Goethe University Frankfurt, Institute of Pharmaceutical Chemistry, 60438, Frankfurt, Germany.
Ludwig-Maximilians-Universität München, Department of Pharmacy, 81377, Munich, Germany.
Nat Commun. 2024 Sep 11;15(1):7946. doi: 10.1038/s41467-024-52060-8.
Generative deep learning models enable data-driven de novo design of molecules with tailored features. Chemical language models (CLM) trained on string representations of molecules such as SMILES have been successfully employed to design new chemical entities with experimentally confirmed activity on intended targets. Here, we probe the application of CLM to generate multi-target ligands for designed polypharmacology. We capitalize on the ability of CLM to learn from small fine-tuning sets of molecules and successfully bias the model towards designing drug-like molecules with similarity to known ligands of target pairs of interest. Designs obtained from CLM after pooled fine-tuning are predicted active on both proteins of interest and comprise pharmacophore elements of ligands for both targets in one molecule. Synthesis and testing of twelve computationally favored CLM designs for six target pairs reveals modulation of at least one intended protein by all selected designs with up to double-digit nanomolar potency and confirms seven compounds as designed dual ligands. These results corroborate CLM for multi-target de novo design as source of innovation in drug discovery.
生成式深度学习模型可实现具有特定特征的分子的基于数据的从头设计。基于分子的字符串表示（如 SMILES）进行训练的化学语言模型 (CLM) 已成功用于设计具有预期靶点实验证实活性的新型化学实体。在这里，我们探索了 CLM 在生成多靶标配体用于设计多效性方面的应用。我们利用 CLM 从小分子微调集学习的能力，并成功地将模型偏向于设计具有与目标对中已知配体相似性的类药性分子。从 CLM 进行汇集微调后获得的设计被预测对两个感兴趣的蛋白质都具有活性，并在一个分子中包含两个目标的配体的药效团元素。对六个靶对的十二种计算上有利的 CLM 设计进行的合成和测试表明，所有选定的设计都至少对一个预期的蛋白质具有调节作用，其效力高达两位数的纳摩尔，并且确认了七种化合物为设计的双配体。这些结果证实了 CLM 作为药物发现创新来源的多靶标从头设计。
