Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan.
Graduate School of Informatics, Nagoya University, Chikusa, Nagoya, Aichi 464-8601, Japan.
J Chem Inf Model. 2024 Apr 8;64(7):2345-2355. doi: 10.1021/acs.jcim.3c00824. Epub 2023 Sep 28.
Deep generative models for molecular generation have been gaining much attention as structure generators to accelerate drug discovery. However, most previously developed methods are chemistry-centric approaches, and comprehensive biological responses in the cell have not been taken into account. In this study, we propose a novel computational method, TRIOMPHE-BOA (transcriptome-based inference and generation of molecules with desired phenotypes using the Bayesian optimization algorithm), to generate new chemical structures of inhibitor or activator candidates for therapeutic target proteins by integrating chemically and genetically perturbed transcriptome profiles. In the algorithm, the substructures of multiple molecules that were selected based on the transcriptome analysis are fused in the design of new chemical structures by exploring the latent space of a Transformer-based variational autoencoder using Bayesian optimization. Our results demonstrate the usefulness of the proposed method in terms of having high reproducibility of existing ligands for 10 therapeutic target proteins when compared with previous methods. Moreover, this method can be applied to proteins without detailed 3D structures or known ligands and is expected to become a powerful tool for more efficient hit identification.
用于分子生成的深度生成模型作为结构生成器在加速药物发现方面受到了广泛关注。然而，大多数以前开发的方法都是以化学为中心的方法，并没有考虑细胞中的全面生物学反应。在这项研究中，我们提出了一种新的计算方法，TRIOMPHE-BOA（基于转录组的推断和使用贝叶斯优化算法生成具有所需表型的分子），通过整合化学和遗传扰动转录组谱，为治疗靶蛋白生成抑制剂或激活剂候选物的新化学结构。在该算法中，基于转录组分析选择的多个分子的子结构通过使用贝叶斯优化探索基于 Transformer 的变分自动编码器的潜在空间来融合在新化学结构的设计中。我们的结果表明，与以前的方法相比，该方法在 10 种治疗靶蛋白的现有配体的高重现性方面具有实用性。此外，该方法可应用于没有详细 3D 结构或已知配体的蛋白质，有望成为更有效命中识别的有力工具。
