SMiCRM: A Benchmark Dataset of Mechanistic Molecular Images

Optical chemical structure recognition (OCSR) systems aim to extract the molecular structure information, usually in the form of molecular graph or SMILES, from images of chemical molecules. While many tools have been developed for this purpose, challenges still exist due to different types of noises that might exist in the images. Specifically, we focus on the 'arrow-pushing' diagrams, a typical type of chemical images to demonstrate electron flow in mechanistic steps. We present Structural molecular identifier of Molecular images in Chemical Reaction Mechanisms (SMiCRM), a dataset designed to benchmark machine recognition capabilities of chemical molecules with arrow-pushing annotations. Comprising 453 images, it spans a broad array of organic chemical reactions, each illustrated with molecular structures and mechanistic arrows. SMiCRM offers a rich collection of annotated molecule images for enhancing the benchmarking process for OCSR methods. This dataset includes a machine-readable molecular identity for each image as well as mechanistic arrows showing electron flow during chemical reactions. It presents a more authentic and challenging task for testing molecular recognition technologies, and achieving this task can greatly enrich the mechanisitic information in computer-extracted chemical reaction data.

MolNexTR: A Generalized Deep Learning Model for Molecular Image Recognition

In the field of chemical structure recognition, the task of converting molecular images into graph structures and SMILES string stands as a significant challenge, primarily due to the varied drawing styles and conventions prevalent in chemical literature. To bridge this gap, we proposed MolNexTR, a novel image-to-graph deep learning model that collaborates to fuse the strengths of ConvNext, a powerful Convolutional Neural Network variant, and Vision-TRansformer. This integration facilitates a more nuanced extraction of both local and global features from molecular images. MolNexTR can predict atoms and bonds simultaneously and understand their layout rules. It also excels at flexibly integrating symbolic chemistry principles to discern chirality and decipher abbreviated structures. We further incorporate a series of advanced algorithms, including improved data augmentation module, image contamination module, and a post-processing module to get the final SMILES output. These modules synergistically enhance the model's robustness against the diverse styles of molecular imagery found in real literature. In our test sets, MolNexTR has demonstrated superior performance, achieving an accuracy rate of 81-97%, marking a significant advancement in the domain of molecular structure recognition. Scientific contribution: MolNexTR is a novel image-to-graph model that incorporates a unique dual-stream encoder to extract complex molecular image features, and combines chemical rules to predict atoms and bonds while understanding atom and bond layout rules. In addition, it employs a series of novel augmentation algorithms to significantly enhance the robustness and performance of the model.

RetroOOD: Understanding Out-of-Distribution Generalization in Retrosynthesis Prediction

Machine learning-assisted retrosynthesis prediction models have been gaining widespread adoption, though their performances oftentimes degrade significantly when deployed in real-world applications embracing out-of-distribution (OOD) molecules or reactions. Despite steady progress on standard benchmarks, our understanding of existing retrosynthesis prediction models under the premise of distribution shifts remains stagnant. To this end, we first formally sort out two types of distribution shifts in retrosynthesis prediction and construct two groups of benchmark datasets. Next, through comprehensive experiments, we systematically compare state-of-the-art retrosynthesis prediction models on the two groups of benchmarks, revealing the limitations of previous in-distribution evaluation and re-examining the advantages of each model. More remarkably, we are motivated by the above empirical insights to propose two model-agnostic techniques that can improve the OOD generalization of arbitrary off-the-shelf retrosynthesis prediction algorithms. Our preliminary experiments show their high potential with an average performance improvement of 4.6%, and the established benchmarks serve as a foothold for further retrosynthesis prediction research towards OOD generalization.