Global Quantitative Structure–Activity Relationship Models vs Selected Local Models as Predictors of Off-Target Activities for Project Compounds

In the pharmaceutical industry, it is common for large numbers of compounds to be tested for off-target activities. Given a compound synthesized for an on-target project P, what is the best way to predict its off-target activity X? Is it better to use a global quantitative structure–activity relationship (QSAR) model calibrated against all compounds tested for X, or is it better to use a local model for X calibrated against only the set of compounds in project P? The literature is not consistent on this topic, and strong claims have been made for either. One particular idea is that local models will be superior to global models in prospective prediction if one generates many local models and chooses the type of local model that best predicts recent data. We tested this idea via simulated prospective prediction using in-house data involving compounds in 11 projects tested for 9 off-target activities. In our hands, the local model that best predicts the recent past is seldom the local model that is best at predicting the immediate future. Also, the local model that best predicts the recent past is not systematically better than the global model. This means the complexity of having project- or series-specific models for X can be avoided; a single global model for X is sufficient. We suggest that the relative predictivity of global vs local models may depend on the type of chemical descriptor used. Finally, we speculate why, contrary to observation, intuition suggests local models should be superior to global models.