Techniques for generating coded representations of chemical compounds

Abstract

The present invention describes techniques and apparatus for generating coded representations of chemical compounds. In one embodiment, for example, an apparatus may include at least one memory and logic coupled to the at least one memory. The logic may be configured to: receive parsed information associated with at least one chemical compound; generate at least one coded representation of the at least one chemical compound, the coded representation including at least one fragment representing at least one attribute of the at least one chemical compound using a plurality of symbols. The present invention describes other embodiments.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of and priority to U.S. Provisional Patent Application No. 62 / 842,694, filed on May 3, 2019, which is incorporated by reference in its entirety. Technical Field

[0003] Embodiments herein relate generally to mass analysis of samples, and more particularly, to generating and using coded representations to identify compounds in samples. Background Art

[0004] Mass analysis techniques, such as mass spectrometry (MS) or liquid chromatography mass spectrometry (LC-MS), are used to identify and / or quantify molecular species in a sample. During analysis, molecules from a sample are ionized to acquire an electric charge and then introduced into a mass analyzer as ions. The mass analyzer determines analytical information associated with these ions based on the characteristics of these ions, such as their mass-to-charge ratio (m / z). The detector generates a signal related to the intensity of these ions at their specific m / z. The identification of sample components in conventional mass analysis experiments involves matching analytical information with models, libraries, etc. (e.g., "compound libraries") of known compounds. Non-limiting examples of compound libraries for MS analysis may include NIST databases and METLIN databases. The analytical information and these compound libraries are typically complex and high-resolution data. Therefore, conventional techniques for determining compounds of interest in samples, especially complex samples, are inefficient and time-consuming processes. Therefore, complex samples cannot be analyzed in real time or near real time using current methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0005] Figure 1 An embodiment of a first operating environment is shown.

[0006] Figure 2A and Figure 2B Depicted are exemplary encoding representations according to some embodiments.

[0007] Figure 3 Shown is the natural distribution of product ion m / z values ​​according to some embodiments.

[0008] Figure 4 Depicted is a graph of mass defect versus integer m / z values ​​of product ions according to some embodiments.

[0009] Figure 5 Depicted is information associated with time percentages of retention time and drift time according to some embodiments.

[0010] Figure 6Depicted are illustrative graphs and tables for generating segment vectors.

[0011] Figure 7 Depicted is a plot of m / z versus mass defect for a set of ions and a table of corresponding segment vectors.

[0012] Figure 8 Depicted is a plot of m / z versus mass defect for a set of ions and a table of corresponding segment vectors.

[0013] Fig. 9 Plots of m / z versus fractional m / z are depicted, along with corresponding data tables.

[0014] Fig.10 An embodiment of a computing architecture is shown. DETAILED DESCRIPTION

[0015] Various embodiments may generally relate to systems, methods and / or devices for generating coded representations of compounds. Generally speaking, a compound may be or may include a molecule, a molecular fragment, a precursor molecule, a product molecule, etc. In some embodiments, the coded representation may include an information element formed by symbols configured to represent the properties of the compound. In various embodiments, the coded representation may include a string containing alphanumeric characters. In an exemplary embodiment, the coded representation may include binary characters (e.g., 1 and 0), such as a string of binary characters, wherein at least a portion of these binary characters is set to a specific binary value. In some embodiments, the properties represented in the coded representation may include, but are not limited to, mass (m), charge (z), mass-to-charge ratio (m / z), retention time, drift time, collision cross-sectional area (CCS A 2 ) etc. In an exemplary embodiment, the encoded representation may include discrete character segments, wherein one or more of the character segments are associated with a specific attribute.

[0016] Thus, high resolution processes (e.g., MS, tandem MS, etc.) can be used to create coded representations of molecular species in a sample. These coded representations can be or can include low resolution versions of corresponding high resolution library information. The coded representations generated according to some embodiments can be used as targets for identifying and / or quantifying molecular species in a sample using high resolution or low resolution instruments. Thus, mass analysis using low resolution instruments according to some embodiments can be more efficient and require fewer processing resources to identify and / or quantify sample components compared to conventional methods.

[0017] In this description, many specific details, such as components and system configurations, may be set forth in order to provide a more thorough understanding of the described embodiments. However, those skilled in the art will appreciate that the described embodiments may be practiced without such specific details. In addition, some well-known structures, elements, and other features are not shown in detail to avoid unnecessarily obscuring the described embodiments.

