Molecular Fingerprints Research Articles

DiPPI: A Curated Data Set for Drug-like Molecules in Protein-Protein Interfaces.

Proteins interact through their interfaces, and dysfunction of protein-protein interactions (PPIs) has been associated with various diseases. Therefore, investigating the properties of the drug-modulated PPIs and interface-targeting drugs is critical. Here, we present a curated large data set for drug-like molecules in protein interfaces. We further introduce DiPPI (Drugs in Protein-Protein Interfaces), a two-module web site to facilitate the search for such molecules and their properties by exploiting our data set in drug repurposing studies. In the interface module of the web site, we present several properties, of interfaces, such as amino acid properties, hotspots, evolutionary conservation of drug-binding amino acids, and post-translational modifications of these residues. On the drug-like molecule side, we list drug-like small molecules and FDA-approved drugs from various databases and highlight those that bind to the interfaces. We further clustered the drugs based on their molecular fingerprints to confine the search for an alternative drug to a smaller space. Drug properties, including Lipinski's rules and various molecular descriptors, are also calculated and made available on the web site to guide the selection of drug molecules. Our data set contains 534,203 interfaces for 98,632 protein structures, of which 55,135 are detected to bind to a drug-like molecule. 2214 drug-like molecules are deposited on our web site, among which 335 are FDA-approved. DiPPI provides users with an easy-to-follow scheme for drug repurposing studies through its well-curated and clustered interface and drug data and is freely available at http://interactome.ku.edu.tr:8501.

Molecular Fingerprinting of the Biodegradation of Petroleum Organic Pollutants in Groundwater and under Site-Specific Environmental Impacts

A quantitative and qualitative assessment using molecular markers derived from compound-specific indices for indicating groundwater impacted by petroleum spills in an oil field was recently undertaken and demonstrated serious contamination, with both high total petroleum hydrocarbons (TPH) (3.68–7.32 mg/L) and hazardous compounds in the groundwater. A petroleum source was identified, and the analysis revealed a decreasing trend of fresh petroleum input, along with groundwater advection and an increasing trend of biodegradation potential at locations farther from the source. This was confirmed via microbial analysis with both biodegrading microorganisms and diversity indices (Shannon, Simpson, and Pielou) and the principal component analysis (PCA) modeling approach, which classified the field samples into three types according to the distribution correlations between different organic compounds. Biodegradation was believed to be the dominant sink of hydrocarbons due to the increasing Pr/C17 and Ph/C18 values with seasonal changes. Raised temperatures activated the microbial degradation process; specifically, low-weight hydrocarbons degraded more rapidly than high-weight hydrocarbons, resulting in the accumulation of an unresolved complex mixture of bioproducts at locations that were farther away. Spatially, the Pr/C17 and Ph/C18 values increased from the upstream to the downstream areas, showing substantial biodegradation. The relationships between the molecular markers and chemical indices were quantified via canonical correlation analysis (CCA) to visually explain the interactive reaction processes. It was also demonstrated that the biodegradation of petroleum organics can be characterized by the consumption of dissolved oxygen and a decreasing Pr/Ph ratio, due to system reduction. These results demonstrate that compound-specific molecular markers, coupled with biochemical parameters, can effectively support a better understanding and effective fingerprinting of the fate and transport of petroleum organic contaminants, thus offering valuable technical support for a cost-effective remediation strategy.

Exploring the chemical subspace of RPLC: A data driven approach

BackgroundThe chemical space is comprised of a vast number of possible structures, of which an unknown portion comprises the human and environmental exposome. Such samples are frequently analyzed using non-targeted analysis via liquid chromatography (LC) coupled to high-resolution mass spectrometry often employing a reversed phase (RP) column. However, prior to analysis, the contents of these samples are unknown and could be comprised of thousands of known and unknown chemical constituents. Moreover, it is unknown which part of the chemical space is sufficiently retained and eluted using RPLC. ResultsWe present a generic framework that uses a data driven approach to predict whether molecules fall ‘inside’, ‘maybe’ inside, or ‘outside’ of the RPLC subspace. Firstly, three retention index random forest (RF) regression models were constructed that showed that molecular fingerprints are able to predict RPLC retention behavior. Secondly, these models were used to set up the dataset for building an RPLC RF classification model. The RPLC classification model was able to correctly predict whether a chemical belonged to the RPLC subspace with an accuracy of 92% for the testing set. Finally, applying this model to the 91 737 small molecules (i.e., ≤1 000 Da) in NORMAN SusDat showed that 19.1% fall ‘outside’ of the RPLC subspace. Significance and noveltyThe RPLC chemical space model provides a major step towards mapping the chemical space and is able to assess whether chemicals can potentially be measured with an RPLC method (i.e., not every RPLC method) or if a different selectivity should be considered. Moreover, knowing which chemicals are outside of the RPLC subspace can assist in reducing potential candidates for library searching and avoid screening for chemicals that will not be present in RPLC data.

