A data-driven generative strategy to avoid reward hacking in multi-objective molecular design.

ABSTRACT: Molecular design using data-driven generative models has emerged as a promising technology, impacting various fields such as drug discovery and the development of functional materials. However, this approach is often susceptible to optimization failure due to reward hacking, where prediction models fail to extrapolate, i.e., fail to accurately predict properties for designed molecules that considerably deviate from the training data. While methods for estimating prediction reliability, such as the applicability domain (AD), have been used for mitigating reward hacking, multi-objective optimization makes it challenging. The difficulty arises from the need to determine in advance whether the multiple ADs with some reliability levels overlap in chemical space, and to appropriately adjust the

SUBMITTER: Yoshizawa T 

PROVIDER: S-EPMC11897179 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature