Predicting Potent Compounds via Model-Based Global Optimization

Finding potent compounds for a given target in silico can be viewed as a constraint global optimization problem. This requires the use of an optimization function for which evaluations might be costly. The major task is maximizing the function while minimizing the number of evaluation steps. To solve this problem, we propose a machine learning algorithm, which first builds a statistical QSAR-model of the SAR landscape and then uses the model to identify regions in compound space having a high probability to contain a highly potent compound. For this purpose, we devise the so-called expected potency improvement (El) criterion to rank candidate compounds with respect to their likelihood to exhibit higher potency than the most active compound in the training data. Therefore, this approach significantly differs from a purely prediction-oriented classical QSAR model. The method is superior to a nearest neighbor approach as significantly fewer evaluation steps are needed to identify the most potent compound for the given target.