Tuning Multipolar Mie Scattering of Particles on a Dielectric-Covered Mirror.

Optically resonant particles are key building blocks of many nanophotonic devices such as optical antennas and metasurfaces. Because the functionalities of such devices are largely determined by the optical properties of individual resonators, extending the attainable responses from a given particle is highly desirable. Practically, this is usually achieved by introducing an asymmetric dielectric environment. However, commonly used simple substrates have limited influences on the optical properties of the particles atop. Here, we show that the multipolar scattering of silicon microspheres can be effectively modified by placing the particles on a dielectric-covered mirror, which tunes the coupling between the Mie resonances of microspheres and the standing waves and waveguide modes in the dielectric spacer. This tunability allows selective excitation, enhancement, suppression, and even elimination of the multipolar resonances and enables scattering at extended wavelengths, providing transformative opportunities in controlling light-matter interactions for various applications. We further demonstrate with experiments the detection of molecular fingerprints by single-particle mid-infrared spectroscopy and with simulations strong optical repulsive forces that could elevate the particles from a substrate.

Flower Species Ingredient Verification Using Orthogonal Molecular Methods.

Flowers are gaining considerable interest among consumers as ingredients in food, beverages, cosmetics, and natural health products. The supply chain trades in multiple forms of botanicals, including fresh whole flowers, which are easier to identify than dried flowers or flowers processed as powdered or liquid extracts. There is a gap in the scientific methods available for the verification of flower species ingredients traded in the supply chains of multiple markets. The objective of this paper is to develop methods for flower species ingredient verification using two orthogonal methods. More specifically, the objectives of this study employed both (1) DNA-based molecular diagnostic methods and (2) NMR metabolite fingerprint methods in the identification of 23 common flower species ingredients. NMR data analysis reveals considerable information on the variation in metabolites present in different flower species, including color variants within species. This study provides a comprehensive comparison of two orthogonal methods for verifying flower species ingredient supply chains to ensure the highest quality products. By thoroughly analyzing the benefits and limitations of each approach, this research offers valuable insights to support quality assurance and improve consumer confidence.

DeepRA: A novel deep learning-read-across framework and its application in non-sugar sweeteners mutagenicity prediction

Non-sugar sweeteners (NSSs) or artificial sweeteners have long been used as food chemicals since World War II. NSSs, however, also raise a concern about their mutagenicity. Evaluating the mutagenic ability of NSSs is crucial for food safety; this step is needed for every new chemical registration in the food and pharmaceutical industries. A computational assessment provides less time, money, and involved animals than the in vivo experiments; thus, this study developed a novel computational method from an ensemble convolutional deep neural network and read-across algorithms, called DeepRA, to classify the mutagenicity of chemicals. The mutagenicity data were obtained from the curated Ames test data set. The DeepRA model was developed using both molecular descriptors and molecular fingerprints. The obtained DeepRA model provides accurate and reliable mutagenicity classification through an independent test set. This model was then used to examine the NSSs-related chemicals, enabling the evaluation of mutagenicity from the NSSs-like substances. Finally, this model was publicly available at https://github.com/taraponglab/deepra for further use in chemical regulation and risk assessment.

Beyond driver mutations: exploring the landscape of mutational signatures in adenocarcinomas of the bladder†.

