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