acruzpr

#!/usr/bin/env python

"""
Install a package from your local pip download cache without having to touch
the 'net at all.

You'll need to be using a pip download cache; that is, you'll need the
following in your ~/.pip/pip.cfg:

    [install]

acruzpr

# building Visual Studio Code Debian package on ARM

# get source code
git clone git@github.com:Microsoft/vscode.git
cd vscode

# build debian package
./scripts/npm.sh install --arch=armhf
./node_modules/.bin/gulp vscode-linux-arm-build-deb

acruzpr

# building Visual Studio Code Debian package on ARM

# get source code
git clone git@github.com:Microsoft/vscode.git
cd vscode

# build debian package
./scripts/npm.sh install --arch=armhf
./node_modules/.bin/gulp vscode-linux-arm-build-deb

acruzpr

# building Visual Studio Code Debian package on ARM

# get source code
git clone git@github.com:Microsoft/vscode.git
cd vscode

# build debian package
./scripts/npm.sh install --arch=armhf
./node_modules/.bin/gulp vscode-linux-arm-build-deb

acruzpr

def getAminoAcids(include_protonated=False):
  molecules = dict()
  molecules['GLY'] = 'NCC(=O)O'
  molecules['ALA'] = 'NC(C)C(=O)O'
  molecules['SER'] = 'NC(CO)C(=O)O'
  molecules['THR'] = 'NC(C(C)O)C(=O)O'
  molecules['CYS'] = 'NC(CS)C(=O)O'
  molecules['VAL'] = 'NC(C(C)C)C(=O)O'
  molecules['LEU'] = 'NC(CC(C)C)C(=O)O'
  molecules['ILE'] = 'NC(C(C)CC)C(=O)O'

acruzpr

import openbabel as ob

# Standard openbabel molecule load
conv = ob.OBConversion()
conv.SetInAndOutFormats('xyz','xyz')
mol = obOBMol()
conv.ReadFile(mol,'my_mol.xyz')

# Define constraints
constraints = ob.OBFFConstraints()

acruzpr

# Copyright (c) 2016 Kyle Barlow
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE

acruzpr

# -*- coding: utf-8 -*-

import numpy as np
from scipy.sparse.linalg import cg
import tensorflow as tf
import time


def conjugate_grad(A, b, x=None):
    """

acruzpr

import pandas as pd
from rdkit import Chem
from rdkit.Chem import AllChem
df = pd.read_csv('SMILES.csv')
mols = [Chem.MolFromSmiles(smi) for smi in df.SMILES]
hmols = [Chem.AddHs(m) for m in mols]
for mol  in hmols:
    AllChem.EmbedMolecule(mol,AllChem.ETKDG())
    print(AllChem.UFFOptimizeMolecule(mol,1000))
smiles = list(df.SMILES)