Designing molecules with desirable properties, such as drug-likeliness and
high binding affinities towards protein targets, is a challenging problem. In
this paper, we propose the Dual-Space Optimization (DSO) method that integrates
latent space sampling and data space selection to solve this problem. DSO
iteratively updates a latent space generative model and a synthetic dataset in
an optimization process that gradually shifts the generative model and the
synthetic data towards regions of desired property values. Our generative model
takes the form of a Latent Prompt Transformer (LPT) where the latent vector
serves as the prompt of a causal transformer. Our extensive experiments
demonstrate effectiveness of the proposed method, which sets new performance
benchmarks across single-objective, multi-objective and constrained molecule
design tasks.

通过引入 Dual-Space Optimization (DSO) 方法，结合了潜空间采样和数据空间选择来解决分子设计中的难题，本文提出的生成模型采用潜在提示变换器（LPT）的形式，通过优化过程逐渐实现生成模型和合成数据朝着期望的属性值区域移动，并在单目标、多目标和受限分子设计任务中创造了新的性能基准。

双空间优化：潜在提示变换提升分子序列设计

Dual-Space Optimization: Improved Molecule Sequence Design by Latent  Prompt Transformer

Try to generate new bridge types using generative artificial intelligence
technology. Using symmetric structured image dataset of three-span beam bridge,
arch bridge, cable-stayed bridge and suspension bridge , based on Python
programming language, TensorFlow and Keras deep learning platform framework ,
PixelCNN is constructed and trained. The model can capture the statistical
structure of the images and calculate the probability distribution of the next
pixel when the previous pixels are given. From the obtained latent space
sampling, new bridge types different from the training dataset can be
generated. PixelCNN can organically combine different structural components on
the basis of human original bridge types, creating new bridge types that have a
certain degree of human original ability. Autoregressive models cannot
understand the meaning of the sequence, while multimodal models combine
regression and autoregressive models to understand the sequence. Multimodal
models should be the way to achieve artificial general intelligence in the
future.

尝试使用生成人工智能技术来生成新的桥梁类型。基于 Python 编程语言，TensorFlow 和 Keras 深度学习平台框架，利用对称结构化的图像数据集对三跨梁桥、拱桥、斜拉桥和悬索桥进行了 PixelCNN 的构建和训练。通过获得的潜在空间采样，可以生成与训练数据集中不同的桥梁类型。Multimodal 模型结合回归和自回归模型来理解序列，可能是实现人工通用智能的方式。