Liu Dayan, Song Tao, Na Kang, Wang Shudong
College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, China.
The Ninth Department of Health Care Administration, The Second Medical Center, Chinese PLA General Hospital, Beijing, China.
Front Artif Intell. 2024 Apr 16;7:1374148. doi: 10.3389/frai.2024.1374148. eCollection 2024.
Alzheimer's disease (AD) is a gradually advancing neurodegenerative disorder characterized by a concealed onset. Acetylcholinesterase (AChE) is an efficient hydrolase that catalyzes the hydrolysis of acetylcholine (ACh), which regulates the concentration of ACh at synapses and then terminates ACh-mediated neurotransmission. There are inhibitors to inhibit the activity of AChE currently, but its side effects are inevitable. In various application fields where Al have gained prominence, neural network-based models for molecular design have recently emerged and demonstrate encouraging outcomes. However, in the conditional molecular generation task, most of the current generation models need additional optimization algorithms to generate molecules with intended properties which make molecular generation inefficient. Consequently, we introduce a cognitive-conditional molecular design model, termed PED, which leverages the variational auto-encoder. Its primary function is to adeptly produce a molecular library tailored for specific properties. From this library, we can then identify molecules that inhibit AChE activity without adverse effects. These molecules serve as lead compounds, hastening AD treatment and concurrently enhancing the AI's cognitive abilities. In this study, we aim to fine-tune a VAE model pre-trained on the ZINC database using active compounds of AChE collected from Binding DB. Different from other molecular generation models, the PED can simultaneously perform both property prediction and molecule generation, consequently, it can generate molecules with intended properties without additional optimization process. Experiments of evaluation show that proposed model performs better than other methods benchmarked on the same data sets. The results indicated that the model learns a good representation of potential chemical space, it can well generate molecules with intended properties. Extensive experiments on benchmark datasets confirmed PED's efficiency and efficacy. Furthermore, we also verified the binding ability of molecules to AChE through molecular docking. The results showed that our molecular generation system for AD shows excellent cognitive capacities, the molecules within the molecular library could bind well to AChE and inhibit its activity, thus preventing the hydrolysis of ACh.
阿尔茨海默病（AD）是一种隐匿起病、逐渐进展的神经退行性疾病。乙酰胆碱酯酶（AChE）是一种高效水解酶，催化乙酰胆碱（ACh）水解，调节突触处ACh浓度，进而终止ACh介导的神经传递。目前有抑制AChE活性的抑制剂，但其副作用不可避免。在人工智能已崭露头角的各个应用领域，基于神经网络的分子设计模型最近出现并展现出令人鼓舞的成果。然而，在条件分子生成任务中，当前大多数生成模型需要额外的优化算法来生成具有预期性质的分子，这使得分子生成效率低下。因此，我们引入了一种认知条件分子设计模型，称为PED，它利用变分自编码器。其主要功能是巧妙地生成针对特定性质定制的分子库。然后，我们可以从这个库中识别出无不良影响地抑制AChE活性的分子。这些分子作为先导化合物，加速AD治疗，同时增强人工智能的认知能力。在本研究中，我们旨在使用从Binding DB收集的AChE活性化合物对在ZINC数据库上预训练的VAE模型进行微调。与其他分子生成模型不同，PED可以同时进行性质预测和分子生成，因此，它可以在无需额外优化过程的情况下生成具有预期性质的分子。评估实验表明，所提出的模型在相同数据集上的表现优于其他方法。结果表明，该模型学习到了潜在化学空间的良好表示，能够很好地生成具有预期性质的分子。在基准数据集上的大量实验证实了PED的效率和有效性。此外，我们还通过分子对接验证了分子与AChE的结合能力。结果表明，我们的AD分子生成系统具有出色的认知能力，分子库中的分子能够很好地与AChE结合并抑制其活性，从而阻止ACh的水解。
