Biases in Evaluation of Molecular Optimization Methods and Bias Reduction Strategies
Proceedings of the 40th International Conference on Machine Learning, PMLR 202:15567-15585, 2023.
We are interested in an evaluation methodology for molecular optimization. Given a sample of molecules and their properties of our interest, we wish not only to train a generator of molecules optimized with respect to a target property but also to evaluate its performance accurately. A common practice is to train a predictor of the target property using the sample and apply it to both training and evaluating the generator. However, little is known about its statistical properties, and thus, we are not certain about whether this performance estimate is reliable or not. We theoretically investigate this evaluation methodology and show that it potentially suffers from two biases; one is due to misspecification of the predictor and the other to reusing the same finite sample for training and evaluation. We discuss bias reduction methods for each of the biases, and empirically investigate their effectiveness.