For the longest time, the identification and differentiation of isomers are a fundamental challenge in molecular characterization, especially in biomedical and pharmaceutical research.
Traditional methods like spectroscopic and chromatographic methods are effective but they require extensive reference databases and manual peak matching. This makes the untargeted identification of isomers extremely difficult.
If differentiating common isomers is not hard enough, we also have epimeric cerebrosides – biomolecules known for their neurological functions. Differentiating these isomers is like a boss level challenge because their structural differences are very subtle. Unlike typical isomers, which may vary in functional groups or bond connectivity, epimeric cerebrosides differ only in the spatial orientation of a single hydroxyl group at the C4 position. These minute variations in structure makes distinguishing these molecules a formidable challenge.
However, a study conducted by Emily Tan and her colleagues successfully integrated Surface-Enhanced Raman Spectroscopy (SERS) with a machine learning model to create a forward-predictive chemical taxonomy for epimeric cerebrosides.
The study demonstrated the capacity of predictive modeling in structure elucidation and quantification of chemical products even at trace concentrations. This concentration-independent ML-driven SERS chemical taxonomy can forward predict epimeric cerobrosides over a wide range of concentrations, from 10-4 to 10-10 M.
Synthesis of Cerebrosides
The study by Tan utilized biomolecules that benefit the most from SERS chemical taxonomy, the cerebrosides, particularly the glucocerebrosides (GlcCer) and galactocerebrosides (GalCer). The researchers first functionalized an Ag-based SERS substrate with 4-mercaptophenylboronic acid to capture specific epimers at their C4 site. This will form unique epimer-4-MPBA complexes, each with distinct SERS peaks and signatures.
The SERS fingerprints were then compared with DFT simulations, identifying distinct spectral features correlating to key structural attributes of the epimers:
- Presence or absence of epimers (at 1330 cm-1)
- Monosaccharide vs cerebroside structure (at 1300 cm-1)
- Saturation level of the ceramide moiety (at 1595-1603 cm-1)
- Glucosyl vs galactosyl groups (at 414-419 cm-1)
- Carbon chain length of GlcCer (at 1023 cm-1) and GalCer (at 687 cm-1)
A total of 840 spectra were recorded, with each of the cerebroside having 60 spectra measured at 10⁻⁴ M concentration. Additional data points were also collected at concentrations ranging from 10⁻⁴ M to 10⁻¹⁰ M to assess the model’s generalizability.
Feature Engineering Process
The feature engineering stage focused on enhancing ML model accuracy and reducing complexity by parameterizing and isolating relevant spectral peaks. The spectral peaks of the epimeric cerebrosides were first analyzed to extract five key spectral features – position, intensity, full width at half maximum (FWHM), skew, and Gaussian/Lorentzian ratio. This step reduced the input features from 1200 to 95 for analysis.
Although the researchers did not state it, the choice for these attributes were most likely aimed to capture the most critical and descriptive features of the SERS peaks while also avoiding any redundant information. This process streamlined and refined the data in a lot of ways including:
- Dimensionality reduction: Reducing the spectra from 1200 to 95 (19 spectra for each 5 attributes) reduced the computational load of the algorithm. This made modeling a lot faster and less prone to overfitting. This also retained critical information about each peak’s characteristics.
- Comprehensive peak characterization: The five attributes chosen provided a well rounded representation of each peak:
- Position and intensity are important for identifying specific structural properties
- Width can indicate peak resolution and complexity of the molecule.
- Skew can reveal asymmetry, which may correlate with molecular interactions and/or conformational variations.
- Gaussian/Lorentzian ratio provides insights into peak shape, helping differentiate between different types of bonding and material properties.
- Feature Relevance for ML: The five attributes captured diverse spectral information without overwhelming the model. This balance allowed the model to focus on key structural distinctions between cerebrosides, improving its ability to identify relevant patterns during classification.
Before running the hierarchical machine learning framework, t-SNE clustering was performed to visualize high-dimensional spectral data in two dimensions. After t-SNE, distinct clusters emerged, showing differentiation by: absence/presence of epimers, monosaccharide vs cerebroside, saturation level of the ceramide moiety, and differentiation of GlcCer and GalCer structures.
Hierarchical Machine Learning Framework
The hierarchical machine learning framework used in the study utilizes a five level taxonomy model with 4 random forest classifiers (RF-C1 to RF-C4) and two support vector machine aggressors (SVM-R 5.1 and 5.2) in a step-by-step, sequential process to deduce the specific structural characteristics.
Framework Function
The framework was designed to deduce the structure of a compound progressively, starting from a general level classification to the most specific. Here’s a breakdown of how this framework works (shown in the image as an inverted pyramid).
- RF-C1: Confirms the presence of epimers
- RF-C2: Differentiates monosaccharides (glucose and galactose) from the cerebrosides
- RF-C3: Classifies as saturated or unsaturated cerebrosides.
- RF-C4: Identifies the glycosyl type (GlcCer or GalCer)
- SVM-R 5.1 and 5.2: Predicts carbon chain length for GlcCer and GalCer, respectively.
Each stage of this hierarchical framework builds on the previous, using the classifiers and regressors to construct a molecular profile and identify the cerebroside structure accurately.
Why Use Random Forests and Support Vector Machines in Succession?
One of the benefits of using random forests is that they are ideal for complex data structures, those with both linear and non-linear relationships. By using an ensemble of decision trees, RF classifiers can capture subtle spectral variations, making them powerful for progressive classification tasks like in this research.