The emergence of mutational signatures in cancer research represents a paradigm shift, offering profound insights into tumor biology, therapeutic strategies, and risk assessment. A recent article published in The Journal of Pathology delves into the significance of mutational signatures, assessing their role as molecular fingerprints that illuminate the etiology of individual cancer cases. By deciphering the diverse patterns of DNA alterations, in the context of urachal and nonurachal bladder adenocarcinomas, researchers can unravel intrinsic and environmental risk factors shaping cancer development. The study highlighted a predominance of environmental risk factors in Chinese populations affected by bladder adenocarcinomas. In this context, in this commentary, we highlight some of the potential applications of molecular signatures in differential diagnosis and therapy prediction. As the field continues to evolve, a deeper understanding of mutational signatures promises to revolutionize precision oncology by offering personalized approaches informed by comprehensive mutational patterns of tumors. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

A novel interpretable machine learning model approach for the prediction of TiO2 photocatalytic degradation of air contaminants

Air contaminants lead to various environmental and health issues. Titanium dioxide (TiO2) features the benefits of autogenous photocatalytic degradation of air contaminants. To evaluate its performance, laboratory experiments are commonly used to determine the kinetics of the photocatalytic-degradation rate, which is labor intensive, time-consuming, and costly. In this study, Machine Learning (ML) models were developed to predict the photo-degradation rate constants of air-borne organic contaminants with TiO2 nanoparticles and ultraviolet irradiation. The hyperparameters of the ML models were optimized, which included Artificial Neural Network (ANN) with Bayesian optimization, gradient booster regressor (GBR) with Bayesian optimization, Extreme Gradient Boosting (XGBoost) with optimization using Hyperopt, and Catboost combined with Adaboost. The organic contaminant was encoded through Molecular fingerprints (MF). Imputation method was applied to deal with the missing data. A generative ML model Vanilla Gan was utilized to create synthetic data to further augment the size of available dataset and the SHapley Additive exPlanations (SHAP) was employed for ML model interpretability. The results indicated that data imputation allowed for the full utilization of the limited dataset, leading to good machine learning prediction performance and preventing common overfitting problems with small-sized data. Additionally, augmenting experimental data with synthetic data significantly improved prediction accuracy and considerably reduced overfitting issues. The results ranked the feature importance and assessed the impacts of different experimental variables on the rate of photo-degradation, which were consistent with physico-chemical laws.

Transcriptome-wide mapping of small-molecule RNA-binding sites in live cells.

Small molecules targeting RNA can be valuable chemical probes and potential therapeutics. The interactions between small molecules, particularly fragments, and RNA, however, can be difficult to detect due to their modest affinities and short residence times. Here, we describe the procedures for mapping the molecular fingerprints of small molecules in vitro and throughout the human transcriptome in live cells, identifying both the targets bound by the small molecule and the sites of binding therein. For complete details on the use and execution of this protocol, please refer to 1.

Raman-based machine-learning platform reveals unique metabolic differences between IDHmut and IDHwt glioma.

Formalin-fixed, paraffin-embedded (FFPE) tissue slides are routinely used in cancer diagnosis, clinical decision-making, and stored in biobanks, but their utilization in Raman spectroscopy-based studies has been limited due to the background coming from embedding media. Spontaneous Raman spectroscopy was used for molecular fingerprinting of FFPE tissue from 46 patient samples with known methylation subtypes. Spectra were used to construct tumor/non-tumor, IDH1WT/IDH1mut, and methylation-subtype classifiers. Support vector machine and random forest were used to identify the most discriminatory Raman frequencies. Stimulated Raman spectroscopy was used to validate the frequencies identified. Mass spectrometry of glioma cell lines and TCGA were used to validate the biological findings. Here, we develop APOLLO (rAman-based PathOLogy of maLignant gliOma)-a computational workflow that predicts different subtypes of glioma from spontaneous Raman spectra of FFPE tissue slides. Our novel APOLLO platform distinguishes tumors from nontumor tissue and identifies novel Raman peaks corresponding to DNA and proteins that are more intense in the tumor. APOLLO differentiates isocitrate dehydrogenase 1 mutant (IDH1mut) from wild-type (IDH1WT) tumors and identifies cholesterol ester levels to be highly abundant in IDHmut glioma. Moreover, APOLLO achieves high discriminative power between finer, clinically relevant glioma methylation subtypes, distinguishing between the CpG island hypermethylated phenotype (G-CIMP)-high and G-CIMP-low molecular phenotypes within the IDH1mut types. Our results demonstrate the potential of label-free Raman spectroscopy to classify glioma subtypes from FFPE slides and to extract meaningful biological information thus opening the door for future applications on these archived tissues in other cancers.

