DNA-encoded libraries (DELs) are a powerful approach for rapidly screening
large numbers of diverse compounds. One of the key challenges in using DELs is
library design, which involves choosing the building blocks that will be
combinatorially combined to produce the final library. In this paper we
consider the task of protein-protein interaction (PPI) biased DEL design. To
this end, we evaluate several machine learning algorithms on the PPI modulation
task and use them as a reward for the proposed GFlowNet-based generative
approach. We additionally investigate the possibility of using structural
information about building blocks to design a hierarchical action space for the
GFlowNet. The observed results indicate that GFlowNets are a promising approach
for generating diverse combinatorial library candidates.

DNA-encoded libraries (DELs) are evaluated for protein-protein interaction (PPI) biased DEL design, using machine learning algorithms as rewards for the proposed GFlowNet-based generative approach, and investigating the use of structural information for hierarchical action space design. GFlowNets show promise for generating diverse combinatorial library candidates.

借助 GFlowNets 实现 DNA 编码库生成

Towards DNA-Encoded Library Generation with GFlowNets

DNA encoded libraries (DELs) are used for rapid large-scale screening of
small molecules against a protein target. These combinatorial libraries are
built through several cycles of chemistry and DNA ligation, producing large
sets of DNA-tagged molecules. Training machine learning models on DEL data has
been shown to be effective at predicting molecules of interest dissimilar from
those in the original DEL. Machine learning chemical property prediction
approaches rely on the assumption that the property of interest is linked to a
single chemical structure. In the context of DNA-encoded libraries, this is
equivalent to assuming that every chemical reaction fully yields the desired
product. However, in practice, multi-step chemical synthesis sometimes
generates partial molecules. Each unique DNA tag in a DEL therefore corresponds
to a set of possible molecules. Here, we leverage reaction yield data to
enumerate the set of possible molecules corresponding to a given DNA tag. This
paper demonstrates that training a custom GNN on this richer dataset improves
accuracy and generalization performance.

研究表明，机器学习化学属性预测方法依赖于一个假设，即感兴趣的属性与一个单独的化学结构相关联。本文中，我们应用反应收率数据枚举与给定 DNA 标签相对应的可能分子集，通过在这个更丰富的数据集上训练一个自定义的 GNN 模型，证明了它相比原来的模型提高了准确度和泛化性能。