用新方法在分子优化中更高效地找到多样且优质的解。
Multi-Objective Bayesian Optimization with Independent Tanimoto Kernel Gaussian Processes for Diverse Pareto Front Exploration
- 基于独立Tanimoto核的高斯过程，直接处理分子指纹全维度。
- 20轮优化后几何均值更高，逼近帕累托前沿效果更好。
- 适合药物分子设计等需多目标探索的场景。
我们提出GP-MOBO，一种新型多目标贝叶斯优化算法，显著提升分子优化性能。该方法结合快速精确高斯过程（GPs）框架，可高效处理稀疏分子指纹的全维度数据，无需大量计算资源。与传统方法相比，GP-MOBO 充分利用指纹维度信息，持续生成更高质量且合法的SMILES结构。实验表明，其在化学空间探索范围更广，所有测试场景下均更接近帕累托前沿。基于DockSTRING数据集的实证结果揭示，在20轮贝叶斯优化中，GP-MOBO实现了更高的几何平均值，证明其在复杂多目标优化中兼具高效性与低计算开销。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。