Biases in Evaluation of Molecular Optimization Methods and Bias Reduction Strategies

Hiroshi Kajino, Kohei Miyaguchi, Takayuki Osogami

TL;DR: This paper analyzes biases in the evaluation of molecular optimization methods, and methods to alleviate them.

Abstract: We are interested in in silico evaluation methodology for molecular optimization methods. Given a sample of molecules and their properties of our interest, we wish not only to train a generator of molecules that can find those 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 on the sample and use it for both training and evaluating the generator. 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 sample for training and evaluation. We discuss bias reduction methods for each of the biases, and empirically investigate their effectiveness.

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