Predicting the yield percentage of a chemical reaction is useful in many
aspects such as reducing wet-lab experimentation by giving the priority to the
reactions with a high predicted yield. In this work we investigated the use of
multiple type inputs to predict chemical reaction yield. We used simplified
molecular-input line-entry system (SMILES) as well as calculated chemical
descriptors as model inputs. The model consists of a pre-trained bidirectional
transformer-based encoder (BERT) and a multi-layer perceptron (MLP) with a
regression head to predict the yield. We experimented on two high throughput
experimentation (HTE) datasets for Buchwald-Hartwig and Suzuki-Miyaura
reactions. The experiments show improvements in the prediction on both datasets
compared to systems using only SMILES or chemical descriptors as input. We also
tested the model's performance on out-of-sample dataset splits of
Buchwald-Hartwig and achieved comparable results with the state-of-the-art. In
addition to predicting the yield, we demonstrated the model's ability to
suggest the optimum (highest yield) reaction conditions. The model was able to
suggest conditions that achieves 94% of the optimum reported yields. This
proves the model to be useful in achieving the best results in the wet lab
without expensive experimentation.

该研究使用 SMILES 和化学描述符的多类型输入预测化学反应产量，模型由预训练的 BERT 编码器和 MLP 组成，可提高产量预测精度并在 Buchwald-Hartwig 和 Suzuki-Miyaura 反应中测试，同时可以推荐最佳反应条件，从而在湿实验中实现最佳结果。

基于多模态 Transformer 模型的 Buchwald-Hartwig 反应和 Suzuki-Miyaura 反应产率预测

Multimodal Transformer-based Model for Buchwald-Hartwig and Suzuki-Miyaura Reaction Yield Prediction

Many texts, especially in chemistry and biology, describe complex processes.
We focus on texts that describe a chemical reaction process and questions that
ask about the process's outcome under different environmental conditions. To
answer questions about such processes, one needs to understand the interactions
between the different entities involved in the process and to simulate their
state transitions during the process execution under different conditions. A
state transition is defined as the memory modification the program does to the
variables during the execution. We hypothesize that generating code and
executing it to simulate the process will allow answering such questions. We,
therefore, define a domain-specific language (DSL) to represent processes. We
contribute to the community a unique dataset curated by chemists and annotated
by computer scientists. The dataset is composed of process texts, simulation
questions, and their corresponding computer codes represented by the DSL.We
propose a neural program synthesis approach based on reinforcement learning
with a novel state-transition semantic reward. The novel reward is based on the
run-time semantic similarity between the predicted code and the reference code.
This allows simulating complex process transitions and thus answering
simulation questions. Our approach yields a significant boost in accuracy for
simulation questions: 88\% accuracy as opposed to 83\% accuracy of the
state-of-the-art neural program synthesis approaches and 54\% accuracy of
state-of-the-art end-to-end text-based approaches.

本研究提出了一种基于 DSL 和强化学习的神经程序合成方法，通过运行时语义相似性奖励实现了化学反应过程的模拟，从而获得了 88％的准确性提升。

生成用于回答模拟问题的代码的假设

What If: Generating Code to Answer Simulation Questions

Retrosynthesis is one of the fundamental problems in organic chemistry. The
task is to identify reactants that can be used to synthesize a specified
product molecule. Recently, computer-aided retrosynthesis is finding renewed
interest from both chemistry and computer science communities. Most existing
approaches rely on template-based models that define subgraph matching rules,
but whether or not a chemical reaction can proceed is not defined by hard
decision rules. In this work, we propose a new approach to this task using the
Conditional Graph Logic Network, a conditional graphical model built upon graph
neural networks that learns when rules from reaction templates should be
applied, implicitly considering whether the resulting reaction would be both
chemically feasible and strategic. We also propose an efficient hierarchical
sampling to alleviate the computation cost. While achieving a significant
improvement of $8.1\%$ over current state-of-the-art methods on the benchmark
dataset, our model also offers interpretations for the prediction.

本文提出了一种新的基于条件图逻辑网络的化学反应逆向设计方法，使用图神经网络隐式考虑反应是否在化学上可行和策略性，并提出了一种有效的分层采样，成功将当前现有方法的性能提高了 8.1%。

基于条件图逻辑网络的反合成预测

Retrosynthesis Prediction with Conditional Graph Logic Network

We address a fundamental problem in chemistry known as chemical reaction
product prediction. Our main insight is that the input reactant and reagent
molecules can be jointly represented as a graph, and the process of generating
product molecules from reactant molecules can be formulated as a sequence of
graph transformations. To this end, we propose Graph Transformation Policy
Network (GTPN) -- a novel generic method that combines the strengths of graph
neural networks and reinforcement learning to learn the reactions directly from
data with minimal chemical knowledge. Compared to previous methods, GTPN has
some appealing properties such as: end-to-end learning, and making no
assumption about the length or the order of graph transformations. In order to
guide model search through the complex discrete space of sets of bond changes
effectively, we extend the standard policy gradient loss by adding useful
constraints. Evaluation results show that GTPN improves the top-1 accuracy over
the current state-of-the-art method by about 3% on the large USPTO dataset. Our
model's performances and prediction errors are also analyzed carefully in the
paper.

通过提出一种基于图转换的模型（GTPN）， 结合了图神经网络和强化学习方法，以最小的化学知识从数据中直接学习反应， 常规策略梯度损失不适用于指导模型在化学成键变换的集合中进行搜索，因此作者提出将一些有用的约束添加到标准策略梯度损失函数中来指导模型的搜索， 评估结果表明，在美国专利和商标局数据集上，与当前最先进的方法相比，GTPN 的表现有所改进。