Multi-task aquatic toxicity prediction model based on multi-level features fusion

IntroductionWith the escalating menace of organic compounds in environmental pollution imperiling the survival of aquatic organisms, the investigation of organic compound toxicity across diverse aquatic species assumes paramount significance for environmental protection. Understanding how different species respond to these compounds helps assess the potential ecological impact of pollution on aquatic ecosystems as a whole. Compared with traditional experimental methods, deep learning methods have higher accuracy in predicting aquatic toxicity, faster data processing speed and better generalization ability. ObjectivesThis article presents ATFPGT-multi, an advanced multi-task deep neural network prediction model for organic toxicity. MethodsThe model integrates molecular fingerprints and molecule graphs to characterize molecules, enabling the simultaneous prediction of acute toxicity for the same organic compound across four distinct fish species. Furthermore, to validate the advantages of multi-task learning, we independently construct prediction models, named ATFPGT-single, for each fish species. We employ cross-validation in our experiments to assess the performance and generalization ability of ATFPGT-multi. ResultsThe experimental results indicate, first, that ATFPGT-multi outperforms ATFPGT-single on four fish datasets with AUC improvements of 9.8%, 4%, 4.8%, and 8.2%, respectively, demonstrating the superiority of multi-task learning over single-task learning. Furthermore, in comparison with previous algorithms, ATFPGT-multi outperforms comparative methods, emphasizing that our approach exhibits higher accuracy and reliability in predicting aquatic toxicity. Moreover, ATFPGT-multi utilizes attention scores to identify molecular fragments associated with fish toxicity in organic molecules, as demonstrated by two organic molecule examples in the main text, demonstrating the interpretability of ATFPGT-multi. ConclusionIn summary, ATFPGT-multi provides important support and reference for the further development of aquatic toxicity assessment. All of codes and datasets are freely available online at https://github.com/zhaoqi106/ATFPGT-multi.

Prediction of adverse drug reactions due to genetic predisposition using deep neural networks.

Drug development is a long and costly process, often limited by the toxicity and adverse drug reactions (ADRs) caused by drug candidates. Even on the market, some drugs can cause strong ADRs that can vary depending on an individual polymorphism. The development of Genome-wide association studies (GWAS) allowed the discovery of genetic variants of interest that may cause these effects. In this study, the objective was to investigate a deep learning approach to predict genetic variations potentially related to ADRs. We used single nucleotide polymorphisms (SNPs) information from dbSNP to create a network based on ADR-drug-target-mutations and extracted matrixes of interaction to build deep Neural Networks (DNN) models. Considering only information about mutations known to impact drug efficacy and drug safety from PharmGKB and drug adverse reactions based on the MedDRA System Organ Classes (SOCs), these DNN models reached a balanced accuracy of 0.61 in average. Including molecular fingerprints representing structural features of the drugs did not improve the performance of the models. To our knowledge, this is the first model that exploits DNN to predict ADR-drug-target-mutations. Although some improvements are suggested, these models can be of interest to analyze multiple compounds over all of the genes and polymorphisms information accessible and thus pave the way in precision medicine.

Advancements in medical research: Exploring Fourier Transform Infrared (FTIR) spectroscopy for tissue, cell, and hair sample analysis.

Fourier Transform Infrared (FTIR) spectroscopy has emerged as a powerful analytical tool in medical research, offering non-invasive and precise examination of the molecular composition of biological samples. The primary objective of this review is to underscore the benefits of FTIR spectroscopy in medicinal research, emphasizing its ability to delineate molecular fingerprints and assist in the identification of biochemical structures and key peaks in biological samples. This review comprehensively explores the diverse applications of FTIR spectroscopy in medical investigations, with a specific focus on its utility in analyzing tissue, cells, and hair samples. Various sources, including Google Scholar, PubMed, WorledCat and Scopus, were utilized to conduct this comprehensive literature review. Recent advancements showcase the versatility of FTIR spectroscopy in elucidating cellular and molecular processes, facilitating disease diagnostics, and enabling treatment monitoring. Notably, FTIR spectroscopy has found significant utility in clinical assessment, particularly in screening counterfeit medicines, owing to its user-friendly operation and minimal sample preparation requirements. Furthermore, customs officials can leverage this technique for preliminary analysis of suspicious samples. This review aims to bridge a gap in the literature and serve as a valuable resource for future research endeavors in FTIR spectroscopy within the medical domain. Additionally, it presents fundamental concepts of FTIR spectroscopy and spectral data interpretation, highlighting its utility as a tool for molecular analysis using Mid-Infrared (MIR) radiation.

