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# Copyright (c) 2016. Mount Sinai School of Medicine
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
MAX_IC50 = 50000.0
def ic50_to_regression_target(ic50, max_ic50=MAX_IC50):
"""
Transform IC50 inhibitory binding concentrations to affinity values between
[0,1] where 0 means a value greater or equal to max_ic50 and 1 means very
strong binder.
Parameters
----------
ic50 : numpy.ndarray
max_ic50 : float
"""
log_ic50 = np.log(ic50) / np.log(max_ic50)
regression_target = 1.0 - log_ic50
# clamp to values between 0, 1
regression_target = np.maximum(regression_target, 0.0)
regression_target = np.minimum(regression_target, 1.0)
return regression_target
def regression_target_to_ic50(y, max_ic50=MAX_IC50):
"""
Transform values between [0,1] to IC50 inhibitory binding concentrations
between [1.0, infinity]
Parameters
----------
y : numpy.ndarray of float
max_ic50 : float
Returns numpy.ndarray
"""
return max_ic50 ** (1.0 - y)