[0018] In the following description, references to "one embodiment," "an embodiment," "an exemplary embodiment," "various embodiments," etc., indicate that an embodiment of the described technology may include a particular feature, structure, or characteristic, but more than one embodiment may include the particular feature, structure, or characteristic and not every embodiment must include the particular feature, structure, or characteristic. Furthermore, some embodiments may have some, all, or none of the features described for other embodiments.

[0019] As used in this specification and the claims, unless otherwise stated, the use of ordinal adjectives "first", "second", "third", etc. to describe elements merely refers to a specific instance of the referenced element or different instances of similar elements, and is not meant to imply that the elements so described must be in a specific order in time, space, order, or in any other manner.

[0020] Figure 1 An example of an operating environment 100 is shown that may represent some embodiments. Figure 1 As shown, the operating environment 100 may include an analysis system 105 operable to manage analytical information associated with an analytical instrument 115. In some embodiments, the analytical instrument 115 may be or may include a chromatography system, a liquid chromatography (LC) system, a gas chromatography (GC) system, a mass analyzer system, a mass detector system, a mass spectrometer (MS) system, an ion mobility spectrometer (IMS) system, a high performance liquid chromatography (HPLC) system, an ultra-high performance liquid chromatography (ULHPLC) system, or a mass spectrometer (MS) system. Systems, ultra high performance liquid chromatography (UHPLC) systems, solid phase extraction systems, sample preparation systems, capillary electrophoresis instruments, combinations thereof, components thereof, variations thereof, etc. Although LC, MS, and LC-MS are used in the examples in this detailed description, the embodiments are not limited thereto as other analytical instruments capable of operating according to some embodiments are contemplated herein.

[0021] In some embodiments, the analytical instrument 115 is operable to perform an analysis. For example, for an LC-MS system, the analytical instrument 115 may separate a sample and perform mass analysis on the separated sample to generate analytical information 136. The analytical information 136 may be or may include information associated with properties of sample components, such as precursor molecule information or ion information, product molecule information or ion information, retention time, drift time, charge, mass, m / z, etc. In an exemplary embodiment, the analytical information 136 may be or may include information generated as a result of the analysis performed by the analytical instrument 115, such as a value, a graph, an image, a calculation, a signal, etc.

[0022] In various embodiments, the analysis system 105 may include a computing device 110 that is communicatively coupled to an analytical instrument 115 or otherwise configured to receive and store analytical information 136 associated with the analytical instrument 115. For example, the analytical instrument 115 may be operable to provide analytical information to a location on a network 150 accessible to the computing device 110, such as a cloud computing environment and / or nodes 152a-n. In some embodiments, the computing device 110 may be operable to control, monitor, manage, or otherwise handle various operational functions of the analytical instrument 115. In some embodiments, the computing device 110 may be or may include a stand-alone computing device, such as a personal computer (PC), a server, a tablet computing device, a cloud computing device, etc.

[0023] like Figure 1 As shown, computing device 110 may include processing circuit 120, memory unit 130, and transceiver 140. Processing circuit 120 may be communicatively coupled to memory unit 130 and / or transceiver 140.

[0024] The processing circuit 120 may include and / or access various logics for performing processing according to some embodiments. For example, the processing circuit 120 may include and / or access compound encoding logic 122. Generally speaking, the compound encoding logic 122 may be a circuit arranged to perform specific operations (such as encoding, decoding, encryption, decryption, verification, etc.) related to processing distributed ledgers. The processing circuit and / or compound encoding logic 122 or parts thereof may be implemented in hardware, software, or a combination thereof. As used in this application, the terms "logic", "component", "layer", "system", "circuit", "decoder", "encoder" and / or "module" are intended to refer to computer-related entities, which may be hardware, a combination of hardware and software, software, or software in execution, and their examples are provided by the exemplary computing architecture 1000. For example, logic, circuitry, etc. may be and / or may include, but are not limited to, a process running on a processor, a processor, a hard drive, multiple storage drives (optical and / or magnetic storage media), an object, an executable program, an execution thread, a program, a computer, a hardware circuit, an integrated circuit, an application specific integrated circuit (ASIC), a programmable logic device (PLD), a digital signal processor (DSP), a field programmable gate array (FPGA), a system on a chip (SoC), a memory cell, a logic gate, a register, a semiconductor device, a chip, a microchip, a chipset, a software component, a program, an application, firmware, a software module, computer code, any combination of the foregoing, etc.

