maxentile

openforcefield version:  0.7.0
rdkit version:           2020.03.4
openeye version:         2019.Oct.2

[... suppressing stereochem warnings ...]

CN(=O)=O
        [#1:1]-[#6X4]~[*+1,*+2]                   (3 in RDKit, 0 in OpenEye)
        [*:1]-[#6X4:2]-[#7X3:3]-[*:4]             (3 in RDKit, 0 in OpenEye)
        [*:1]-[#7X4,#7X3,#7X2-1:2]-[*:3]          (2 in RDKit, 0 in OpenEye)

maxentile

##geometric functions
using LinearAlgebra:dot, norm, atan, acos, ×, ⋅
distance(x, y) = norm(x - y)
∠(a, b, c) = acos(-dot((b-a)/norm(b-a), (c-b)/norm(c-b)))

function dihedral_angle(a, b, c, d)
    b1 = b - a
    # b2 = c - b # mdtraj convention
    b2 = b - c  # openmm convention
    b3 = d - c

maxentile

"""parse subset of smirks that covers "self-contained" patterns

like [<atom>:1] <bond> [<atom>:2]

but not like [<atom>:1] <bond> [<atom>:2]=[<atom>]
or [<atom>:1] <bond> [<atom>$(*~[#6]):2]


later, it's straightforward to extend to handle recursive smarts (like "$(*~[#6])")
but less straightforward to handle extended chemical environments

maxentile

maxentile

# Implementation of balanced locally informed proposals, following:
# [Zanella, 2017] "Informed proposals for local MCMC in discrete spaces"
#    https://arxiv.org/abs/1711.07424

# Usage: define a neighborhood_fxn(x) that returns an iterable of x's neighbors.
# Where you would have made uninformed proposals using this neighborhood, you
#    can now make target-informed proposals using this neighborhood.

# Assumption: neighborhood_fxn is deterministic
# Assumption: if y is in neighborhood_fxn(x), then x is in neighborhood_fxn(y)

maxentile

maxentile

from openmmtools.testsystems import HostGuestVacuum
from simtk import openmm as mm

testsystem = HostGuestVacuum(constraints=None)
ligand_residue = list(testsystem.topology.residues())[1]
atoms_in_ligand = [atom.index for atom in ligand_residue.atoms()]


def filter_from_subset(atom_subset=None):
    if type(atom_subset) == type(None):

maxentile

import numpy as np
from numba import jit
AND = np.bitwise_and


def off_mol_to_arrays(mol):
    """accept an openforcefield.topology.Molecule

    return
        * an (n_atoms, 2) integer array containing (atomic_number, formal_charge) for each atom

maxentile

from simtk.openmm import app
import simtk.openmm as mm
from simtk import unit
import mbpol
import numpy as np
import mdtraj as md
import matplotlib.pyplot as plt

# create simulation
pdb = app.PDBFile("water14_cluster.pdb")

maxentile

# simplified from GP structure learning example, section 7.2
# https://github.com/probcomp/pldi2019-gen-experiments/tree/master/gp -- `cov_tree.jl` and `lightweight.jl`

using Gen

# abstract types for binary expression tree
abstract type Node end
abstract type LeafNode <: Node end
abstract type BinaryOpNode <: Node end
num_children(node::LeafNode) = 0