XMOL: Explainable Multi-property Optimization of Molecules

TL;DR

XMOL is a novel framework that enables simultaneous multi-property molecular optimization with built-in explainability, improving efficiency and interpretability in drug discovery and material science.

Contribution

It introduces a multi-property optimization method based on geometric diffusion models with explainability features, addressing inefficiencies and lack of transparency in prior approaches.

Findings

Effective multi-property optimization demonstrated on QM9 dataset

Provides interpretable results during the molecular design process

Outperforms existing single-property methods in efficiency

Abstract

Molecular optimization is a key challenge in drug discovery and material science domain, involving the design of molecules with desired properties. Existing methods focus predominantly on single-property optimization, necessitating repetitive runs to target multiple properties, which is inefficient and computationally expensive. Moreover, these methods often lack transparency, making it difficult for researchers to understand and control the optimization process. To address these issues, we propose a novel framework, Explainable Multi-property Optimization of Molecules (XMOL), to optimize multiple molecular properties simultaneously while incorporating explainability. Our approach builds on state-of-the-art geometric diffusion models, extending them to multi-property optimization through the introduction of spectral normalization and enhanced molecular constraints for stabilized training. Additionally, we integrate interpretive and explainable techniques throughout the optimization process. We evaluated XMOL on the real-world molecular datasets i.e., QM9, demonstrating its effectiveness in both single property and multiple properties optimization while offering interpretable results, paving the way for more efficient and reliable molecular design.