[0025] although Figure 1 Compound encoding logic 122 is depicted as being within processing circuitry 120, but embodiments are not limited thereto. For example, compound encoding logic 122 may be located within an accelerator, a processor core, an interface, a separate processor die, fully implemented as a software application (e.g., compound encoding application 132), etc.

[0026] The memory unit 130 may include various types of computer-readable storage media and / or systems in the form of one or more higher-speed memory units, such as read-only memory (ROM), random access memory (RAM), dynamic RAM (DRAM), double data rate DRAM (DDRAM), synchronous DRAM (SDRAM), static RAM (SRAM), programmable ROM (PROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, polymer memory (such as ferroelectric polymer memory, bidirectional memory, phase change or ferroelectric memory), silicon-oxide-nitride-oxide-silicon (SONOS) memory, magnetic card or optical card, device array (such as independent disk redundant array (RAID) drive), solid-state memory device (e.g., USB memory, solid-state drive (SSD), and any other type of storage medium suitable for storing information. In addition, the memory unit 130 may include various types of computer-readable storage media in the form of one or more lower-speed memory units, including internal (or external) hard disk drives (HDD), magnetic floppy disk drives (FDD), optical disk drives for reading or writing removable optical disks (e.g., CD-ROM or DVD), solid-state drives (SSD), etc.

[0027] According to some embodiments, the memory unit 130 can store a compound coding application 132 that can operate alone or in combination with the compound coding logic 122 to perform various compound coding functions. For example, the compound coding application 132 can generate and / or maintain the encoded molecule library 134 in the memory unit 130, the nodes 152a-n of the network 150, a data repository, etc. In various embodiments, the compound coding application 132 can process the parsed information 136 (alone or in combination with other information) to generate an encoded representation for the encoded molecule library 134.

[0028] See now Figure 2A and Figure 2B , which depicts a coded representation according to some embodiments. Figure 2AAs shown, the coded representation 205 can be formed by at least one segment 210a-n having at least one symbol 212a-n. At least a portion of each segment 210a-n can be used to encode one or more attributes of the compound. For example, in some embodiments, a portion of the segments 210a-n can include unspecified or otherwise unused symbols, which (e.g., leading or trailing 0 or other symbols) do not include information used to represent the compound. In some embodiments, at least one segment 210a-n and / or a portion thereof can be used to include information for identifying other aspects other than the characteristics of the compound, such as a device identifier, a sample identifier, a sample type (e.g., toxicology, food and environment, pharmaceutical preparation, biomedical, etc.), timestamp information, operator information, experimental information (e.g., duration, heat, humidity, sample preparation information, analytical information (e.g., MS, tandem MS), facility information, etc.

[0029] In some embodiments, at least one of the segments 210a-n and / or a portion thereof (e.g., segments 210a-n, symbols 212a-n, etc.) may include a primary key, such as an individual code associated with a sample, a data identifier (e.g., a spectrum ID) used in generating the encoded representation 205 and / or a portion thereof. In various embodiments, the primary key may provide, among other things, maintaining a chain of custody between the parsed information 136 (e.g., spectrum metadata) and the corresponding encoded representation 205.

[0030] For example, the encoded representation 205 may include a header or other segments 210a-n that include metadata associated with a sample, a compound, a confidence indicator, a library indicator, and / or other aspects associated with the encoded representation.

[0031] like Figure 2B As shown, the coded representation 220 can be used to encode specific information about a compound using segments 210a-f having binary symbols. In some embodiments, Figure 2B The particular coded representation 220 of may be referred to as a high entropy digital molecular ion signature (HE-DMIS or DMIS). For example, the coded representation 220 may include precursor z fragments 210a, precursor m / z fragments 210b, retention time fragments 210c, drift time fragments 210d, product z fragments, and / or product ion fragments 210f. In some embodiments, one or more symbols 212a-n may be used to represent characteristics of the fragments 210a-n, such as the relevance of the data for a sample, an analysis, a compound, etc. For example, if the first symbol of the drift time fragment 210d is "1", this may indicate that the information is not relevant to the sample (or vice versa).

