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.

我们提出了一种基于 LLama 2 架构的单一新颖生成模型 LLamol，它在来自多样公共来源的 1300 万有机化合物的超集上进行了训练，并引入了一种名为 “Stochastic Context Learning” 的新的训练过程。该模型可以灵活地将三个数字和 / 或一个令牌序列加入到生成过程中，展示了在单一和多条件有机分子生成方面的熟练处理能力，使得 LLamol 成为一个易于扩展新属性的全新分子设计工具。