Generative models hold great promise for small molecule discovery,
significantly increasing the size of search space compared to traditional in
silico screening libraries. However, most existing machine learning methods for
small molecule generation suffer from poor synthesizability of candidate
compounds, making experimental validation difficult. In this paper we propose
Reaction-GFlowNet (RGFN), an extension of the GFlowNet framework that operates
directly in the space of chemical reactions, thereby allowing out-of-the-box
synthesizability while maintaining comparable quality of generated candidates.
We demonstrate that with the proposed set of reactions and building blocks, it
is possible to obtain a search space of molecules orders of magnitude larger
than existing screening libraries coupled with low cost of synthesis. We also
show that the approach scales to very large fragment libraries, further
increasing the number of potential molecules. We demonstrate the effectiveness
of the proposed approach across a range of oracle models, including pretrained
proxy models and GPU-accelerated docking.

通过在化学反应空间中操作，将生成模型扩展到小分子发现中，从而增加了搜索空间的大小，并保持候选分子生成的可合成性，同时对于预训练代理模型和 GPU 加速对接等多个 Oracle 模型都具有良好的表现。

可合成化分子生成的 GFlowNets

RGFN: Synthesizable Molecular Generation Using GFlowNets

Retrosynthesis, which aims to find a route to synthesize a target molecule
from commercially available starting materials, is a critical task in drug
discovery and materials design. Recently, the combination of ML-based
single-step reaction predictors with multi-step planners has led to promising
results. However, the single-step predictors are mostly trained offline to
optimize the single-step accuracy, without considering complete routes. Here,
we leverage reinforcement learning (RL) to improve the single-step predictor,
by using a tree-shaped MDP to optimize complete routes while retaining
single-step accuracy. Desirable routes should be both synthesizable and of low
cost. We propose an online training algorithm, called Planning with Dual Value
Networks (PDVN), in which two value networks predict the synthesizability and
cost of molecules, respectively. To maintain the single-step accuracy, we
design a two-branch network structure for the single-step predictor. On the
widely-used USPTO dataset, our PDVN algorithm improves the search success rate
of existing multi-step planners (e.g., increasing the success rate from 85.79%
to 98.95% for Retro*, and reducing the number of model calls by half while
solving 99.47% molecules for RetroGraph). Furthermore, PDVN finds shorter
synthesis routes (e.g., reducing the average route length from 5.76 to 4.83 for
Retro*, and from 5.63 to 4.78 for RetroGraph).

利用强化学习提高基于机器学习的单步反应预测算法的搜索成功率和寻找更短的化合物合成路线。

双值网络的逆合成规划

Retrosynthetic Planning with Dual Value Networks

The discovery of functional molecules is an expensive and time-consuming
process, exemplified by the rising costs of small molecule therapeutic
discovery. One class of techniques of growing interest for early-stage drug
discovery is de novo molecular generation and optimization, catalyzed by the
development of new deep learning approaches. These techniques can suggest novel
molecular structures intended to maximize a multi-objective function, e.g.,
suitability as a therapeutic against a particular target, without relying on
brute-force exploration of a chemical space. However, the utility of these
approaches is stymied by ignorance of synthesizability. To highlight the
severity of this issue, we use a data-driven computer-aided synthesis planning
program to quantify how often molecules proposed by state-of-the-art generative
models cannot be readily synthesized. Our analysis demonstrates that there are
several tasks for which these models generate unrealistic molecular structures
despite performing well on popular quantitative benchmarks. Synthetic
complexity heuristics can successfully bias generation toward
synthetically-tractable chemical space, although doing so necessarily detracts
from the primary objective. This analysis suggests that to improve the utility
of these models in real discovery workflows, new algorithm development is
warranted.

新型深度学习算法在提高新药发现效率方面表现出色，但其缺乏可合成性的信息，导致其合成难度大幅上升，需要新算法的开发来加以解决。