Surrogate Modeling via Factorization Machine and Ising Model with Enhanced Higher-Order Interaction Learning
This work addresses the need for more accurate predictive models in drug discovery, though it is incremental as it builds on an existing surrogate modeling approach.
The paper tackled the problem of building efficient surrogate models for predicting drug combination effects by enhancing a factorization machine with slack variables to capture higher-order interactions, resulting in a notable performance improvement.
Recently, a surrogate model was proposed that employs a factorization machine to approximate the underlying input-output mapping of the original system, with quantum annealing used to optimize the resulting surrogate function. Inspired by this approach, we propose an enhanced surrogate model that incorporates additional slack variables into both the factorization machine and its associated Ising representation thereby unifying what was by design a two-step process into a single, integrated step. During the training phase, the slack variables are iteratively updated, enabling the model to account for higher-order feature interactions. We apply the proposed method to the task of predicting drug combination effects. Experimental results indicate that the introduction of slack variables leads to a notable improvement of performance. Our algorithm offers a promising approach for building efficient surrogate models that exploit potential quantum advantages.