The perception of bitter and sweet tastes is a crucial aspect of human sensory experience. Concerns over the long-term use of aspartame, a widely used sweetener suspected of carcinogenic risks, highlight the importance of developing new taste modifiers. This study utilizes Large Language Models (LLMs) such as GPT-3.5 and GPT-4 for predicting molecular taste characteristics, with a focus on the bitter-sweet dichotomy. Employing random and scaffold data splitting strategies, GPT-4 demonstrated superior performance, achieving an impressive 86% accuracy under scaffold partitioning. Additionally, ChatGPT was employed to extract specific molecular features associated with bitter and sweet tastes. Utilizing these insights, novel molecular compounds with distinct taste profiles were successfully generated. These compounds were validated for their bitter and sweet properties through molecular docking and molecular dynamics simulation, and their practicality was further confirmed by ADMET toxicity testing and DeepSA synthesis feasibility. This research highlights the potential of LLMs in predicting molecular properties and their implications in health and chemical science.

中文翻译：

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探索苦与甜：大语言模型在分子味觉预测中的应用


苦味和甜味的感知是人类感官体验的一个重要方面。阿斯巴甜是一种广泛使用的甜味剂，疑似具有致癌风险，对长期使用的担忧凸显了开发新口味调节剂的重要性。本研究利用 GPT-3.5 和 GPT-4 等大型语言模型 (LLMs) 来预测分子味道特征，重点关注苦甜二分法。采用随机和脚手架数据分割策略，GPT-4 表现出了卓越的性能，在脚手架分割下实现了令人印象深刻的 86% 准确率。此外，ChatGPT 还被用来提取与苦味和甜味相关的特定分子特征。利用这些见解，成功生成了具有独特口味特征的新型分子化合物。通过分子对接和分子动力学模拟验证了这些化合物的苦味和甜味特性，并通过ADMET毒性测试和DeepSA合成可行性进一步证实了它们的实用性。这项研究强调了 LLMs 在预测分子特性及其对健康和化学科学的影响方面的潜力。