Machine Learning Approaches for Filtering Organometallic Reactions: A Comparative Study of Molecular Descriptors

Walter Bonke Mahlangu, Taurai Hungwe, Nomasonto Rapulenyane, Somandla Ncube

Organometallic chemistry deals with the synthesis, structure, reactivity, and applications of compounds containing metal–carbon covalent bonds. In recent years, there has been a growing interest in predicting the catalytic activity of organometallics using machine learning. However, the major drawback in developing algorithms that can be used in predicting organometallic reactions is the availability of organometallic reaction data and organometallic filtering tools. The main aim of the current study is to develop organometallic reaction-filtering tools that are crucial for building accurate and effective ML models in organometallic chemistry. Random Forest (RF), K-Nearest Neighbors (kNN), Support Vector Classifiers (SVC), and Multi-Layer Perceptrons (MLP) were employed, using feature subsets selected via Permutation Feature Importance from Morgan fingerprints and MACCS keys. The results demonstrate that the MACCS-based MLP architecture provides the most reliable filtering performance, achieving a superior F1 score of 0.85, a Recall of 0.85, and a high AUC-ROC of 0.837. Furthermore, the MACCS-MLP exhibited the highest predictive confidence, yielding the study’s lowest Log Loss of 0.312. In contrast, while Morgan fingerprints paired with kNN offered a specialized “strict” filter with absolute Precision (1.00), the sparse dimensionality of circular fingerprints generally resulted in lower calibration for probabilistic models. These findings underscore that dense, fragment-based descriptors refined by data-driven feature selection are most effective for identifying complex organometallic motifs. This study successfully provides a validated methodology for building precise filtering tools, establishing a critical foundation for automated catalyst discovery and the expansion of effective machine learning applications in organometallic chemistry. The study is limited to only identifying organometallic reactions and cannot filter based on organometallic reaction types. Future studies should also explore integrating multiple feature representations to classify or cluster the identified organometallic reactions based on the reaction types.