In the realm of computational optimisation, Bayesian Optimisation (BO) has emerged as a pivotal tool, especially within the pharmaceutical industry. BO is a method that helps find the best solutions for complex problems by making informed guesses and learning from the results. Two prominent implementations of BO have been developed and open-sourced by Bristol-Myers Squibb (BMS) and Merck; they each offer unique features and capabilities. In this blog, LabLinks dives into the strengths and challenges of these two systems, providing a comprehensive comparison of the BMS and Merck Bayesian Optimisation Systems for the scientific community.
Bristol-Myers Squibb (BMS) Bayesian Optimisation
GitHub Repository: BMS Bayesian Optimisation
BMS's Bayesian Optimisation tool leverages Gaussian Processes (GP) for modelling the objective function. The choice of GP is significant as it provides a probabilistic approach to modelling, which is crucial for capturing uncertainty in predictions. The tool supports various acquisition functions such as Expected Improvement (EI), Probability of Improvement (PI), and Upper Confidence Bound (UCB), each offering different strategies to balance exploration and exploitation during optimisation.
A notable strength of the BMS BO system is its emphasis on customisation and flexibility. Users can easily tailor the kernels and acquisition functions to meet specific needs, making the system adaptable to a wide range of optimisation problems. The integration with popular machine learning libraries like Scikit-learn further extends its usability, allowing for seamless incorporation into existing workflows.
Scalability is another key feature of the BMS BO system. It is designed to handle large-scale optimisation problems efficiently, incorporating features for parallel and distributed optimisation. This capability is crucial for pharmaceutical applications where computational resources are often extensive, and the optimisation problems are complex.
Visualisation tools within the BMS BO system are robust, offering detailed insights into the optimisation process and the resulting surrogate models. Users can generate plots to analyse convergence and performance, aiding in the interpretation and refinement of the optimisation strategy. Comprehensive documentation and an active community further enhance the system's usability, providing ample support for both new and experienced users.
Merck Bayesian Optimisation
GitHub Repository: Merck Bayesian Optimisation
Merck's Bayesian Optimisation system also utilises Gaussian Processes, but it distinguishes itself with the inclusion of advanced acquisition functions such as q-Probability of Improvement (qPI) and q-Expected Improvement (qEI). Merck provides a broader range of options for selecting recommenders to initiate the search such as Farthest Point Sampling and KMEANS clustering. These functions are designed to optimise the decision-making process, offering more sophisticated strategies for navigating the search space.
The modular design of Merck's BO system allows for extensive customisation, enabling users to modify every component of the optimisation process. This flexibility is particularly beneficial for complex and varied optimisation tasks, where the ability to tweak specific elements can lead to significant performance improvements.
Optimised for high-performance computing environments, Merck's BO system excels in scalability. It supports asynchronous parallel execution, which accelerates the optimisation process by allowing multiple evaluations to occur simultaneously. This feature is especially advantageous in large-scale scenarios where computational efficiency is paramount.
Merck's system also stands out with its flexible visualisation capabilities. While it doesn't come with built-in plotting tools, it allows users to leverage Python libraries to create custom visualisations. This enables users to see where their objective is approaching optimal values, providing deep insights into the optimisation dynamics. Such visualisations can be invaluable for identifying trends, anomalies, and opportunities for improvement.
Detailed documentation and strong community engagement ensure that users have access to the resources and support they need. The active development and contributions from the community also mean that the system continues to evolve and improve, incorporating new features and enhancements over time.
Comparative Insights
Algorithm Implementation: While both BMS and Merck BO systems use Gaussian Processes, Merck’s implementation offers more advanced acquisition functions. The Knowledge Gradient (KG) and Entropy Search (ES) functions can provide more efficient search strategies, particularly in high-dimensional or complex search spaces.
Customisation and Flexibility: Both systems are highly customisable, but Merck’s modular design may offer greater ease in tweaking individual components. This modularity allows users to experiment with different configurations and fine-tune the optimisation process to their specific needs.
Scalability: Both systems are designed for scalability, but Merck’s system, optimised for high-performance computing, may provide superior performance in large-scale scenarios. The support for asynchronous parallel execution is a significant advantage in environments where computational resources are abundant.
Visualisation: Both systems offer robust visualisation capabilities, but Merck's system is more flexible for in-depth analysis. Although it doesn't have built-in interactive plots, users can leverage Python libraries to create custom visualisations. This approach allows for deeper insights into the optimisation process, facilitating better decision-making and strategy refinement.
Documentation and Community: Both boast comprehensive documentation and active communities, ensuring that users can find the help and information they need. However, the choice between them may depend on the specific support and community engagement required for a given project.
Conclusion
Choosing between the BMS and Merck Bayesian Optimisation systems depends largely on the specific requirements of the optimisation task at hand. Merck’s system might be better suited for high-complexity and high-performance computing environments, offering advanced features and interactive visualisation tools. Conversely, BMS’s system provides a solid, flexible foundation suitable for a broad range of applications.
Both systems represent significant contributions to the field, empowering scientists and researchers to tackle complex optimisation problems with greater efficiency and precision. For those looking to explore these tools, the respective GitHub repositories offer a wealth of resources and examples to get started.
As the field of Bayesian Optimisation continues to evolve, contributions from leading pharmaceutical companies like BMS and Merck will undoubtedly drive further innovations and applications across various scientific domains. The ongoing development and open-source nature of these tools promise a bright future for optimisation techniques, paving the way for new breakthroughs and advancements in science and technology.
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