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Inchikey collision for WDGFFVCWBZVLCE | |
https://pubchem.ncbi.nlm.nih.gov/#query=WDGFFVCWBZVLCE | |
https://cactus.nci.nih.gov/cgi-bin/lookup/search | |
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%nproc=4 | |
%mem=10gb | |
#p b2plyp/aug-cc-pvtz iop(3/125=0360003600,3/78=0640006400,3/76=0350006500,3/77=1000010000,5/33=1) | |
# int(grid=ultrafine) empiricaldispersion=gd2 iop(3/174=400000) | |
Using the new G16 mechanism to specify nonstandard dispersion parameters // https://www.compchem.me/b2gp-plyp | |
0 1 | |
C 0.0 0.0 0.0 | |
O 0.0 0.0 1.1314 |
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import sys | |
import requests | |
from lxml import etree | |
if __name__=="__main__": | |
smiles = sys.argv[1] | |
html_doc = requests.get("https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/smiles/" + smiles + "/record/XML") | |
root = etree.XML(html_doc.text) |
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#!/bin/sh | |
export XTBHOME=/home/ubuntu/qcxms/.XTBPARAM | |
export PATH=$PATH:/home/ubuntu/qcxms | |
export OMP_NUM_THREADS=8 | |
ulimit -s unlimited | |
echo "qcxms sourced" |
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GeekBench August 2021 - SweetSpotFinder | |
# Processor Specifications Score (multi) Score (single) | |
1 AMD Ryzen Threadripper 3990X 2.9 GHz (64 cores) 25036 1212 | |
2 AMD Ryzen Threadripper 3970X 3.7 GHz (32 cores) 22397 1261 | |
3 AMD Ryzen Threadripper 3960X 3.8 GHz (24 cores) 19876 1272 | |
4 Intel Xeon W-3175X 3.1 GHz (28 cores) 19764 1081 | |
5 Intel Xeon W-3275M 2.5 GHz (28 cores) 18880 1052 | |
6 Intel Xeon W-3265M 2.7 GHz (24 cores) 17836 1143 | |
7 AMD Ryzen 9 5950X 3.4 GHz (16 cores) 16755 1692 |
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#!/usr/bin/env python2 | |
import numpy as np | |
import sys | |
elements = dict() | |
elements["H"] = 1 | |
elements["C"] = 6 | |
elements["N"] = 7 | |
elements["O"] = 8 | |
elements["F"] = 9 |
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! PUBLIC DOMAIN LICENSE 2017 BY ANDERS S. CHRISTENSEN | |
! | |
! I WROTE THIS BECAUSE I WAS BORED - I DON'T RECOMMEND | |
! WRITING FILE PARSERS IN FORTRAN BECAUSE IT IS NOT | |
! PRODUCTIVE. | |
program convert | |
implicit none |
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Mrv1823 08262014252D | |
17 17 0 0 0 0 999 V2000 | |
0.5678 1.1143 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 | |
1.2352 0.6294 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 | |
2.0198 0.8843 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 | |
0.9803 -0.1553 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0 | |
0.1553 -0.1553 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0 | |
-0.3297 -0.8227 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0 |
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Unidentified peaks remain a major problem in untargeted metabolomics by LC-MS/MS. Confidence in peak annotations increases by combining MS/MS matching and retention time. We here show how retention times can be predicted from molecular structures. Two large, publicly available datasets were used for model training in machine learning: the Fiehn hydrophilic interaction liquid chromatography dataset (HILIC) of 981 primary metabolites and biogenic amines, and the RIKEN Plant Specialized Metabolome Annotation (PlaSMA) database of 852 secondary metabolites that uses reversed-phase liquid chromatography (RPLC). Five different machine learning algorithms have been integrated into the Retip R package: the random forest, Bayesian-regularized neural network, XGBoost, light gradient-boosting machine (LightGBM) and Keras algorithms for building the retention time prediction models. A complete workflow for retention time prediction was developed in R. It can be freely downloaded from the GitHub repository (https://www.ret |
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