Biotherapeutic and Medicinal Sciences, Biogen, 225 Binney Street, Cambridge, MA 02142, USA.
College of Agriculture and Life Sciences, Cornell University, Ithaca, NY 14850, USA.
Molecules. 2023 May 30;28(11):4430. doi: 10.3390/molecules28114430.
Deep generative models applied to the generation of novel compounds in small-molecule drug design have attracted a lot of attention in recent years. To design compounds that interact with specific target proteins, we propose a Generative Pre-Trained Transformer (GPT)-inspired model for de novo target-specific molecular design. By implementing different keys and values for the multi-head attention conditional on a specified target, the proposed method can generate drug-like compounds both with and without a specific target. The results show that our approach (cMolGPT) is capable of generating SMILES strings that correspond to both drug-like and active compounds. Moreover, the compounds generated from the conditional model closely match the chemical space of real target-specific molecules and cover a significant portion of novel compounds. Thus, the proposed Conditional Generative Pre-Trained Transformer (cMolGPT) is a valuable tool for de novo molecule design and has the potential to accelerate the molecular optimization cycle time.
近年来，应用于小分子药物设计中新型化合物生成的深度生成模型受到了广泛关注。为了设计与特定靶标蛋白相互作用的化合物，我们提出了一种基于生成式预训练转换器（GPT）的新方法，用于从头开始针对特定靶标的分子设计。通过在指定的靶标上为多头注意力实现不同的键和值，该方法可以生成既有又没有特定靶标的类药物化合物。结果表明，我们的方法（cMolGPT）能够生成与类药物和活性化合物相对应的 SMILES 字符串。此外，从条件模型生成的化合物与真实靶标特异性分子的化学空间紧密匹配，并涵盖了很大一部分新型化合物。因此，所提出的条件生成式预训练转换器（cMolGPT）是一种有价值的从头分子设计工具，有可能加速分子优化周期时间。
