float
lower_bound: float
upper_bound: float
to_measurement() schrodinger.application.desmond.measurement.Measurement

Convert the ConfidenceInterval into a Measurement

Since confidence intervals (ci) are non-symetrical, we take its average to make it symmetrical. We then divide it by two to make it same scale as standard deviation, assuming that the input is a 95% confidence interval.

Returns

measurement derived from the represented confidence interval

__init__(val: float, lower_bound: float, upper_bound: float) None
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_expected_slope(experiment, experiment_sigma=0.3, prediction_sigma=0.3, num_sample=1000)
schrodinger.application.desmond.correlation_tau.predict_correlation(experiment, experiment_sigma=0.3, prediction_sigma=0.3, num_sample=1000, return_ci=False)

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

  • return_ci – If True, return the data in ConfidenceInterval format: <R>, <R^2>, <R^2_signed> If False, return: <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