GAUCHE: A Library for Gaussian Processes in Chemistry (2212.04450v2)

Abstract: We introduce GAUCHE, a library for GAUssian processes in CHEmistry. Gaussian processes have long been a cornerstone of probabilistic machine learning, affording particular advantages for uncertainty quantification and Bayesian optimisation. Extending Gaussian processes to chemical representations, however, is nontrivial, necessitating kernels defined over structured inputs such as graphs, strings and bit vectors. By defining such kernels in GAUCHE, we seek to open the door to powerful tools for uncertainty quantification and Bayesian optimisation in chemistry. Motivated by scenarios frequently encountered in experimental chemistry, we showcase applications for GAUCHE in molecular discovery and chemical reaction optimisation. The codebase is made available at https://github.com/leojklarner/gauche

• The paper introduces GAUCHE, a GP framework designed for chemical applications with specialized kernels for structured molecular data.
• The paper shows that combining tailored molecular representations with the Tanimoto kernel yields competitive regression and uncertainty quantification.
• The paper presents an open-source, GPU-enabled library integrated with tools like GPyTorch, BoTorch, and RDKit to enable efficient Bayesian optimization.

Overview of GAUCHE: A Library for Gaussian Processes in Chemistry

Gaussian processes (GPs) have been pivotal in the domain of probabilistic machine learning, appreciated for their strength in uncertainty quantification and optimization tasks. The paper entitled GAUCHE: A Library for Gaussian Processes in Chemistry introduces GAUCHE, a comprehensive library that implements GPs with a focus on chemical applications. The authors present the challenges of integrating GPs with molecular and chemical data, which generally require sophisticated kernel methods that can handle structured data types such as graphs, strings, and bit vectors. By defining these necessary kernels, GAUCHE aims to harness the potential of GPs for key tasks in chemistry, like molecular discovery and chemical reaction optimization.

Key Contributions and Results

The significant contributions of the paper include:

1. The introduction of a GP framework tailored for applications in chemistry, focusing on molecular and reaction representations.
2. Provision of an open-source, GPU-enabled library based on GPyTorch, BoTorch, and RDKit, facilitating advanced probabilistic modeling and Bayesian optimization (BO).
3. Integration with existing tools, such as GraKel, to extend graph kernel operations within GPs to accelerate the optimization process through hyperparameter tuning.
4. Evaluation of the utility of the GP framework through benchmark experiments concerning regression, uncertainty quantification, and BO tasks across varied datasets.

Empirical results demonstrated that Gaussian processes, particularly when combined with specific molecular representations and kernels, can deliver competitive or superior performance in regression and uncertainty quantification tasks when compared to deep probabilistic models like Bayesian neural networks and deep ensemble methods. The paper notes that the Tanimoto kernel combined with fragprint representations shines in delivering accurate and well-calibrated predictions.

Theoretical and Practical Implications

From a theoretical perspective, GAUCHE's advent paves the way for more robust uncertainty quantification in chemical applications, which has often lagged due to the high dimensionality and discrete nature of chemical data. Practically, the ability of GAUCHE to perform well in small data regimes, typical in chemical experimentation, offers significant utility in early-phase scientific research where generating extensive datasets is often infeasible.

The exploration and specification of different kernel types within GAUCHE highlight the importance of domain-specific adaptations in GPs, encouraging a shift in focus toward structured data types. Additionally, the integration of probabilistic modeling directly with chemistry-specific representations and libraries marks a meaningful advance in computational chemistry, enabling more informed decision-making in experimental design and molecule screening.

Future Directions

The research identifies two primary directions for further development. Firstly, methodological advancements could focus on incorporating multi-fidelity and multi-objective BO techniques to handle larger datasets and more complex optimization tasks. Secondly, user feedback from domain experts in chemistry will play a critical role in refining GAUCHE's applications toward real-world problem-solving, potentially easing the adoption barrier in laboratory settings.

In summary, this paper lays a strong foundation for incorporating Gaussian processes in chemistry, both through the theoretical insights into kernel development for molecular data and through practical contributions to software that caters to the specific needs of chemists working within probabilistic frameworks. As such, GAUCHE presents itself as a valuable tool for researchers looking to exploit the benefits of Bayesian inference in chemical discovery and related domains.