Infrared Spectroscopy in Gynecological Oncology: A Comprehensive Review of Diagnostic Potentials and Challenges.

The early detection of gynecological cancers, which is critical for improving patient survival rates, is challenging because of the vague early symptoms and the diagnostic limitations of current approaches. This comprehensive review delves into the game-changing potential of infrared (IR) spectroscopy, a noninvasive technology used to transform the landscape of cancer diagnosis in gynecology. By collecting the distinctive vibrational frequencies of chemical bonds inside tissue samples, Fourier-transform infrared (FTIR) spectroscopy provides a 'molecular fingerprint' that outperforms existing diagnostic approaches. We highlight significant advances in this field, particularly the identification of discrete biomarker bands in the mid- and near-IR spectra. Proteins, lipids, carbohydrates, and nucleic acids exhibited different absorption patterns. These spectral signatures not only serve to distinguish between malignant and benign diseases, but also provide additional information regarding the cellular changes associated with cancer. To underscore the practical consequences of these findings, we examined studies in which IR spectroscopy demonstrated exceptional diagnostic accuracy. This review supports the use of IR spectroscopy in normal clinical practice, emphasizing its capacity to detect and comprehend the intricate molecular underpinnings of gynecological cancers.

Graph neural networks for CO[formula omitted] solubility predictions in Deep Eutectic Solvents

Deep Eutectic Solvents (DESs) are a promising class of solvents for CO2 capture. DESs are complex mixtures that can be designed to optimize CO2 solubility and overall capture process efficiency. However, the vast design landscape of DES mixtures makes experimental investigation prohibitive; as such, there is a need for computational models that can quickly and efficiently navigate the design space and inform data collection efforts. In this work, we propose Graph Neural Network (GNN) models for predicting CO2 solubility for DESs; the GNN leverages a mixture graph representation that captures the molecular structure of the DES components as well as their intermolecular interactions. We compare the GNN framework against alternative architectures (neural networks, graph convolution networks, and random forests) and data representations (molecular fingerprints, sigma profiles, and graphs). We show that the proposed approach offers superior predictive performance; specifically, we show that solubility can be predicted reliably directly from molecular structure (without the need of using sigma profiles as proposed in previous studies). This result is important, as obtaining sigma profiles requires expensive density functional theory computations. We also explored the ability of GNNs to predict solubility for new DES mixtures and operating conditions. We found that the model extrapolates across temperature reliably. However, we also found deficiencies in the ability of the model to predict solubility for DES mixtures, pressures, and molar ratio not included in the training sets; we show that this is due to an inherent lack of chemical diversity in datasets available in the literature. The proposed computational capabilities can thus help navigate the design space of DES and inform data collection efforts. Our models, data, and benchmarks are shared as Python code implemented in Jupyter notebooks.

Predicting Chemical Immunotoxicity through Data-Driven QSAR Modeling of Aryl Hydrocarbon Receptor Agonism and Related Toxicity Mechanisms.

Computational modeling has emerged as a time-saving and cost-effective alternative to traditional animal testing for assessing chemicals for their potential hazards. However, few computational modeling studies for immunotoxicity were reported, with few models available for predicting toxicants due to the lack of training data and the complex mechanisms of immunotoxicity. In this study, we employed a data-driven quantitative structure-activity relationship (QSAR) modeling workflow to extensively enlarge the limited training data by revealing multiple targets involved in immunotoxicity. To this end, a probe data set of 6,341 chemicals was obtained from a high-throughput screening (HTS) assay testing for the activation of the aryl hydrocarbon receptor (AhR) signaling pathway, a key event leading to immunotoxicity. Searching this probe data set against PubChem yielded 3,183 assays with testing results for varying proportions of these 6,341 compounds. 100 assays were selected to develop QSAR models based on their correlations to AhR agonism. Twelve individual QSAR models were built for each assay using combinations of four machine-learning algorithms and three molecular fingerprints. 5-fold cross-validation of the resulting models showed good predictivity (average CCR = 0.73). A total of 20 assays were further selected based on QSAR model performance, and their resulting QSAR models showed good predictivity of potential immunotoxicants from external chemicals. This study provides a computational modeling strategy that can utilize large public toxicity data sets for modeling immunotoxicity and other toxicity endpoints, which have limited training data and complicated toxicity mechanisms.

POxload: Machine Learning Estimates Drug Loadings of Polymeric Micelles.