[0032] The coded representation 220 may include more or fewer fragments, because the coded representation 220 is only for schematic illustration. In some embodiments, the coded representation may include 1 fragment, 2 fragments, 3 fragments, 5 fragments, 10 fragments, 20 fragments, 50 fragments, 100 fragments, 1000 fragments and any value or range (including end values) between any of these values. In various embodiments, the coded representation 205 may include any type of fragment that can be used to identify a compound and / or a compound associated therewith, including m, z, m / z, precursor z, precursor m / z, product z, product ion, retention time, drift time, etc. In an exemplary embodiment, the coded representation may include any type of character that can be used to encode information, including but not limited to characters containing letters and numbers, binary symbols, etc. In various embodiments, the coded representation may include 128 bits. In some embodiments, encoded representation 205 may include 8 bits, 16 bits, 32 bits, 64 bits, 128 bits, 256 bits, 512 bits, 1 KB, 1 MB, 1 TB, and any value or range between any two of these values. Embodiments are not limited in this context.

[0033] exist Figure 2B In the coded representation 220, the precursor z and / or the product z can be 2 3 The precursor m / z can be expressed by 2 11 bit indicates that the time (retention and / or drift (e.g., IMS drift time) can be obtained by 2 10 In various embodiments, the coded representation 205 may provide a 3+11+10+10=34-bit precursor code.

[0034] In various embodiments, data or metadata including z, m / z, relative retention time, relative drift time (e.g., for IMS), and / or collision cross section area (CCS A2) for each precursor ion and its associated product ion may be encoded as a HE-DMIS, such as a 128-bit HE-DMIS. In some embodiments, 34 of the 128 bits may be used to encode the precursor ion metadata, while the remaining 94 bits are reserved for the product ion metadata.

[0035] For example, the encoding representation 220 can depict the encoding of a three-dimensional product ion spectrum, including the m / z dimension, the retention time dimension, and the drift time dimension. In a three-dimensional experiment, for precursor ions, the encoding can include z (3 bits), integer m / z (11 bits), normalized retention time (10 bits), and normalized drift time (10 bits). For product ions, z (3 bits) and product ion m / z (91 bits) can be encoded. In various embodiments, for two-dimensional data (e.g., two-dimensional data that does not include chromatographic separation or ion mobility separation), 10 bits for encoding normalized values ​​can be added to the product ion encoding.

[0036] HE-DMIS experimentally generated from multiple samples can be initially matched by the precursor ion codes of these multiple samples. The product ion codes (including product ion z) of the matched precursors can then be associated. For example, only those bits that show a statistically significant match rate (P<0.01) can be retained for creating putative or potential DMIs. In some embodiments, potential DMIs from multiple collections may be matched and associated until the matching segments do not change no matter how many more data sets are added. It is at this point that, according to some embodiments, for example, the potential DMIs can become or be designated as a HE-DMIS and added to the encoded molecule library 134 as a target for automated high-throughput screening. In various embodiments, each HE-DMIS may include a master key, for example, a separate code associated with all samples and spectrum IDs used in the creation of each HE-DMIS. Among other things, the master key can provide a chain of custody between a spectrum or other metadata and its HE-DMIS.

[0037] Figure 3 shows the natural distribution of product ion m / z values. For example, Figure 3 A graph 305 of the natural distribution of product ion m / z values ​​generated by in-silico trypsin digestion of 7,500 randomly selected human proteins (e.g., trypsin) is depicted. The distribution shown may have been generated based on in-silico product ion generation from any class of chemical compounds (including but not limited to lipids, metabolites and other natural products, foods, pesticides, cosmetics and other chemical materials, such as hydrocarbons and polyethylene glycol). The distribution of in-silico product ion m / z values ​​can be filtered by both precursor ion z and product ion z. In some embodiments, the product ion segment width (m / z) of each bit can be varied based on these distributions. For example, in some embodiments, the encoding process can set the segment width of each bit so that the number of possible product ions of each segment is similar or substantially similar (e.g., as close to the same as possible). By limiting the maximum m / z by z and changing the segment width of each bit by the natural distribution of integer m / z, z increases the entropy of DMIS relative to the simple division of the fixed m / z scale divided by the number of available bits.

