Data Analysis in QSAR
- 1. Data analysis in QSAR Noel O’Boyle Dave Palmer, John Mitchell
- 2. Quantitative Structure-Activity Relationship (QSAR) Also QSPR (Property) Perceive Physical Structure Predict property Propose
- 3. Quantitative Structure-Activity Relationship (QSAR) Also QSPR (Property) Perceive Physical Structure Predict property Propose Descriptors Molecular weight No.of H-bonding groups Surface area MOE: 180 descriptors
- 4. Quantitative Structure-Activity Relationship (QSAR) Need to use an estimate of the error Optimise model on training set (2/3) of prediction (CV or bootstrap, but not Test model on test set (1/3) resubstitution) or Build model on training set (2/3 of 2/3) Optimise on test set (1/3 of 2/3) Using the error of prediction Test model on validation set (1/3)
- 5. Quantitative Structure-Activity Relationship (QSAR) Need to use an estimate of the error Optimise model on training set (2/3) of prediction (CV or bootstrap, but not Test model on test set (1/3) resubstitution) or Build model on training set (2/3 of 2/3) Optimise on test set (1/3 of 2/3) Using the error of prediction Test model on validation set (1/3) Testing must be carried out on a hold-out set from the same distribution as the the set of molecules used for optimisation => don't optimise model on diverse set Assessing Model Fit by Cross-Validation, JCICS, 2003, 43, 579. Beware of q2, J.Mol.Graph.Model., 2002, 20, 269.
- 6. Feature selection Number of descriptors → Features = descriptors 2n different subsets
- 7. Parameter optimisation – Models have parameters which should be tuned • Support vector machines – gamma, cost, epsilon
- 8. QSAR: Feature selection and parameter optimisation • Aim: To find a robust method of simultaneously optimising the parameters and performing feature selection • Prediction of solubility for drug-like molecules – David Palmer – Support vector machines • gamma, cost, epsilon – 127 descriptors • Large search space – stochastic algorithm required
- 9. Ant Colony Optimisation (ACO) • Insipired by behaviour of ants foraging for food • Ants lay down pheromones, which influence the path taken by other ants. Meanwhile pheromones are evaporating. • Ants’ trails converge to shortest path between nest and food • Ant Colony Optimisation (ACO) – Marco Dorigo, PhD Thesis, 1992. • ACO for feature selection – Shen et al, JCIM, 2005, 45, 1024. • We have extended it to perform simultaneous parameter optimisation
- 10. Ant Colony Optimisation (ACO) • population of ants (typically 50 to 100) – each ant represents a model – i.e. a subset of descriptors and values for the parameters – each ant has a fitness score, e.g. 10-fold cross validation rmse • it is more likely that an ant will choose a particular descriptor/parameter value in the next iteration if – many ants have chosen it in this iteration (local search), or – many ants have chosen it in their best models to date (global search) • for descriptors/parameter values that are not chosen in the current or best models, the probability that they will be chosen decreases (evaporation)
- 11. Does it work?