Generative Multi-Objective Bayesian Optimization with Scalable Batch Evaluations for Sample-Efficient De Novo Molecular Design

This article presents a research paper on a specific application of AI in molecular design. The focus is on improving the efficiency of the design process by using generative models and Bayesian optimization techniques. The paper likely explores methods to reduce the number of samples needed for effective molecular design, which is crucial for saving time and resources. The use of 'scalable batch evaluations' suggests an effort to optimize the computational aspects of the process.

• •Focus on sample-efficient molecular design.
• •Utilizes generative models and Bayesian optimization.
• •Emphasizes scalable batch evaluations for computational efficiency.