Deep generative models that produce novel molecular structures have the
potential to facilitate chemical discovery. Diffusion models currently achieve
state of the art performance for 3D molecule generation. In this work, we
explore the use of flow matching, a recently proposed generative modeling
framework that generalizes diffusion models, for the task of de novo molecule
generation. Flow matching provides flexibility in model design; however, the
framework is predicated on the assumption of continuously-valued data. 3D de
novo molecule generation requires jointly sampling continuous and categorical
variables such as atom position and atom type. We extend the flow matching
framework to categorical data by constructing flows that are constrained to
exist on a continuous representation of categorical data known as the
probability simplex. We call this extension SimplexFlow. We explore the use of
SimplexFlow for de novo molecule generation. However, we find that, in
practice, a simpler approach that makes no accommodations for the categorical
nature of the data yields equivalent or superior performance. As a result of
these experiments, we present FlowMol, a flow matching model for 3D de novo
generative model that achieves improved performance over prior flow matching
methods, and we raise important questions about the design of prior
distributions for achieving strong performance in flow matching models. Code
and trained models for reproducing this work are available at
this https URL

在这项研究中，我们探索了用于生成化学分子的流匹配框架的扩展，该框架在 3D 分子生成方面取得了良好性能，并通过构建在概率简单形式上存在的流来对分类数据进行扩展，从而实现了改进的性能。

三维全新分子生成的混合连续和分类流匹配

Mixed Continuous and Categorical Flow Matching for 3D De Novo Molecule  Generation

The prediction of chemical reactions has gained significant interest within
the machine learning community in recent years, owing to its complexity and
crucial applications in chemistry. However, model evaluation for this task has
been mostly limited to simple metrics like top-k accuracy, which obfuscates
fine details of a model's limitations. Inspired by progress in other fields, we
propose a new assessment scheme that builds on top of current approaches,
steering towards a more holistic evaluation. We introduce the following key
components for this goal: CHORISO, a curated dataset along with multiple
tailored splits to recreate chemically relevant scenarios, and a collection of
metrics that provide a holistic view of a model's advantages and limitations.
Application of this method to state-of-the-art models reveals important
differences on sensitive fronts, especially stereoselectivity and chemical
out-of-distribution generalization. Our work paves the way towards robust
prediction models that can ultimately accelerate chemical discovery.

化学反应预测是近年来机器学习领域引起了重大关注的问题，由于其复杂性及在化学领域中的关键应用。然而，此任务的模型评估往往仅局限于像 top-k 准确度这样的简单度量标准，忽略了模型局限性的细微细节。我们提出了一种新的评估方案，基于现有方法，并朝着更全面的评估目标进行改进。我们引入了一些关键组件，包括 CHORISO，一份经过策划的数据集以及多个定制拆分，以再现化学相关的情景，以及一系列度量标准，提供模型优势和局限性的全面视图。利用这种方法应用于当下最先进的模型，揭示了特异性和化学领域外普适性等敏感方面的重要差异。我们的工作为最终能够加速化学发现的强大预测模型铺平了道路。

全面化学评估揭示反应预测模型中的陷阱

Holistic chemical evaluation reveals pitfalls in reaction prediction  models

This paper provides a simulated laboratory for making use of Reinforcement
Learning (RL) for chemical discovery. Since RL is fairly data intensive,
training agents `on-the-fly' by taking actions in the real world is infeasible
and possibly dangerous. Moreover, chemical processing and discovery involves
challenges which are not commonly found in RL benchmarks and therefore offer a
rich space to work in. We introduce a set of highly customizable and
open-source RL environments, ChemGymRL, based on the standard Open AI Gym
template. ChemGymRL supports a series of interconnected virtual chemical
benches where RL agents can operate and train. The paper introduces and details
each of these benches using well-known chemical reactions as illustrative
examples, and trains a set of standard RL algorithms in each of these benches.
Finally, discussion and comparison of the performances of several standard RL
methods are provided in addition to a list of directions for future work as a
vision for the further development and usage of ChemGymRL.

该研究提供了一个基于 Open AI Gym 模板的高度可定制和开源的 RL 环境，ChemGymRL，以支持在化学发现中训练 RL 代理，通过使用一系列互连的虚拟化学试验台。研究介绍了这些试验台，并在其中训练了一组标准 RL 算法，最后提供了多个标准 RL 方法的性能讨论和比较以及未来工作的发展方向列表作为 ChemGymRL 的远景。