论文标题
分子优化方法和偏置评估方法的硅化评估中的偏见
Biases in In Silico Evaluation of Molecular Optimization Methods and Bias-Reduced Evaluation Methodology
论文作者
论文摘要
我们对分子优化方法的硅评估方法感兴趣。鉴于分子的样本及其感兴趣的特性，我们不仅希望训练可以找到针对目标特性优化的分子的代理，而且还希望评估其性能。一种常见的做法是训练样品中目标特性的预测指标，并将其用于培训和评估代理。我们表明，该评估者可能患有两个偏见。一种是由于预测变量指定错误，另一个是为了重用同一样本进行培训和评估。我们全面讨论了每种偏见的偏见减少方法，并在经验上研究了它们的有效性。
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 an agent that can find molecules optimized with respect to the target property but also to evaluate its performance. A common practice is to train a predictor of the target property on the sample and use it for both training and evaluating the agent. We show that this evaluator 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 comprehensively, and empirically investigate their effectiveness.
