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

This paper proposes a novel variant of GFlowNet, genetic-guided GFlowNet
(Genetic GFN), which integrates an iterative genetic search into GFlowNet.
Genetic search effectively guides the GFlowNet to high-rewarded regions,
addressing global over-exploration that results in training inefficiency and
exploring limited regions. In addition, training strategies, such as rank-based
replay training and unsupervised maximum likelihood pre-training, are further
introduced to improve the sample efficiency of Genetic GFN. The proposed method
shows a state-of-the-art score of 16.213, significantly outperforming the
reported best score in the benchmark of 15.185, in practical molecular
optimization (PMO), which is an official benchmark for sample-efficient
molecular optimization. Remarkably, ours exceeds all baselines, including
reinforcement learning, Bayesian optimization, generative models, GFlowNets,
and genetic algorithms, in 14 out of 23 tasks.

本篇论文提出了一种新的 GFlowNet 变体，遗传引导 GFlowNet (Genetic GFN)，通过将迭代遗传搜索集成到 GFlowNet 中，有效地指导 GFlowNet 到高回报区域，解决全局过度探索导致训练效率低下和探索有限区域的问题。此方法在实际分子优化 (PMO) 的基准测试中达到了 16.213 的最高分数，明显优于报道的最佳分数 15.185，超过了 23 个任务中的所有基准线，包括强化学习、贝叶斯优化、生成模型、GFlowNets 和遗传算法。

基因引导的 GFlowNets：提升实用分子优化基准

Genetic-guided GFlowNets: Advancing in Practical Molecular Optimization  Benchmark

Sampling diverse, thermodynamically feasible molecular conformations plays a
crucial role in predicting properties of a molecule. In this paper we propose
to use GFlowNet for sampling conformations of small molecules from the
Boltzmann distribution, as determined by the molecule's energy. The proposed
approach can be used in combination with energy estimation methods of different
fidelity and discovers a diverse set of low-energy conformations for highly
flexible drug-like molecules. We demonstrate that GFlowNet can reproduce
molecular potential energy surfaces by sampling proportionally to the Boltzmann
distribution.

使用 GFlowNet 从玻尔兹曼分布中对小分子进行构象采样的方法，可以与不同准确性的能量估计方法结合使用，并为高度柔性的药物样品发现一组多样性的低能量构象。该方法通过比例抽样来复现分子的势能表面。

基于 GFlowNets 实现分子构型生成的平衡状态追求

Towards equilibrium molecular conformation generation with GFlowNets

Finding the best way to schedule operations in a computation graph is a
classical NP-hard problem which is central to compiler optimization. However,
evaluating the goodness of a schedule on the target hardware can be very
time-consuming. Traditional approaches as well as previous machine learning
ones typically optimize proxy metrics, which are fast to evaluate but can lead
to bad schedules when tested on the target hardware. In this work, we propose a
new approach to scheduling by sampling proportionally to the proxy metric using
a novel GFlowNet method. We introduce a technique to control the trade-off
between diversity and goodness of the proposed schedules at inference time and
demonstrate empirically that the pure optimization baselines can lead to subpar
performance with respect to our approach when tested on a target model.
Furthermore, we show that conditioning the GFlowNet on the computation graph
enables generalization to unseen scheduling problems for both synthetic and
real-world compiler datasets.

本研究提出了一种新的调度方法，使用 GFlowNet 方法按比例抽样代理指标，通过在推理时间控制所提出的时间表的差异性和优良性之间的权衡以及将 GFlowNet 条件化为计算图，证明了相对于我们的方法，纯优化基线在目标模型上的性能不佳。