论文标题
AI驱动分子优化的零阶优化方法的经验评估
An Empirical Evaluation of Zeroth-Order Optimization Methods on AI-driven Molecule Optimization
论文作者
论文摘要
分子优化是化学发现中的一个重要问题，并已使用许多技术（包括生成建模，增强学习，遗传算法等）来解决。最近的工作还应用了零阶（ZO）优化，这是无梯度优化的子集，该梯度优化与基于梯度的方法相似，以优化自动编码器的潜在矢量表示。在本文中，我们研究了各种ZO优化方法优化分子目标的有效性，这些方法的特征是可变的平滑度，不经常的Optima和其他挑战。我们提供有关此环境中各种ZO优化器鲁棒性的见解，显示基于ZO标志的梯度下降（ZO-SignGD）的优势，讨论如何实际使用ZO优化在现实的发现任务中，并证明ZO优化方法的潜在有效性是从Guacamolol套件中广泛使用的Benchmark任务。代码可在以下网址获得：https：//github.com/ibm/qmo-bench。
Molecule optimization is an important problem in chemical discovery and has been approached using many techniques, including generative modeling, reinforcement learning, genetic algorithms, and much more. Recent work has also applied zeroth-order (ZO) optimization, a subset of gradient-free optimization that solves problems similarly to gradient-based methods, for optimizing latent vector representations from an autoencoder. In this paper, we study the effectiveness of various ZO optimization methods for optimizing molecular objectives, which are characterized by variable smoothness, infrequent optima, and other challenges. We provide insights on the robustness of various ZO optimizers in this setting, show the advantages of ZO sign-based gradient descent (ZO-signGD), discuss how ZO optimization can be used practically in realistic discovery tasks, and demonstrate the potential effectiveness of ZO optimization methods on widely used benchmark tasks from the Guacamol suite. Code is available at: https://github.com/IBM/QMO-bench.