[0038] In various embodiments, the normalization of retention time and / or drift time may include a DMIS across a collection of associated data sets and a calculated match rate for each data set. If the match rate of the precursor ion encoding and the product ion encoding is considered to be statistically significant (e.g., p<0.01) relative to the total number of data sets in the collection, a composite DMSI may be created, retaining only the matching product ion positions whose match rate is considered to be statistically significant. For the DMIS of the identified compound (e.g., a peptide from a database search), the normalized intensity of each matching product ion may be calculated. For example, in various embodiments, the normalized intensity may be or may include the intensity ratio of the intensity of each individually matched product ion to the sum. In a DMIS where the number of verification bits is not proportional (e.g., greater than 7), the normalized intensity plays a role in limiting which bits are to be used. There may be a minimum number and a maximum number of bits required by the HE-DMIS. The maximum and minimum values ​​may be predicted based on the precursor ion integer m / z and / or the precursor ion integer z and the product ion z and which bits across the mass scale are active. When examining the corresponding histogram (e.g., counts versus bits) by integer precursors m / z and z, some bits may be matched at much higher frequencies than others. Therefore, in an exemplary embodiment, entropy can be increased by selecting highly replicated lower frequency bits for the DMIS.

[0039] Figure 4 The mass defect (fractional m / z) is plotted against the integer m / z value of the product ion. Figure 4 As shown, scatter plot 405 includes an x-axis representing the fractional portion (mass defect) of a molecular ion, and a y-axis representing integer values. A non-limiting description of the relationship between the charge of a molecular ion and its integer m / z and fractional m / z and drift time in the case of IMS is included in U.S. Pat. No. 9,734,996, which is incorporated herein by reference. The data bands of scatter plot 405 may represent the charge state of the product ion m / z. The distribution of precursor ions may be the same.

[0040] Figure 5 A representation 510 of the percentage of time for retention time and drift time is depicted, along with a table 510 of DMIs exceeding the minimum number of matched product ion positions, sorted by descending match rate and ascending retention time. Time zero is assigned the highest match rate for the earliest eluting encoded precursor and time 100 is assigned the highest match rate for the last eluting encoded precursor. The absolute time difference is measured and each individual time is normalized to the difference, thereby converting the time to time %. Given that hydrophobicity is an immutable property of chemical compounds, the elution order should be maintained, providing similar buffer systems and column matrices.

[0041] In some embodiments, precursor ion spectra and / or product ion spectra can be created according to various methods. Knowing the m / z of each precursor ion allows the encoding process to set a maximum m / z value for the last m / z segment in the product ion encoding. In some embodiments, the width (m / z) of each segment can be set by dividing the maximum m / z by the number of bits used in the product ion encoding. The number of HE-DMSIs generated for each precursor can be a direct function of the charge state of the precursor. For example, when the fragmentation mechanism is a collision cell, the maximum charge state of the product ion should not be higher than the charge of the precursor ion minus one, while the charge state of the precursor ion is greater than one. Singly charged ions produce product ions of the same charge state when fragmented. Therefore, when fragmentation is performed in a collision cell, 1+ precursor ions and 2+ precursor ions produce singly charged product ions. Precursor ions with a charge state greater than two can produce product ions from 1+ to z minus one of the precursor ion.

[0042] For a particular mass analyzer, there may be a maximum measurable m / z value that can be isolated and / or transmitted. Thus, there may be a highest product ion m / z value that can be encoded as a precursor ion for a given m / z and z. Figure 5 Table 510 shows how the precursor M r Product ion encoding is performed with both product ion m / z and z. In some embodiments, the two may be associated with corresponding groups of product ions. In various embodiments, the maximum charge state of the product generated via collisional dissociation (collision cell) may be equal to the charge state of the precursor ion minus 1. In an exemplary embodiment, the maximum product ion m / z used for encoding may be directly related to the number of bits used for product ion encoding, which may vary with the number of experimentally available pre-ion detection separation techniques (e.g., chromatographic techniques, ion migration techniques, etc.) used. For example, a one-dimensional separation may require only 10 bits, while a two-dimensional separation may require 20 bits. The embodiments are not limited to this context.

[0043] The generation of HE-DMIS library data as detailed herein can then be used in high throughput screening applications using low resolution instruments. For example, the HE-DMIS library data can be compared to experimentally derived data (e.g., sensitivity, resolution, dynamic range) by considering the relative detection differences between the HE-DMIS library data and such experimentally derived data. For example, HE-DMIS data can be generated using high resolution MS of molecular ions in simple and complex mixtures. These HE-DMIS can then be used as targets to identify and quantify these molecular ions and their surrogates in low resolution instruments.

