LICO: Large Language Models for In-Context Molecular Optimization

Understand

Optimizing black-box functions is a fundamental problem in science and engineering.

• To solve this problem, many approaches learn a surrogate function that estimates the underlying objective from limited historical evaluations.
• Large Language Models (LLMs), with their strong pattern-matching capabilities via pretraining on vast amounts of data, stand out as a potential candidate for surrogate modeling.
• However, directly prompting a pretrained language model to produce predictions is not feasible in many scientific domains due to the scarcity of domain-specific data in the pretraining corpora and the challenges of articulating complex problems in natural language.