Machine Learning in Drug Discovery

Abstract

Contents

1. Molecular Representations/Descriptors in Machine Learning-Based Drug Development
   1.1 Molecular Graph Theory
       1.1.1 Introduction To The Molecular Graph Representation
       1.1.2 Mathematical Defintion of a Graph
       1.1.3 Graph Traversal Algorithms
       1.1.4 Molecular Graph Reprentations
       1.1.5 Advantages of Molecular Graph Representations
       1.1.6 Disadvantages of Molecular Graph Representations
       1.1.7 Molecular Graphs in AI-Driven Small Molecule Drug Discovery
       1.1.8 References
   1.2 Molecular Descriptors
       1.2.1 Introduction to Molecular Descriptors
       1.2.2 Molecular Fingerprints
       1.2.3 Key-Based Molecular Fingerprints - MACCS Keys
       1.2.4 Hash-Based Molecular Fingerprints - Daylight Fingerprint & ECFPs
       1.2.5 Advantages & Applications of Molecular Fingerprints
       1.2.6 Molecular Fingerprints in Machine Learning
       1.2.7 References
   
2. Machine Learning-Based Drug Development
   2.1 Introduction to Machine Learning
       2.1.1 How does Machine Learning Work?
       2.1.2 Machine Learning Methods
       2.1.3 Machine Learning Notation
       2.1.4 References
   2.2 Supervised Learning
       2.2.1 Classification Algorithms in Supervised Learning
       2.2.2 Regression Algorithms in Supervised Learning