Block copolymers, composed of poly(2-oxazoline)s and poly(2-oxazine)s, can serve as drug delivery systems; they form micelles that carry poorly water-soluble drugs. Many recent studies have investigated the effects of structural changes of the polymer and the hydrophobic cargo on drug loading. In this work, we combine these data to establish an extended formulation database. Different molecular properties and fingerprints are tested for their applicability to serve as formulation-specific mixture descriptors. A variety of classification and regression models are built for different descriptor subsets and thresholds of loading efficiency and loading capacity, with the best models achieving overall good statistics for both cross- and external validation (balanced accuracies of 0.8). Subsequently, important features are dissected for interpretation, and the DrugBank is screened for potential therapeutic use cases where these polymers could be used to develop novel formulations of hydrophobic drugs. The most promising models are provided as an open-source software tool for other researchers to test the applicability of these delivery systems for potential new drug candidates.

Discovery of Delirium Biomarkers through Minimally Invasive Serum Molecular Fingerprinting.

Delirium presents a significant clinical challenge, primarily due to its profound impact on patient outcomes and the limitations of the current diagnostic methods, which are largely subjective. During the COVID-19 pandemic, this challenge was intensified as the frequency of delirium assessments decreased in Intensive Care Units (ICUs), even as the prevalence of delirium among critically ill patients increased. The present study evaluated how the serum molecular fingerprint, as acquired by Fourier-Transform InfraRed (FTIR) spectroscopy, can enable the development of predictive models for delirium. A preliminary univariate analysis of serum FTIR spectra indicated significantly different bands between 26 ICU patients with delirium and 26 patients without, all of whom were admitted with COVID-19. However, these bands resulted in a poorly performing Naïve-Bayes predictive model. Considering the use of a Fast-Correlation-Based Filter for feature selection, it was possible to define a new set of spectral bands with a wider coverage of molecular functional groups. These bands ensured an excellent Naïve-Bayes predictive model, with an AUC, a sensitivity, and a specificity all exceeding 0.92. These spectral bands, acquired through a minimally invasive analysis and obtained rapidly, economically, and in a high-throughput mode, therefore offer significant potential for managing delirium in critically ill patients.

Molecular fingerprint-aided prediction of organic solute rejection in reverse osmosis and nanofiltration

Reverse osmosis and nanofiltration are used to purify feedwaters that contain a range of harmful organic solutes. The rejection of many of these solutes is poorly understood due to our limited ability to experimentally measure removal of any given compound. In this work, we present a machine learning approach that predicts organic solute rejection using molecular fingerprints that encode chemical structure features, such as functional groups and rings, into simple binary vectors. We trained machine learning models on a database of 1906 membrane rejection measurements including 228 organic compounds and 39 types of reverse osmosis and nanofiltration membranes. Three types of molecular fingerprint models (structural key, circular, and path based) were compared, and we observed that the Molecular Access System (MACCS) structural key had high performance (coefficient of determination of 0.87 with the testing set), fast calculation time due to its short bit-length, and easy interpretability. In addition to evaluating prediction performance, Shapley Additive Explanations (SHAP) analysis was implemented to gain a better molecular-scale understanding of membrane rejection, identifying molecular substructures that are important in determining their rejection. Overall, this work presents a method to predict the rejection of compounds that uses readily available molecular structure information and improves our ability to understand rejection mechanisms.

An Ultra-broadband Metallic Plasmonic Antenna for Ultrasensitive Molecular Fingerprint Identification.

Near-field enhanced mid-infrared light-matter interactions via metallic plasmonic antennae (PA) have attracted much attention but are inevitably limited by the detuning between their narrow band and the broad applied spectral range. Here, we develop a new low-temperature incubation synthetic method to acquire uniform Ag microparticles (MPs) with numerous hotspots. Their plasmonic band is remarkably extended by the plasmonic coupling of numerous hotspots and covers the entire mid-infrared range (400-4000 cm-1). Hence, the almost complete molecular fingerprint of 4-mercaptobenzonitrile was successfully probed for the first time via resonant surface-enhanced infrared absorption (rSEIRA), and the rSEIRA spectra of different essential amino acids were further detected and exhibit a high spectral identification degree assisted by machine learning. This work changes the inertia perception of "narrow band and large size but small hotspot area" of mid-infrared metallic PA and paves the way for the ultrasensitive mid-infrared optical sensing.