In this groundbreaking study, we introduce the Single-point Chemical Language Model (SpCLM), a novel and robust framework engineered to advance molecular design and optimization. By leveraging the sophisticated transformer architecture and directing our attention towards single-point molecular optimization, our model demonstrates an exceptional ability to enhance pharmacological properties in a manner that aligns with the practical wisdom of medical chemists. Through rigorous optimization protocols and the generation of a mere few hundred compounds, we have achieved structurally refined molecules that exhibit a strong correlation with experimental activity data, achieving a high degree of consistency with measured binding affinities and functional outcomes. This research emphasizes the potential of our model as a revolutionary tool in drug design, enabling precise, data-driven modifications to molecules that significantly improve the activity and selectivity of lead compounds. By employing single-point optimization strategies, SpCLM predicts 60%-80% of active compounds in an independent test set from a small pool of generated molecules, typically numbering in the hundreds. This approach significantly reduces the need for extensive experimental screening, thereby minimizing time and resource costs and setting a new standard for AI-driven advancements in pharmaceutical research and development.

中文翻译：

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零样本单点分子优化模型：模拟药物化学家的专业知识


在这项开创性的研究中，我们介绍了单点化学语言模型 （SpCLM），这是一个新颖而强大的框架，旨在推进分子设计和优化。通过利用复杂的 transformer 架构并将我们的注意力引导到单点分子优化上，我们的模型展示了一种与医学化学家的实践智慧相一致的方式增强药理学特性的非凡能力。通过严格的优化方案和仅几百种化合物的生成，我们实现了结构精细的分子，这些分子与实验活性数据表现出很强的相关性，实现了与测量的结合亲和力和功能结果的高度一致性。这项研究强调了我们的模型作为药物设计革命性工具的潜力，能够对分子进行精确的、数据驱动的修饰，从而显着提高先导化合物的活性和选择性。通过采用单点优化策略，SpCLM 从一小群生成的分子（通常为数百个）中预测独立测试集中 60%-80% 的活性化合物。这种方法大大减少了对大量实验筛选的需求，从而最大限度地减少了时间和资源成本，并为 AI 驱动的药物研发进步设定了新标准。