MGOL: Molecule Generation with Ordering Loss

Abstract

The discovery of new pharmaceutical drugs remains an essential yet costly, time-consuming task in biomedical research. Recent developments in machine learning and bioinformatics have significantly accelerated this process. State-of-the-art approaches employ neural networks to optimize latent space vectors representing target molecules. In such processes, the optimized parameters correspond to specific chemical properties whose exact mathematical formulations are unknown; where only limited discrete measurements are available and difficult to approximate. We propose a novel loss function that effectively captures chemical property variations by enforcing molecular ordering based on their property values. Our experiments demonstrate that this "ordering loss" outperforms conventional mean squared error (MSE) as an optimization objective for property functions. The ordering loss enables surrogate functions to mirror the variation patterns of black-box property functions, establishing a new state-of-the-art framework for molecule discovery optimization. It exhibits superior performance under data scarcity constraints and shows promising potential for broader black-box function optimization applications. The code is available at: com/VincentH23/MGOL.