In addition, support vector machines excel the most at boundary-specific tasks where separating features, such as chain length, require a precise decision boundary. The SVM aggressors here are well-suited for estimating carbon chain length because of their ability to find optimal hyperplanes that could classify data, even at high-dimensions.
Aside from the use of random forests and SVM in the hierarchical ML model, it is also crucial to note that the design of the framework allows it to eliminate structural possibilities. The framework focuses on specific features at each stage, and deduces the structure incrementally.
Unlike single-model classifiers, the sequential approach of this study avoids forcing unknown samples into predefined categories, critical for untargeted identification.
Removing any of the components, like one RF or an SVM, would disrupt the detailed, structured analysis. For example, if we remove RF-C3, we will fail in separating saturated and unsaturated cerebrosides. This would break the succeeding RF-C4, which then eventually fails at category 5 (SVM 5.1 and 5.2).
Validating & Evaluating the Model
To ensure that the model is generalizable, the researchers removed individual cerebroside spectra sets for blind predictions, stratifying the remaining spectra and employing a 5-fold cross-validation over 100 iterations. The hierarchical ML model achieved over 90% classification accuracy across RF classifiers, effectively deducing key structural features. On the other hand, the SVM regressors accurately predicted the carbon chain lengths.
The high accuracy between test sets signifies the model’s reliability for identifying structural characteristics in a wide range of cerebrosides.
The accuracy of the model was tested using the regular prediction accuracy and F1 score. This was repeated 99 times to derive the average prediction accuracy for the class. On the other hand, the random forests were evaluated using classification accuracy, precision, recall, and F1 score.
Validating the Results of the Framework
The SERS-based chemical taxonomy framework showed effectiveness in identifying and differentiating 11 epimeric cerebrosides, achieving 90% classification accuracy.The framework also generalizes well across concentrations from 10-4 to 10-10 M, indicating its robust performance even at trace levels.
By using a 5-fold cross-validation setup with 100 iterations, the model predicts both molecular identity and carbon chain length with high precision, differing no more than one carbon unit.
Most importantly, the system identifies untrained samples within a range of concentrations and maintains 87–100% accuracy. This signifies that the model is concentration-independent.
These results validate the model’s utility for untargeted, rapid identification and structural elucidation of isomeric biomolecules in scenarios where analyte concentration may vary, positioning it as a versatile tool for future applications in SERS-based chemical sensing.
Limitations and Recommendations
One of the most glaring limitations of the study is the computational cost needed to perform the experiment. Training and validating the model with 5-fold cross-validation over 100 iterations demand significant computational power and time. This cost may be a constraint when scaling the system to larger datasets or for broader molecular libraries.
Another limitation is the generalizability of the study itself. While the model generalizes well across a range of concentrations of cerebrosides, the reliance on SERS spectral differences as a unique chemical “fingerprint” could be challenged when analyzing more complex or similar biomolecular structures with smaller spectral differences. Furthermore, the SERS-based taxonomy may not perform as effectively with molecule classes unless sufficient structural features exist to distinguish them within structural data.
One way to reduce computational complexity and improve generalizability is data augmentation and transfer learning. This could be done by training initial models on a broader class of biomolecules, and then fine-tuning it for specific compounds. Additionally, synthetic data augmentation techniques could be explored to expand the spectral dataset without requiring more physical samples.
We can also integrate the model with other spectroscopic techniques. Combining SERS with complementary spectroscopic methods such as NMR or mass spectrometry may enhance the accuracy and reliability of the model. This is true especially in differentiating highly similar molecular structures or when handling very complex mixtures.
One other limitation of the study is detection limit sensitivity. Although the taxonomy can classify samples at low concentrations, there is some decrease in classification accuracy at detection limits (e.g., 10-10 M), where accuracy drops due to sample misclassification. The technique’s reliability might be affected at such ultra-trace levels, especially in more complex biological or environmental samples.
Exploring automated hyperparameter optimization can fine-tune model performance more efficiently. Furthermore, improving the interpretability of ML predictions, such as feature importance mapping, would allow for better chemical insight and potentially highlight more detailed structural features.
Lastly, expanding the training set to include a more extensive range of biomolecules, and testing the model on actual biological or environmental samples, would strengthen the approach’s robustness and help adapt it for practical applications beyond controlled lab conditions.
What This Means For Us
The study demonstrated the effectiveness of an integrated SERS-based chemical taxonomy machine learning framework for untargeted structural elucidation of 11 epimeric cerebrosides. It achieved >90% accuracy and a robust single epimer and multiplex quantification with <10%. Also, the framework used in the study was used to identify and quantify the cerebrosides at concentrations between 10-4 and 10-10 M.
The approach used in the study was found to be generalizable, even predicting the identity of unknown molecules at concentrations up to six orders of magnitude lower than those used in training.
With robust classification and quantification accuracy, the hierarchical ML model provides a powerful tool for untargeted chemical analysis, offering significant potential for applications in biomolecular detection and analysis. This may include drug metabolite identification, environmental pollutant detection, or protein conformational analysis.
Further improvements may include integration with other spectroscopic methods, and expansion to more complex chemical systems. This can enhance the utility and applicability of the chemical taxonomy ML framework in diverse chemical challenges.
Reference: This article is based on the findings and data presented in the original research study. For full details, methodologies, and supporting information, you may access the research here.
