PETrans: De Novo Drug Design with Protein-Specific Encoding Based on Transfer Learning.
PETrans: De Novo Drug Design with Protein-Specific Encoding Based on Transfer Learning.
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DOI：
10.3390/ijms24021146
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发表时间：
2023-01-06
影响因子：
5.6
通讯作者：
Zheng, Pan
中科院分区：
文献类型：
--
作者：
Wang, Xun;Gao, Changnan;Han, Peifu;Li, Xue;Chen, Wenqi;Rodriguez Paton, Alfonso;Wang, Shuang;Zheng, Pan
近年来，通过深度生成模型设计新型药物分子取得了巨大成功。然而，现有的方法只能产生药物样分子，这需要额外的结构优化才能开发成实际的药物。在这项研究中，提出了一种用于生成目标特异性配体的深度学习方法。当靶特异性配体的数据集有限时，该方法是有用的。深度学习方法可以以数据驱动的方式提取和学习特征（表示），很少或没有人类参与。生成预训练（GPT）用于提取分子的上下文特征。采用三种不同的蛋白质编码方法提取目的蛋白的理化性质和氨基酸信息。结合蛋白质编码和分子序列信息来指导分子生成。迁移学习用于微调预训练模型，以生成与靶蛋白具有更好结合能力的分子。使用三个不同的目标验证模型。对接结果表明，我们的模型是能够产生新的分子具有更高的对接得分的目标蛋白质。
Recent years have seen tremendous success in the design of novel drug molecules through deep generative models. Nevertheless, existing methods only generate drug-like molecules, which require additional structural optimization to be developed into actual drugs. In this study, a deep learning method for generating target-specific ligands was proposed. This method is useful when the dataset for target-specific ligands is limited. Deep learning methods can extract and learn features (representations) in a data-driven way with little or no human participation. Generative pretraining (GPT) was used to extract the contextual features of the molecule. Three different protein-encoding methods were used to extract the physicochemical properties and amino acid information of the target protein. Protein-encoding and molecular sequence information are combined to guide molecule generation. Transfer learning was used to fine-tune the pretrained model to generate molecules with better binding ability to the target protein. The model was validated using three different targets. The docking results show that our model is capable of generating new molecules with higher docking scores for the target proteins.
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影响因子：
4.6
作者：
Grechishnikova D
通讯作者：
Grechishnikova D
影响因子：
18.2
作者：
Gómez-Bombarelli R;Wei JN;Duvenaud D;Hernández-Lobato JM;Sánchez-Lengeling B;Sheberla D;Aguilera-Iparraguirre J;Hirzel TD;Adams RP;Aspuru-Guzik A
通讯作者：
Aspuru-Guzik A
影响因子：
5.5
作者：
Caulfield, Thomas;Coban, Matt;Flores, Samuel Coulbourn
通讯作者：
Flores, Samuel Coulbourn
影响因子：
5.1
作者：
Gumus, Mehmet;Babacan, Semsi N.;Gulcin, Ilhami
通讯作者：
Gulcin, Ilhami
影响因子：
4.6
作者：
Hassan NM;Alhossary AA;Mu Y;Kwoh CK
通讯作者：
Kwoh CK