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

Preface
Contents
Introduction
Technical setup and naming conventions
1 Data science: introduction
2 Data science: the “fourth paradigm” of science
3 Relations to other domains and cheminformatics
Part A: IT, data science, and AI
IT basics (cloud, REST, edge)
4 Cheminformatics application landscape
5 Cloud, fog, and AI runtime environments
6 DevOps, DataOps, and MLOps
7 High-performance computing (HPC) and cluster
8 REST and MQTT
9 Edge devices and IoT
Programming
10 Python and other programming languages
11 Python standard libraries and Conda
12 IDE’s and workflows
13 Jupyter notebooks
14 Working with notebooks and extensions
15 Notebooks and Python
16 Versioning code and Jupyter notebooks
17 Integration of Knime and Excel
Data engineering
18 Big data
19 Jupyter and Spark
20 Files: structure representations
21 Files: other formats
22 Data retrieval and processing: ETL
23 Data pipelines
24 Data ingestion: online data sources
25 Designing databases
26 Data science workflow and chemical descriptors
Data science as field of activity
27 Community and competitions
28 Data science libraries
29 Deep learning libraries
30 ML model sources and marketplaces
31 Model metrics: MLFlow and Ludwig
Introduction to ML and AI
32 First generation (logic and symbols)
33 Second generation (shallow models)
34 Second generation: regression
35 Decision trees
36 Second generation: classification
37 Second generation: clustering and dimensionality reduction
38 Third generation: deep learning models (ANN)
39 Third generation: SNN – spiking neural networks
40 xAI: eXplainable AI
Part B: Jupyter in cheminformatics
Physical chemistry
41 Crystallographic data
42 Crystallographic calculations
43 Chemical kinetics and thermochemistry
44 Reaction paths and mixtures
45 The periodic table of elements
46 Applied thermodynamics
Material science
47 Material informatics
48 Molecular dynamics workflows
49 Molecular mechanics
50 VASP
51 Gaussian (ASE)
52 GROMACS
53 AMBER, NAMD, and LAMMPS
54 Featurize materials
55 ASE and NWChem
Organic chemistry
56 Visualization
57 Molecules handling and normalization
58 Features and 2D descriptors (of carbon compounds)
59 Working with molecules and reactions
60 Fingerprint descriptors (1D)
61 Similarities
Engineering, laboratory, and production
62 Laboratory: SILA and AnIML
63 Laboratory: LIMS and daily calculations
64 Laboratory: robotics and cognitive assistance
65 Chemical engineering
66 Reactors, process flow, and systems analysis
67 Production: PLC and OPC/UA
68 Production: predictive maintenance
Part C: Data science
Data engineering in analytic chemistry
69 Titration and calorimetry
70 NMR
71 X-ray-based characterization: XAS, XRD, and EDX
72 Mass spectroscopy
73 TGA, DTG
74 IR and Raman spectroscopy
75 AFM and thermogram analysis
76 Gas chromatography-mass spectrometry (GC-MS)
Applied data science and chemometrics
77 SVD chemometrics example
78 Principal component analysis (PCA)
79 QSAR: quantitative structure–activity relationship
80 DeepChem: binding affinity
81 Stoichiometry and reaction balancing
Applied artificial intelligence
82 ML Python libraries in chemistry
83 AI in drug design
84 Automated machine learning
85 Retrosynthesis and reaction prediction
86 ChemML
87 AI in material design
Knowledge and information
88 Ontologies and inferencing
89 Analyzing networks
90 Knowledge ingestion: labeling and optical recognition
91 Content mining and knowledge graphs
Part D: Quantum computing and chemistry Introduction
92 Quantum concepts
93 QComp: technology vendors
94 Quantum computing simulators
95 Quantum algorithms
96 Quantum chemistry software (QChem)
Quantum Computing Applications
97 Application examples
98 Simulating molecules using VQE
99 Studies on small clusters of LiH, BeH2, and NaH
100 Quantum machine learning (QAI)
Code index
Index