When machine learning models learn chemistry I: quantifying explainability with matched molecular pairs | Synapse
February 11, 2026Open Access
When machine learning models learn chemistry I: quantifying explainability with matched molecular pairs
Puntos clave
The aim is to investigate the explainability of machine learning methods in the context of chemistry, focusing on matched molecular pairs.
Utilized machine learning algorithms to analyze molecular pairs.
Assessed different explainability methods to evaluate their reliability.
Conducted comparative analysis to identify effective strategies for explainability.
Identified challenges in assessing the reliability of explainability methods.
Demonstrated variations in effectiveness of explainability techniques across different molecular pairs.
Reported a need for deeper investigation into specific chemistry problems.
Resumen
Explainability methods in machine learning-driven research are increasingly being used, but it remains challenging to assess their reliability without deeply investigating the specific problem at hand.