Data Science in Chemistry: Artificial Intelligence, Big Data, Chemometrics and Quantum Computing with Jupyter

540 Pages·2020·207.039 MB·English

by  Thorsten Gressling

Thorsten Gressling
Data Science in Chemistry
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Thorsten Gressling
Data Science
in Chemistry
Artificial Intelligence, Big Data, Chemometrics
and Quantum Computing with Jupyter
Author
Dr. Thorsten Gressling
Am Glunzbusch 9
15741 Bestensee, Germany
[email protected]
ISBN 978-3-11-062939-2
e-ISBN (PDF) 978-3-11-062945-3
e-ISBN (EPUB) 978-3-11-063053-4
Library of Congress Control Number: 2020942815
Bibliographic information published by the Deutsche Nationalbibliothek
The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed
bibliographic data are available on the Internet at http://dnb.dnb.de.
© 2021 Walter de Gruyter GmbH, Berlin/Boston
Cover image: IBM Q – Dilution Refrigerator from the Zürich Quantum Computer,
taken at 2018 ASCE. Credit: Graham Carlow, License: Attribution-NoDerivs 2.0
Generic (CC BY-ND 2.0)
Typesetting: Integra Software Services Pvt. Ltd.
Printing and binding: CPI books GmbH, Leck
www.degruyter.com
Preface
The speed of digital transformation is increasing. Although the introduction of com-
puting started in the middle of last century with the first wave of cheminformatics
and the digitization of production plants, new fields of application are emerging with
data science and artificial intelligence (AI). However, the new fields are a fusion of
three domains:
– computer science,
– mathematics, and
– the specialist domain.
There are many excellent books on AI, cheminformatics, or data science, in general.
But practicing data science must address the specialist domain or even take the per-
spective of the business expert. This is where Data Science in Chemistry starts.
Acknowledgments
The idea of writing this book came up by Karin Sora of De Gruyter at the ACS Spring
Meeting 2019 in Orlando. I was complaining why there were no books on this impor-
tant field, and she told me “Then write it!” I also thank Ms. Vivien Schubert of De
Gruyter for guiding me in authoring this book and helping me with all my questions.
I thank my wife Isabella Lewandowski who endured a nerd writing even on
Christmas Eve.
Credits to the coders
The book would not have been possible without the passion and the contributions
of hundreds of scientists who had listed their programs on GitHub! I thank Greg
Landrum, Oliver Beckstein, Daniel Pelliccia, Björn Dahlgreen, and Jean-Baptiste Lamy,
five individuals who are part of the GitHub community.
Contact
In case of errors or comments, please open an issue on github.com/Gressling/
examples.
I am available on LinkedIn or ResearchGate for further contact and discussion.
https://doi.org/10.1515/9783110629453-202

Contents
Preface   V
Introduction   XIII
Technical setup and naming conventions   XV
1 Data science: introduction   1
2 Data science: the “fourth paradigm” of science   4
3 Relations to other domains and cheminformatics   8
Part A: IT, Data science, and AI
IT basics (cloud, REST, edge)
4 Cheminformatics application landscape   13
5 Cloud, fog, and AI runtime environments   15
6 DevOps, DataOps, and MLOps   19
7 High-performance computing (HPC) and cluster   23
8 REST and MQTT   28
9 Edge devices and IoT   34
Programming
10 Python and other programming languages   41
11 Python standard libraries and Conda   45
12 IDE’s and workflows   55
13 Jupyter notebooks   61
14 Working with notebooks and extensions   68
15 Notebooks and Python   73
16 Versioning code and Jupyter notebooks   80
17 Integration of Knime and Excel   83
Data engineering
18 Big data   91
19 Jupyter and Spark   96
20 Files: structure representations   102
21 Files: other formats   106
VIII   Contents
22 Data retrieval and processing: ETL   111
23 Data pipelines   114
24 Data ingestion: online data sources   117
25 Designing databases   125
26 Data science workflow and chemical descriptors   130
Data science as field of activity
27 Community and competitions   139
28 Data science libraries   143
29 Deep learning libraries   145
30 ML model sources and marketplaces   148
31 Model metrics: MLFlow and Ludwig   152
Introduction to ML and AI
32 First generation (logic and symbols)   161
33 Second generation (shallow models)   164
34 Second generation: regression   170
35 Decision trees   179
36 Second generation: classification   182
37 S econd generation: clustering and dimensionality reduction   190
38 Third generation: deep learning models (ANN)   196
39 Third generation: SNN – spiking neural networks   204
40 xAI: eXplainable AI   206
Part B: Jupyter in cheminformatics
Physical chemistry
41 Crystallographic data   213
42 Crystallographic calculations   215
43 Chemical kinetics and thermochemistry   219
44 Reaction paths and mixtures   223
45 The periodic table of elements   227
46 Applied thermodynamics   230
Contents   IX
Material science
47 Material informatics   235
48 Molecular dynamics workflows   237
49 Molecular mechanics   241
50 VASP   245
51 Gaussian (ASE)   249
52 GROMACS   251
53 AMBER, NAMD, and LAMMPS   255
54 Featurize materials   261
55 ASE and NWChem   266
Organic chemistry
56 Visualization   273
57 Molecules handling and normalization   280
58 F eatures and 2D descriptors (of carbon compounds)   287
59 Working with molecules and reactions   294
60 Fingerprint descriptors (1D)   299
61 Similarities   306
Engineering, laboratory, and production
62 Laboratory: SILA and AnIML    313
63 Laboratory: LIMS and daily calculations   319
64 Laboratory: robotics and cognitive assistance   325
65 Chemical engineering   330
66 Reactors, process flow, and systems analysis   334
67 Production: PLC and OPC/UA   338
68 Production: predictive maintenance   342
Part C: Data science
Data engineering in analytic chemistry
69 Titration and calorimetry   349
70 NMR   352
X   Contents
71 X-ray-based characterization: XAS, XRD, and EDX   354
72 Mass spectroscopy   359
73 TGA, DTG   361
74 IR and Raman spectroscopy   364
75 AFM and thermogram analysis   368
76 Gas chromatography-mass spectrometry (GC-MS)   373
Applied data science and chemometrics
77 SVD chemometrics example   379
78 Principal component analysis (PCA)   383
79 Q SAR: Quantitative structure–activity relationship   386
80 DeepChem: binding affinity   389
81 Stoichiometry and reaction balancing   393
Applied artificial intelligence
82 ML Python libraries in chemistry   399
83 AI in drug design   405
84 Automated machine learning   409
85 Retrosynthesis and reaction prediction    412
86 ChemML   419
87 AI in material design   423
Knowledge and information
88 Ontologies and inferencing   429
89 Analyzing networks   436
90 K nowledge ingestion: labeling and optical recognition   443
91 Content mining and knowledge graphs   450
Part D: Quantum computing and chemistry introduction
92 Quantum concepts   461
93 QComp: technology vendors   471
94 Quantum computing simulators   477
95 Quantum algorithms   480
96 Quantum chemistry software (QChem)   485