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# Copyright 2021 AlQuraishi Laboratory
#
# 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 torch
def drmsd(structure_1, structure_2, mask=None):
def prep_d(structure):
d = structure[..., :, None, :] - structure[..., None, :, :]
d = d ** 2
d = torch.sqrt(torch.sum(d, dim=-1))
return d
d1 = prep_d(structure_1)
d2 = prep_d(structure_2)
drmsd = d1 - d2
drmsd = drmsd ** 2
if(mask is not None):
drmsd = drmsd * (mask[..., None] * mask[..., None, :])
drmsd = torch.sum(drmsd, dim=(-1, -2))
n = d1.shape[-1] if mask is None else torch.sum(mask, dim=-1)
drmsd = drmsd * (1 / (n * (n - 1))) if (n > 1).all() else (drmsd * 0.)
drmsd = torch.sqrt(drmsd)
return drmsd
def drmsd_np(structure_1, structure_2, mask=None):
structure_1 = torch.tensor(structure_1)
structure_2 = torch.tensor(structure_2)
if(mask is not None):
mask = torch.tensor(mask)
return drmsd(structure_1, structure_2, mask)
def rmsd(structure_1, structure_2, mask=None):
squared_dists = torch.sum((structure_1 - structure_2) ** 2, dim=-1)
if mask is None:
return torch.sqrt(torch.sum(squared_dists, dim=1) / squared_dists.shape[-1])
squared_dists = squared_dists * mask
n = torch.sum(mask, dim=1)
return torch.sqrt(torch.sum(squared_dists, dim=1) / n)
def gdt(p1, p2, mask, cutoffs):
n = torch.sum(mask, dim=-1)
p1 = p1.float()
p2 = p2.float()
distances = torch.sqrt(torch.sum((p1 - p2)**2, dim=-1))
scores = []
for c in cutoffs:
score = torch.sum((distances <= c) * mask, dim=-1) / n
score = torch.mean(score)
scores.append(score)
return sum(scores) / len(scores)
def gdt_ts(p1, p2, mask):
return gdt(p1, p2, mask, [1., 2., 4., 8.])
def gdt_ha(p1, p2, mask):
return gdt(p1, p2, mask, [0.5, 1., 2., 4.])
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