[0044] In conventional systems, after the MS data has been generated and processed by the MS system, it can be searched using known libraries (i.e., NIST, METLIN, etc.). Conventional systems require large amounts of MS data to facilitate ion processing (e.g., ion identification and / or quantification), often requiring terabytes of data. However, HE-DMIS data can replace these large amounts of MS data in a compact form without sacrificing ion processing accuracy. Therefore, HE-DMIS data can allow real-time ion processing at the data generation site (e.g., at the analytical instrument). For example, because the operator may already have HE-DMIS data, if the operator is trying to detect pesticides, for example, the operator can extract the HE-DMIS data from the cloud (or other remote storage system), and when the MS data is generated in the instrument, a very small amount of information can be used at the acquisition point. Therefore, in some embodiments, the HE-DMIS data can be used as a filter to perform real-time identification of ions detected in the instrument. In this way, the need to store or use all MS data is reduced or even eliminated by using HE-DMIS data, specifically, all that is required is one or more symbol strings or bit strings represented by HE-DMIS.

[0045] Figure 6 An exemplary graph 605 and table 610 for generating a segment vector are depicted. In some embodiments, the segment vector may include a string of symbols corresponding to m / z values. In various embodiments, the segment vector symbol may be 1 if the ion is determined at the m / z value, and 0 otherwise. For example, once the product ion m / z segment has been normalized (e.g., for a precursor M ranging from 1000 to 1050), the segment vector symbol may be 0. r ), the calculated segment widths can be used to generate segment vectors (e.g., 128 segment vectors) for various spectra or other analytical data. In some embodiments, these spectra may include simulated spectra, validation spectra, and / or experimental composite product ion spectra. Figure 7 Depicted is a graph 705 of m / z versus mass defect for a set of ions and a table 710 of corresponding bin vectors ranging from 600-1030 m / z. Figure 8 Depicted is a graph 805 of m / z versus mass defect for a set of ions and a table 810 of corresponding segment vectors. Fig. 9 Depicted are a graph 905 of m / z versus fractional m / z for sonar analysis and a table 910 of corresponding data, as well as a graph 915 of m / z versus fractional m / z for QE analysis and a table 920 of corresponding data.

[0046] Fig.10An embodiment of an exemplary computing architecture 1000 suitable for implementing various embodiments as described above is shown. In various embodiments, the computing architecture 1000 may include or be implemented as part of an electronic device. In some embodiments, the computing architecture 1000 may represent, for example, a computing device 110. The embodiments are not limited in this context.

[0047] As used in this application, the terms "system" and "component" and "module" are intended to refer to computer-related entities, which may be hardware, a combination of hardware and software, software, or software in execution, examples of which are provided by the exemplary computing architecture 1000. For example, a component can be, but is not limited to, a process running on a processor, a processor, a hard drive, multiple storage drives (optical and / or magnetic storage media), an object, an executable file, an execution thread, a program, and / or a computer. For example, both an application running on a server and a server can be components. One or more components can reside in a process and / or an execution thread, and a component can be located on a computer and / or distributed between two or more computers. In addition, components can be communicatively coupled to each other through various types of communication media to coordinate operations. Coordination can involve unidirectional or bidirectional information exchange. For example, a component can communicate information in the form of a signal conveyed through a communication medium. The information can be implemented as a signal assigned to various signal lines. In such an assignment, each message is a signal. However, another embodiment can alternatively adopt a data message. Such data messages can be sent through various connections. Exemplary connections include parallel interfaces, serial interfaces, and bus interfaces.

[0048] The computing architecture 1000 includes various general computing elements, such as one or more processors, multi-core processors, co-processors, memory units, chipsets, controllers, peripherals, interfaces, oscillators, timing devices, video cards, audio cards, multimedia input / output (I / O) components, power supplies, etc. However, the embodiments are not limited to the implementation of the computing architecture 1000.

[0049] like Fig.10 As shown, computing architecture 1000 includes a processing unit 1004, a system memory 1006, and a system bus 10010. Processing unit 1004 can be any of various commercially available processors, including but not limited to: and processor; Application, embedded and security processors; and and Processors; IBM and Cell processor; Core(2) and Dual microprocessors, multi-core processors, and other multi-processor architectures may also be used as the processing unit 1004 .

