Biases in In Silico Evaluation of Molecular Optimization Methods and Bias-Reduced Evaluation Methodology

Molecular optimization aims to discover novel molecules with improved properties, which is often formulated as a reinforcement learning problem by modeling the construction of a molecule using a Markov decision process. The performance of such agents is measured by the quality of generated molecules. In the community of machine learning, most of the molecular optimization methods have been verified in silico, i.e., in computer simulation. Since most of the generated molecules are novel, their properties are unknown and we have to resort to a predictor to estimate the properties. However, little attention has been paid to how reliable such estimates are, which makes the existing performance estimates less reliable. In this paper, we study the statistical performance of such performance estimators to enhance our understanding of the evaluation protocol and we discuss several directions to improve it. Let us first introduce a common practice to estimate the performance in silico.