Molecule Design by Latent Space Energy-Based Modeling and Gradual Distribution Shifting
Molecule Design by Latent Space Energy-Based Modeling and Gradual Distribution Shifting
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DOI：
10.48550/arxiv.2306.14902
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
Deqian Kong;Bo Pang;Tian Han;Y. Wu
中科院分区：
文献类型：
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作者：
Deqian Kong;Bo Pang;Tian Han;Y. Wu
产生具有所需的化学和生物学特性的分子，如高类药物、与靶蛋白的高结合亲和力，对于药物发现至关重要。在这篇文章中，我们提出了一个概率生成模型来捕捉分子及其性质的联合分布。我们的模型采用基于能量的潜在空间模型(EBM)。在潜在向量的条件下，分子及其性质分别用分子生成模型和性质回归模型来模拟。为了寻找具有期望属性的分子，我们提出了一种采样渐进分布漂移(SGDS)算法，在根据现有分子及其性质的训练数据对模型进行初始学习后，逐渐将模型分布向具有期望属性值的分子所支持的区域移动。我们的实验表明，我们的方法在各种分子设计任务中都取得了很好的性能。
Generation of molecules with desired chemical and biological properties such as high drug-likeness, high binding affinity to target proteins, is critical for drug discovery. In this paper, we propose a probabilistic generative model to capture the joint distribution of molecules and their properties. Our model assumes an energy-based model (EBM) in the latent space. Conditional on the latent vector, the molecule and its properties are modeled by a molecule generation model and a property regression model respectively. To search for molecules with desired properties, we propose a sampling with gradual distribution shifting (SGDS) algorithm, so that after learning the model initially on the training data of existing molecules and their properties, the proposed algorithm gradually shifts the model distribution towards the region supported by molecules with desired values of properties. Our experiments show that our method achieves very strong performances on various molecule design tasks.