© 2015 Optibrium Ltd. Optibrium™, StarDrop™, Glowing Molecule™, Nova™, Auto-Modeller™, Card View™ and MPO Explorer™ are trademarks of Optibrium Ltd. BIOSTER™ is a trademark of Digital Chemistry Ltd., Derek Nexus™ is a trademark of Lhasa Ltd., torch3D™ is a trademark of Cresset Biomolecular Research Ltd. and Matsy™ is a trademark of NextMove Software Ltd. 1 INTRODUCTION ....................................................................................................................... 5 1.1 StarDrop overview ............................................................................................................................. 5 1.2 Reference guide summary ............................................................................................................... 8 2 PROBABILISTIC SCORING .................................................................................................. 10 2.1 Defining scoring criteria ................................................................................................................. 10 2.2 Importance of uncertainty ............................................................................................................. 14 2.3 Interpreting scores .......................................................................................................................... 16 3 CHEMICAL SPACE AND COMPOUND SELECTION .......................................................... 18 3.1 Introduction ..................................................................................................................................... 18 3.2 Chemical similarity and diversity ................................................................................................... 18 3.3 Visualising chemical space ............................................................................................................. 20 3.4 Selecting compounds ...................................................................................................................... 23 3.5 Limitations and tips ......................................................................................................................... 25 4 GLOWING MOLECULE™ ........................................................................................................ 26 4.1 Introduction ..................................................................................................................................... 26 4.2 Interpretation ................................................................................................................................... 30 5 CHEMINFORMATICS ALGORITHMS .................................................................................. 35 5.1 Clustering ......................................................................................................................................... 35 5.2 Molecular Matched Pair Analysis ................................................................................................... 36 5.3 Activity Landscapes and Cliffs ....................................................................................................... 37 6 ADME QSAR MODELS ............................................................................................................ 38 6.1 Modelling principles ......................................................................................................................... 38 6.2 Data sets........................................................................................................................................... 39 6.3 Descriptors ....................................................................................................................................... 39 6.4 Fitting methods ................................................................................................................................ 40 6.5 Validation .......................................................................................................................................... 42 6.6 Chemical space ................................................................................................................................ 43 6.7 Interpreting model results ............................................................................................................. 44 6.8 Global versus local models ............................................................................................................. 44 7 P450 METABOLISM MODELS .............................................................................................. 45 2 7.1 Introduction to P450 metabolism ................................................................................................. 45 7.2 Modelling principles ......................................................................................................................... 46 7.3 Interpreting model results ............................................................................................................. 49 7.4 Model Performance ......................................................................................................................... 54 8 AUTO-MODELLER™ ................................................................................................................ 55 8.1 Introduction ..................................................................................................................................... 55 8.2 Descriptors ....................................................................................................................................... 56 8.3 Data set preparation ....................................................................................................................... 56 8.4 Modelling techniques ...................................................................................................................... 57 8.5 Gaussian Processes ......................................................................................................................... 59 8.6 Radial Basis Functions with a Genetic Algorithm ........................................................................ 62 8.7 Partial Least Squares ...................................................................................................................... 68 8.8 Decision Trees ................................................................................................................................. 69 8.9 Random Forests ............................................................................................................................... 73 8.10 Confidence in prediction ................................................................................................................. 74 9 MPO EXPLORER™ ................................................................................................................... 75 9.1 Profile Builder ................................................................................................................................... 75 9.2 Sensitivity Analysis .......................................................................................................................... 81 10 NOVA™ IDEA GENERATION ................................................................................................ 86 10.1 Introduction ..................................................................................................................................... 86 10.2 Medicinal Chemistry Transformations .......................................................................................... 86 10.3 Matched Series Analysis ................................................................................................................. 94 11 BIOSTER™ .............................................................................................................................. 100 11.1 The BIOSTER database ................................................................................................................ 100 11.2 Creating Bioisosteric Transformations ........................................................................................ 100 11.3 Predictive Application of Bioisosteric Transformations ............................................................ 102 11.4 Conclusions .................................................................................................................................... 105 12 TORCH3D™ ............................................................................................................................ 106 12.1 Introduction ................................................................................................................................... 106 12.2 What are Field Points? .................................................................................................................. 107 12.3 Interpretation of Field Point Patterns ......................................................................................... 107 12.4 Reference molecules ..................................................................................................................... 108 3 12.5 torch3D Scores .............................................................................................................................. 108 13 DEREK NEXUS™ .................................................................................................................... 109 13.1 Derek Endpoint Descriptions ......................................................................................................