Central nervous system (CNS) drugs have had a significant impact on treating a wide range of neurodegenerative and psychiatric disorders. In recent years, deep learning-based generative models have shown great potential for accelerating drug discovery and improving efficacy. However, specific applications of these techniques in CNS drug discovery have not been widely reported. In this study, we developed the CNSMolGen model, which uses a framework of bidirectional recurrent neural networks (Bi-RNNs) for de novo molecular design of CNS drugs. Results showed that the pretrained model was able to generate more than 90% of completely new molecular structures, which possessed the properties of CNS drug molecules and were synthesizable. In addition, transfer learning was performed on small data sets with specific biological activities to evaluate the potential application of the model for CNS drug optimization. Here, we used drugs against the classical CNS disease target serotonin transporter (SERT) as a fine-tuned data set and generated a focused database against the target protein. The potential biological activities of the generated molecules were verified by using the physics-based induced-fit docking study. The success of this model demonstrates its potential in CNS drug design and optimization, which provides a new impetus for future CNS drug development.

中文翻译：

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CNSMolGen：用于从头中枢神经系统药物设计的双向循环神经网络生成模型


中枢神经系统（CNS）药物对治疗多种神经退行性疾病和精神疾病产生了重大影响。近年来，基于深度学习的生成模型在加速药物发现和提高疗效方面显示出巨大潜力。然而，这些技术在中枢神经系统药物发现中的具体应用尚未得到广泛报道。在这项研究中，我们开发了 CNSMolGen 模型，该模型使用双向循环神经网络 (Bi-RNN) 框架进行 CNS 药物的从头分子设计。结果表明，预训练模型能够生成90%以上的全新分子结构，这些结构具有中枢神经系统药物分子的特性，并且可以合成。此外，在具有特定生物活性的小数据集上进行迁移学习，以评估该模型在中枢神经系统药物优化中的潜在应用。在这里，我们使用针对经典中枢神经系统疾病目标血清素转运蛋白（SERT）的药物作为微调数据集，并生成针对目标蛋白的集中数据库。通过基于物理的诱导拟合对接研究验证了所生成分子的潜在生物活性。该模型的成功展示了其在中枢神经系统药物设计和优化方面的潜力，为未来中枢神经系统药物的开发提供了新的动力。