Generative models have demonstrated substantial promise in Natural Language Processing (NLP) and have found application in designing molecules, as seen in General Pretrained Transformer (GPT) models. In our efforts to develop such a tool for exploring the organic chemical space in search of potentially electro-active compounds, we present "LLamol", a single novel generative transformer model based on the LLama 2 architecture, which was trained on a 13M superset of organic compounds drawn from diverse public sources. To allow for a maximum flexibility in usage and robustness in view of potentially incomplete data, we introduce "Stochastic Context Learning" as a new training procedure. We demonstrate that the resulting model adeptly handles single- and multi-conditional organic molecule generation with up to four conditions, yet more are possible. The model generates valid molecular structures in SMILES notation while flexibly incorporating three numerical and/or one token sequence into the generative process, just as requested. The generated compounds are very satisfactory in all scenarios tested. In detail, we showcase the model's capability to utilize token sequences for conditioning, either individually or in combination with numerical properties, making LLamol a potent tool for de novo molecule design, easily expandable with new properties.

翻译：生成模型在自然语言处理领域展现出巨大潜力，并已应用于分子设计中，例如通用预训练Transformer（GPT）模型。为开发探索有机化学空间以寻找潜在电活性化合物的工具，我们提出"LLamol"，这是一种基于LLama 2架构的新型生成式Transformer模型，训练数据来自多个公开来源的1300万有机化合物超集。为实现最大使用灵活性并应对数据不完整性问题，我们引入"随机上下文学习"作为新的训练流程。实验表明，该模型能够灵活处理最多包含四个条件（且支持更多条件）的单条件和多条件有机分子生成任务。模型可生成符合SMILES表示法的有效分子结构，同时能按需灵活地将三个数值条件和/或一个令牌序列纳入生成过程。在测试的所有场景中，生成的化合物均表现优异。具体而言，我们展示了模型利用令牌序列进行条件化生成的能力——无论是单独使用还是与数值属性组合使用，使LLamol成为易于扩展新属性的从头分子设计高效工具。