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|a Machine learning in chemistry :
|b the impact of artificial intelligence /
|c edited by Hugh M. Cartwright.
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|a Cambridge :
|b Royal Society of Chemistry,
|c 2020.
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|a Theoretical and computational chemistry ;
|v 17
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0 |
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|a Intro -- Title -- Copyright -- Preface -- Contents -- Chapter 1 Computers as Scientists -- 1.1 What Is Computational Science? -- 1.2 What Is Artificial Intelligence? -- 1.3 What Is Machine Learning? -- References -- Chapter 2 How Do Machines Learn? -- 2.1 Framing the Question -- 2.2 Gathering Data -- 2.3 Setting up the Algorithm -- 2.4 The Training Procedure -- 2.5 Overcoming Flaws -- 2.6 Deploying the Algorithms -- Note on References -- References -- Chapter 3 MedChemInformatics: An Introduction to Machine Learning for Drug Discovery -- 3.1 Introduction
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| 505 |
8 |
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|a 3.1.1 Artificial Intelligence vs. Machine Learning -- 3.1.2 Supervised vs. Unsupervised Learning -- 3.2 Forewarned Is Forearmed -- 3.2.1 Dataset Assembly and Curation -- 3.2.2 Model Building -- 3.2.3 Principal Component Analysis -- 3.2.4 When Machines Learn in 3D, Alignment IS EVERYTHING -- 3.2.5 Activity Range and Distribution of Activities for QSAR Modelling -- 3.2.6 Outliers -- 3.3 Deep Learning and Neural Networks -- 3.4 Underlying Mathematics -- 3.4.1 "Observables" and "Features" -- 3.4.1.1 Molecular Descriptors and Fingerprints -- 3.4.2 Comparing Molecules Based on Their Descriptors
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| 505 |
8 |
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|a 3.4.2.1 Tanimoto Similarity -- 3.4.2.2 Tversky Similarity -- 3.4.2.3 Dice Similarity -- 3.4.2.4 Which Similarity Metric Works Best? -- 3.4.3 Model Quality and Statistics -- 3.4.3.1 Coefficients of Determination -- 3.4.3.2 Kendall's Tau -- 3.4.4 Overtraining and Characteristics of Good Descriptors -- 3.5 Machine Learning Methods -- 3.5.1 k-Nearest Neighbours -- 3.5.2 Linear Regression -- 3.5.2.1 Ordinary or Partial Least Squares? -- 3.5.3 Decision Trees and Random Forests -- 3.5.3.1 Information -- 3.5.3.2 Algorithms -- 3.5.3.3 Decision Tree Ensembles -- 3.5.4 Support Vector Machines
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| 505 |
8 |
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|a 3.5.4.1 Hyperplanes -- 3.5.4.2 Kernels -- 3.5.4.3 Support Vectors -- 3.6 Summary -- Acknowledgements -- References -- Chapter 4 Machine Learning for Nonadiabatic Molecular Dynamics -- 4.1 Introduction -- 4.2 Methods -- 4.2.1 Machine Learning (ML) Models -- 4.2.1.1 Linear Model (LR) -- 4.2.1.2 Kernel Ridge Regression (KRR) -- 4.2.1.3 Support Vector Regression (SVR) -- 4.2.1.4 Neural Networks (NNs) -- 4.2.1.5 Training Process -- 4.2.1.6 Descriptors -- 4.2.1.7 Distance-matrix Based Descriptors -- 4.2.1.8 FCHL: Faber-Christensen-Huang-Lilienfeld Representation -- 4.2.2 Training Set Generation
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| 505 |
8 |
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|a 4.2.2.1 Initial Training Set -- 4.2.2.2 Adaptive Sampling for Excited States -- 4.2.3 Wave Function Phases -- 4.2.4 Phase Correction Algorithm -- 4.2.5 Surface Hopping Dynamics -- 4.3 Example: Methylenimmonium Cation -- 4.3.1 ML Surface Hopping Dynamics -- 4.3.2 Energy Conservation -- 4.3.3 Further Tools of ML Models -- 4.4 Conclusion and Outlook -- References -- Chapter 5 Machine Learning in Science -- A Role for Mechanical Sympathy? -- 5.1 Introduction -- 5.1.1 A Little History -- 5.1.2 Challenges -- 5.2 Problems and Solutions -- 5.2.1 How Many Samples do I Need to Train an AI?
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| 520 |
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|a This book provides practical examples of machine learning applied to science to help researchers make an informed choice about using the method in chemistry.
|
| 588 |
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|a Description based on online resource; title from digital title page (viewed on September 24, 2020).
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|a Chemistry
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|z 9781788017893
|w (OCoLC)1173575313
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|a Theoretical and computational chemistry ;
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