[0050] The system bus 10010 provides an interface for system components, including but not limited to an interface connecting the system memory 1006 to the processing unit 1004. The system bus 10010 can be any of several types of bus structures, which can be further interconnected to a memory bus (with or without a memory controller), a peripheral bus, and a local bus using any of a variety of commercially available bus architectures. Interface adapters can be connected to the system bus 10010 via a slot architecture. Exemplary slot architectures can include but are not limited to accelerated graphics port (AGP), card bus, (extended) industry standard architecture ((E)ISA), micro channel architecture (MCA), NuBus, peripheral component interconnect (extended) (PCI (X)), PCI Express, personal computer memory card international association (PCMCIA), etc.

[0051] The system memory 1006 may include various types of computer-readable storage media in the form of one or more higher-speed memory cells, such as read-only memory (ROM), random access memory (RAM), dynamic RAM (DRAM), double data rate DRAM (DDRAM), synchronous DRAM (SDRAM), static RAM (SRAM), programmable ROM (PROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, polymer memory (such as ferroelectric polymer memory, bidirectional memory, phase change or ferroelectric memory), silicon-oxide-nitride-oxide-silicon (SONOS) memory, magnetic or optical cards, device arrays (such as redundant array of independent disks (RAID) drives), solid-state memory devices (e.g., USB memory, solid-state drives (SSDs), and any other type of storage medium suitable for storing information. Fig.10 In the illustrated embodiment, system memory 1006 may include non-volatile memory 1010 and / or volatile memory 1012. A basic input / output system (BIOS) may be stored in non-volatile memory 1010.

[0052] The computer 1002 may include various types of computer-readable storage media in the form of one or more slower memory units, including an internal (or external) hard disk drive (HDD) 1014, a magnetic floppy disk drive (FDD) 1016 for reading or writing to a removable magnetic disk 10110, and an optical drive 1020 (e.g., a CD-ROM or DVD) for reading or writing to a removable optical disk 1022. The HDD 1014, FDD 1016, and optical drive 1020 may be connected to the system bus 10010 via a HDD interface 1024, a FDD interface 1026, and an optical drive interface 10210, respectively. The HDD interface 1024 for external drive implementations may include at least one or both of a universal serial bus (USB) and IEEE 13104 interface technologies.

[0053] The drives and associated computer-readable media provide volatile and / or nonvolatile storage of data, data structures, computer-executable instructions, etc. For example, a number of program modules may be stored in the drives and memory units 1010, 1012, including an operating system 1030, one or more application programs 1032, other program modules 1034, and program data 1036. In one embodiment, one or more application programs 1032, other program modules 1034, and program data 1036 may include, for example, various applications and / or components of devices 105, 205, 305, and / or 405.

[0054] A user may enter commands and information into the computer 1002 through one or more wired / wireless input devices (e.g., a keyboard 10310 and a pointing device such as a mouse 1040). Other input devices may include a microphone, an infrared (IR) remote control, a radio frequency (RF) remote control, a game controller, a stylus, a card reader, a dongle, a fingerprint reader, a glove, a graphics tablet, a joystick, a keyboard, a retina reader, a touch screen (e.g., capacitive, resistive, etc.), a trackball, a trackpad, a sensor, a stylus, etc. These and other input devices are typically connected to the processing unit 1004 through an input device interface 1042 coupled to the system bus 1010, but may be connected through other interfaces such as a parallel port, an IEEE 13104 serial port, a game port, a USB port, an IR port, etc.

[0055] A monitor 1044 or other type of display device is also connected to the system bus 10010 via an interface, such as a video adapter 1046. The monitor 1044 may be internal or external to the computer 802. In addition to the monitor 1044, computers typically include other peripheral output devices, such as speakers, printers, and the like.

[0056] The computer 1002 can operate in a networking environment using logical connections via wired and / or wireless communications to one or more remote computers, such as remote computer 10410. The remote computer 10410 can be a workstation, server computer, router, personal computer, portable computer, microprocessor-based entertainment device, peer device, or other public network node, and typically includes many or all of the elements described relative to the computer 1002, but only a memory / storage device 1050 is shown for simplicity. The depicted logical connections include wired / wireless connections to a local area network (LAN) 1052 and / or a larger network, such as a wide area network (WAN) 1054. Such LAN and WAN networking environments are common in offices and companies, and facilitate enterprise-wide computer networks, such as intranets, all of which can be connected to a global communications network, such as the Internet.

