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XMOL: Explainable Multi-property Optimization of Molecules

Aye Phyu Phyu Aung, Jay Chaudhary, Ji Wei Yoon, Senthilnath Jayavelu

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.

XMOL: Explainable Multi-property Optimization of Molecules

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.
Paper Structure (10 sections, 1 theorem, 15 equations, 1 figure, 2 tables)

This paper contains 10 sections, 1 theorem, 15 equations, 1 figure, 2 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Generative Models for Molecular Optimization
  4. Explainable Design for Molecular Structures
  5. XMOL
  6. Constrained Conditional Optimization
  7. Multi-property predicting EGNN
  8. Model Explainability for Property Regression
  9. Experiments
  10. Conclusion

Key Result

Theorem 1

Let $f: \mathbb{R}^n \to \mathbb{R}$ be a function defined by a composition of layers in EGNN, such that $f = g_k \circ \alpha_k \circ g_{k-1} \circ \dots \circ g_1$, where each layer $g_i$ represents a linear and $\alpha$ represents a non-linear function. Then, the spectral normalization process ma

Figures (1)

  • Figure 1: Graph and bar plots illustrating the contributions by each edge of the generated molecule to EGNN prediction

Theorems & Definitions (2)

  • Theorem 1
  • proof