The document discusses advancements in QSAR modeling methods and automation, focusing on the meta QSAR framework and associated databases such as Wombat and EBI Stardrop. It details model building techniques, evaluation, and the integration of cloud computing resources for expanded data analysis. Additionally, it addresses the ongoing development of new methods and their applications in property prediction and classification models.

1. Meta QSAR David Leahy

2. Structures Data Descriptor Descriptor Descriptor Descriptor Test Set Filter Filter Filter Filter Model Model Model Model Transform Transform Train Train Predict Predict Predict Predict Predict Predict Predict Predict

3. Calculate Descriptors Filter feature request ... Model build request ... Calculate descriptors request ... responses responses responses Planner Model Build Filter Features

4. Calculate Descriptors Filter feature request ... Model build request ... Calculate descriptors request ... responses responses responses responses responses responses Planner Model Build Filter Features Calculate Descriptors

6. Meta QSAR. Methods Wombat Database (thanks to Tudor) First 80 data sets (human, n >75, Range > 2 Logs)

7. Miscellaneous (HSA, Herg, CHI, Cl I ) Descriptors CDK, CDL, MOPAC, HQSAR, E-State, H-State, LSER, AlogP, XlogP Filter Feature Hall method Model Building (Continuous) RNN, Rlinear, Rpart, RPLS, GUIDE

9. Model Assessment & Selection Validity Statistical tests

10. y-scrambling Stability

11. Domain of Application

19. Meta QSAR: Automation of Decision Making in QSAR Modelling

20. Mother of All QSAR Study Expand data series Wombat Database (~ 1000 datasets)

21. EBI Stardrop Database (~2-3000 datasets)