Multi-Objective Bayesian Optimization with Independent Tanimoto Kernel Gaussian Processes for Diverse Pareto Front Exploration

TL;DR

GP-MOBO is a new multi-objective Bayesian optimization method that efficiently explores chemical space, producing higher-quality molecules and better Pareto front coverage with minimal computational resources.

Contribution

It introduces a fast Gaussian Process-based approach that fully utilizes molecular fingerprint dimensionality for improved molecular optimization.

Findings

Outperforms traditional methods like GP-BO in molecular optimization tasks.

Achieves broader chemical space exploration and better Pareto front proximity.

Demonstrates higher geometric mean values across multiple optimization iterations.

Abstract

We present GP-MOBO, a novel multi-objective Bayesian Optimization algorithm that advances the state-of-the-art in molecular optimization. Our approach integrates a fast minimal package for Exact Gaussian Processes (GPs) capable of efficiently handling the full dimensionality of sparse molecular fingerprints without the need for extensive computational resources. GP-MOBO consistently outperforms traditional methods like GP-BO by fully leveraging fingerprint dimensionality, leading to the identification of higher-quality and valid SMILES. Moreover, our model achieves a broader exploration of the chemical search space, as demonstrated by its superior proximity to the Pareto front in all tested scenarios. Empirical results from the DockSTRING dataset reveal that GP-MOBO yields higher geometric mean values across 20 Bayesian optimization iterations, underscoring its effectiveness and efficiency in addressing complex multi-objective optimization challenges with minimal computational overhead.