LICO: Large Language Models for In-Context Molecular Optimization

• URL: http://arxiv.org/abs/2406.18851v1
• Date: Thu, 27 Jun 2024 02:43:18 GMT
• Title: LICO: Large Language Models for In-Context Molecular Optimization
• Authors: Tung Nguyen, Aditya Grover,
• Abstract summary: We introduce LICO, a general-purpose model that extends arbitrary base LLMs for black-box optimization. We train the model to perform in-context predictions on a diverse set of functions defined over the domain. Once trained, LICO can generalize to unseen molecule properties simply via in-context prompting.
• Score: 33.5918976228562
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. In this work, we introduce LICO, a general-purpose model that extends arbitrary base LLMs for black-box optimization, with a particular application to the molecular domain. To achieve this, we equip the language model with a separate embedding layer and prediction layer, and train the model to perform in-context predictions on a diverse set of functions defined over the domain. Once trained, LICO can generalize to unseen molecule properties simply via in-context prompting. LICO achieves state-of-the-art performance on PMO, a challenging molecular optimization benchmark comprising over 20 objective functions.

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