Deep Convolutional Neural Networks for predicting highest priority functional group in organic molecules

Kunal Khatri; Vineet Mehta

cs.LGMar 25, 2026

Deep Convolutional Neural Networks for predicting highest priority functional group in organic molecules

Authors: Kunal Khatri, Vineet Mehta

Organizations: Dhirubhai Ambani Institute of Information & Communication Technology

Abstract

Our work addresses the problem of predicting the highest priority functional group present in an organic molecule. Functional Groups are groups of bound atoms that determine the physical and chemical properties of organic molecules. In the presence of multiple functional groups, the dominant functional group determines the compound's properties. Fourier-transform Infrared spectroscopy (FTIR) is a commonly used spectroscopic method for identifying the presence or absence of functional groups within a compound. We propose the use of a Deep Convolutional Neural Networks (CNN) to predict the highest priority functional group from the Fourier-transform infrared spectrum (FTIR) of the organic molecule. We have compared our model with other previously applied Machine Learning (ML) method Support Vector Machine (SVM) and reasoned why CNN outperforms it.

Figures & tables

Functional Group	Number of Data samples
Carboxylic	537
Ester	333
Amide	948
Nitrile	371
Aldehyde	357
Ketone	183

Table 1: The class frequencies of highest priority functional groups present in our dataset.

No.	1600 (A)		3200 (B)		4730 (C)
No.	SVM	CNN	SVM	CNN	SVM	CNN
1	64.51	68.85	66.18	75.01	69.12	79.70
2	63.31	69.53	66.54	74.20	68.55	77.77
3	64.40	67.32	65.93	74.51	68.30	76.89
4	63.56	70.49	67.45	73.89	70.63	79.84
5	63.87	68.14	67.62	73.97	78.80	68.68

Table 2: Accuracy of the ML models on (A) 1600 molecules randomly sampled from our dataset, (B) 3200 molecules randomly sampled from our dataset and (C) entire dataset of 4730 molecules.

K	SVM	CNN
1	68.42	78.43
2	80.1	88.20
3	89.2	94.3

Table 3: Accuracy for Top K predictions made by ML models on the dataset, that is the percentage of predictions in which the actual highest priority functional group is among the top k predictions (based on probability) made by the ML model.

No.	SVM	CNN
1	61.38	72.20
2	61.45	71.59
3	65.08	73.09
4	61.01	71.83
5	61.59	71.38
6	62.05	71.46

Table 4: Accuracy of ML models on undersampled data, i.e, randomly sample 200 examples of each class and form a dataset on which we perform Stratified 10 fold test.

SVM	RF	CNN
85.96	86.02	90.32

Table 5: Accuracy on applying ML models on previous paper’s [ 21 ] dataset.

Training Data	SVM	CNN
DATA-NEW	68%	74%
DATA-OLD	50%	59%

Table 6: Comparing accuracy of ML models, trained on different data.

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Infrared Spectroscopy Preference Optimization

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Infrared (IR) spectra provide characteristic signals of molecular structure, which are often interpreted by experts via functional-group identification or library matching, making the process time-consuming and ambiguous. Recent machine learning methods have made progress in molecular structure elucidation using molecular formulas and IR spectra. However, these models often infer unreasonable candidate molecular structures, including top-ranked predictions. More specifically, the molecular formula implied by a candidate structure often fails to match the input molecular formula, and the candidate's theoretical IR spectrum is often inconsistent with the observed IR spectrum. To address these issues, we propose Formula- and IR-Matched Preference Optimization (FIRMPO), a general and plug-and-play chemical feedback-driven preference optimization framework for molecular structure elucidation. FIRMPO incorporates chemical feedback as preference signals based on exact molecular formula matching and IR spectral consistency to guide reasonable structure predictions. Unlike generic preference optimization methods, FIRMPO is tailored to molecular structure elucidation while remaining model-agnostic, enabling it to be readily integrated with different structure prediction models in this class. This encourages models to prioritize structures that satisfy the chemical feedback, leading to a substantial improvement in the accuracy of top-ranked predictions. Extensive experiments on three widely used IR datasets show that FIRMPO significantly improves molecular structure elucidation accuracy over existing baselines.

Abstract

Infrared Spectroscopy

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