Bayesian Optimization in Chemical Compound Sub-Spaces using Low-Dimensional Molecular Descriptors

TL;DR

This paper introduces a Bayesian optimization framework that efficiently discovers molecules with targeted properties using low-dimensional descriptors and fewer than 2,000 data points, enabling practical small-data molecular design.

Contribution

The study presents a novel, data-efficient Bayesian optimization method with a reliable inverse mapping for discrete molecular structures, improving chemical compound optimization in low-data regimes.

Findings

Achieves 100% success rate in entropy optimization with fewer than 1,000 evaluations.

Over 80% success rate in ZPVE optimization for molecules with more than two heavy atoms.

Demonstrates effective inverse mapping from descriptor space to valid molecular structures.

Abstract

Efficient optimization of molecules with targeted properties remains a significant challenge due to the vast size and discrete nature of chemical compound space. Conventional machine-learning-based optimization approaches typically require large datasets to construct accurate surrogate models, limiting their applicability in data-scarce settings. In this study, we present a Bayesian optimization (BO) framework that identifies optimal molecular structures with high precision using fewer than 2,000 training data points within a chemical subspace containing more than 133,000 molecules. The framework employs a low-dimensional and physics-informed molecular descriptor vector that facilitates data-efficient surrogate modelling and optimization. A key innovation of the proposed framework is a reliable inverse mapping scheme that translates optimized points in the descriptor space back into chemically valid molecular structures, thereby bridging continuous optimization and discrete molecular design. We demonstrate the effectiveness of our approach on the QM9 benchmark dataset, where the framework successfully identifies organic molecules with the target entropy and zero-point vibrational energy (ZPVE) values.For entropy optimization, our approach achieves a 100% success rate while requiring fewer than 1,000 molecular evaluations in more than 80% of test cases. For ZPVE, the success rate exceeds 80% for molecules containing more than two heavy atoms. These results highlight the critical role of low-dimensional, interpretable descriptors in enabling data-efficient optimization and robust inverse molecular design, and establish Bayesian optimization as a practical tool for molecular discovery in small-data regimes.