Optimizing molecular design through Multi-Armed Bandits and adaptive discretization: A computational benchmark investigation

Abstract

In the present benchmark study, a novel strategy is unveiled for the optimization of molecular design by integrating Multi-Armed Bandits (MAB) with cutting-edge adaptive discretization techniques. Central to this approach is the employment of the Ultrafast Shape Recognition (USR) method a proven technique for assessing molecular similarity. Moreover, the integration of the Zooming Algorithm is noteworthy. This innovative algorithm demonstrates dynamism, adjusting in real-time to adeptly navigate the vast expanse of chemical space. One of the standout revelations from this investigation is the significant influence of a scaling factor. It serves as the fulcrum for striking an optimal balance between computational agility and peak performance. Such insights profoundly challenge the limitations inherent in conventional discrete MAB methodologies, especially when operating within the bounds of finite computational bandwidth. Beyond merely delineating a blueprint for future interdisciplinary endeavors, this research illuminates the intricacies of molecular design optimization. Additionally, it suggests that a marriage between network and cluster analysis could be the key to enhancing and fine-tuning the reinforcement learning journey.