深度gan:基于生成对抗网络的药物发现分子生成

Mengdi Xu, Jiandong Cheng, Yirong Liu, Wei Huang
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引用次数: 2

摘要

药物发现作为医药行业最核心的环节之一,是人工智能技术应用的重要方向。加速发现过程仍然是一个巨大的挑战。为了解决这个问题,我们开发了一个基于生成对抗网络算法的新生小分子生成模型,称为DeepGAN。值得一提的是,我们将DeepSMILES作为训练对象,避免了SMILES的局限性。强化学习的加入避免了鉴别器不可微的问题。该模型通过策略梯度来优化特定区域的回报和对抗损失。通过这种方式,DeepGAN与ORGAN及其衍生物OR(W)GAN和Naive RL相比具有优势,后者已经经过了充分的测试。实验表明,我们的模型可以创建出保持分子多样性、提高有效性和改善预期指标的分子。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DeepGAN: Generating Molecule for Drug Discovery Based on Generative Adversarial Network
As one of the most core links in the pharmaceutical industry, drug discovery is an important direction for the application of artificial intelligence technology. It is still a huge challenge to accelerate the discovery process. To address it, we have developed a generative model for de novo small-molecule based on Generative Adversarial Network algorithm called DeepGAN. It is worth mentioning that we make DeepSMILES as training object, which has avoided the limitations of SMILES. And the addition of reinforcement learning keeps away from non-differentiable problem of the discriminator. The model is trained to optimize the rewards and adversarial loss in specific areas through strategy gradient. In this way, DeepGAN compares favorably to ORGAN and its derivatives OR(W)GAN and Naive RL which have been already well-tested. The experiments indicate our model can create molecules which can maintain molecular diversity, increase validity and show improvement in the desired metrics.
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