Accelerating Black-Box Molecular Property Optimization by Adaptively Learning Sparse Subspaces

TL;DR

MPO is hindered by vast discrete search spaces and expensive evaluations. The paper introduces MolDAIS, which operates directly in a numerical descriptor space by leveraging Mordred descriptors ($D \approx 2000$) and a sparse axis-aligned GP (SAAS-GP) prior to adaptively learn a small, interpretable subspace for Bayesian optimization. Through Hamiltonian Monte Carlo inference over kernel hyperparameters and EI-based acquisition implemented in BoTorch, MolDAIS achieves strong data efficiency, outperforming multiple baselines on logP in the Zinc dataset ($|\mathcal{Z}| = 250{,}000$) and solvation/redox tasks in quinones (>$100{,}000$ candidates). The results highlight that a few descriptors dominate the predictive signal, enabling rapid convergence to near-optimal molecules with as few as $100$ queries, and demonstrate practical impact for drug design and materials discovery through interpretable, scalable MPO optimization.

Abstract

Molecular property optimization (MPO) problems are inherently challenging since they are formulated over discrete, unstructured spaces and the labeling process involves expensive simulations or experiments, which fundamentally limits the amount of available data. Bayesian optimization (BO) is a powerful and popular framework for efficient optimization of noisy, black-box objective functions (e.g., measured property values), thus is a potentially attractive framework for MPO. To apply BO to MPO problems, one must select a structured molecular representation that enables construction of a probabilistic surrogate model. Many molecular representations have been developed, however, they are all high-dimensional, which introduces important challenges in the BO process -- mainly because the curse of dimensionality makes it difficult to define and perform inference over a suitable class of surrogate models. This challenge has been recently addressed by learning a lower-dimensional encoding of a SMILE or graph representation of a molecule in an unsupervised manner and then performing BO in the encoded space. In this work, we show that such methods have a tendency to "get stuck," which we hypothesize occurs since the mapping from the encoded space to property values is not necessarily well-modeled by a Gaussian process. We argue for an alternative approach that combines numerical molecular descriptors with a sparse axis-aligned Gaussian process model, which is capable of rapidly identifying sparse subspaces that are most relevant to modeling the unknown property function. We demonstrate that our proposed method substantially outperforms existing MPO methods on a variety of benchmark and real-world problems. Specifically, we show that our method can routinely find near-optimal molecules out of a set of more than $>100$k alternatives within 100 or fewer expensive queries.

Figures (2)

• Figure 1: Left: logP maximization. Middle: Solvation free energy maximization. Right: Feature importance and test accuracy for solvation free energy GP model.
• Figure 2: Top Left: Convergence results for maximization of redox potential versus number of iterations. Top Right: The distribution in the best maximum found at the final iteration over the replicates. Bottom Left: Inverse squared lengthscale values sorted in descending order for the SAAS-GP model at the final iteration. Bottom Right: Test RMSE values for the standard GP and SAAS-GP models given random training sets of different sizes.