Publication

RXNMapperv2: updated validation and evaluation on expanded chemical space

2026

Abstract

Atom-to-atom mapping (AAM) assigns reactant atoms to product atoms and is fundamental for reaction classification, template extraction, and synthesis planning. RXNMapper derives AAM from the attention of a Transformer model trained by masked language modeling on unmapped reaction SMILES. However, its original model selection relied on USPTO-50K, a validation set later shown to contain erroneous mappings. Here we revisit RXNMapper using a curated validation set of 4’533 reactions from the dataset Pistachio. We retrained ALBERT-based RXNMapper models on USPTOfull and Pistachio and selected the best head, layer, and checkpoint on this new validation set. To improve the comparison of AAM algorithms, we developed an evaluation framework that removes ambiguous reference reactions and compares predictions by reaction graph isomorphism rather than direct string matching, avoiding false negatives from inconsistent formats and symmetry-equivalent mappings. Across the golden dataset, Pistachio, and an internal AstraZeneca medicinal chemistry benchmark, the updated models outperform the original RXNMapper and LocalMapper, while rearrangements remain persistent challenges.

Official source

https://infoscience.epfl.ch/entities/publication/e003cad2-1fd0-46d7-912f-c4e28d69270e

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

2026

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/e003cad2-1fd0-46d7-912f-c4e28d69270e

About this result

Related concepts (32)

Transformer (machine learning model)

A transformer is a deep learning architecture that relies on the parallel multi-head attention mechanism. The modern transformer was proposed in the 2017 paper titled 'Attention Is All You Need' by Ashish Vaswani et al., Google Brain team. It is notable for requiring less training time than previous recurrent neural architectures, such as long short-term memory (LSTM), and its later variation has been prevalently adopted for training large language models on large (language) datasets, such as the Wikipedia corpus and Common Crawl, by virtue of the parallelized processing of input sequence.

Diffusion model

In machine learning, diffusion models, also known as diffusion probabilistic models or score-based generative models, are a class of latent variable models. They are Markov chains trained using variational inference. The goal of diffusion models is to learn the latent structure of a dataset by modeling the way in which data points diffuse through the latent space. In computer vision, this means that a neural network is trained to denoise images blurred with Gaussian noise by learning to reverse the diffusion process.

Large language model

A large language model (LLM) is a language model characterized by its large size. Their size is enabled by AI accelerators, which are able to process vast amounts of text data, mostly scraped from the Internet. The artificial neural networks which are built can contain from tens of millions and up to billions of weights and are (pre-)trained using self-supervised learning and semi-supervised learning. Transformer architecture contributed to faster training.

Related publications (18)

RXNMapperv2: updated validation and evaluation on expanded chemical space

Chat with Graph Search

LayerSync: Self-aligning Intermediate Layers

From recursion to incrementality: return to recurrent neural networks

Integrating the expected future in load forecasts with contextually enhanced transformer models