[0057] When used in a LAN networking environment, the computer 1002 is connected to the LAN 1052 through a wired and / or wireless communication network interface or adapter 1056. The adapter 1056 can facilitate wired and / or wireless communication to the LAN 1052, which can also include a wireless access point disposed thereon for communicating with the wireless functionality of the adapter 1056.

[0058] When used in a WAN networking environment, the computer 1002 may include a modem 10510, or be connected to a communications server on the WAN 1054, or have other means for establishing communications over the WAN 1054, such as over the Internet. The modem 10510 may be internal or external and a wired and / or wireless device connected to the system bus 10010 via the input device interface 1042. In a networked environment, program modules depicted relative to the computer 1002, or portions thereof, may be stored in the remote memory / storage device 1050. It should be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers may be used.

[0059] The computer 1002 is operable to communicate with wired and wireless devices or entities using the IEEE 802 family of standards, such as wireless devices operatively arranged for wireless communications (e.g., IEEE 802.16 over-the-air modulation techniques). This includes at least Wi-Fi (or Wireless Fidelity), WiMax, and Bluetooth. TMWireless technology, etc. Therefore, the communication can be a predefined structure like a conventional network, or just an ad hoc communication between at least two devices. Wi-Fi networks use radio technologies called IEEE 802.11x (a, b, g, n, etc.) to provide secure, reliable, and fast wireless connectivity. Wi-Fi networks can be used to connect computers to each other, to the Internet, and to wired networks (using IEEE 802.3 related media and functions).

[0060] Many specific details have been set forth herein to provide a thorough understanding of the embodiments. However, those skilled in the art will appreciate that the embodiments may be practiced without these specific details. In other cases, well-known operations, components, and circuits are not described in detail to avoid obscuring the embodiments. It is understood that the specific structural and functional details disclosed herein may be representative and do not necessarily limit the scope of the embodiments.

[0061] The expressions "coupled" and "connected," as well as their derivatives, may be used to describe some embodiments. These terms are not intended to be synonymous with each other. For example, the terms "connected" and / or "coupled" may be used to describe some embodiments to indicate that two or more elements are in direct physical or electrical contact with each other. However, the term "coupled" may also mean that two or more elements are not in direct contact with each other, but still cooperate or interact with each other.

[0062] Unless expressly stated otherwise, it is understood that terms such as "processing", "computing", "calculating", "determining", etc. refer to the operation and / or process of a computer or computing system or similar electronic computing device, which processes and / or transforms data represented as physical quantities (e.g., electronic) within the registers and / or memories of the computing system into physical quantities similarly represented as memories, registers or other such information storage devices, transmission or display devices of the computing system. The embodiments are not limited to this context.

[0063] It should be noted that the methods described herein do not have to be performed in the order described or in any particular order. In addition, the various activities described with respect to the methods identified herein can be performed in series or in parallel.

[0064] Although specific embodiments have been depicted and described herein, it should be understood that any arrangement that is expected to achieve the same purpose can replace the specific embodiments shown. The present disclosure is intended to cover any and all adaptations or changes of various embodiments. It should be understood that the above description is carried out in an illustrative manner, rather than restrictive. After reading the above description, the combination of the above-mentioned embodiments and other embodiments not specifically described herein will be apparent to those skilled in the art. Therefore, the scope of the various embodiments includes any other application in which the above-mentioned compositions, structures and methods are used.

[0065] Although the subject matter has been described in language specific to structural features and / or methodological acts, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts are disclosed as example forms of implementing the claims.

Claims

1. A device, comprising: at least one memory; and logic, the logic being coupled to the at least one memory, the logic being configured to: receiving analytical information associated with at least one compound, generating at least one coded representation of the at least one compound, the coded representation comprising at least one fragment representing at least one property of the at least one compound using a plurality of symbols, wherein the at least one property comprises one or more of charge, mass, mass-to-charge ratio, retention time, drift time, or collision cross section, the generating comprising: accessing a plurality of ion signatures, each ion signature comprising a plurality of bits representing a precursor ion encoding and a plurality of bits representing a product ion encoding, matching a subset of the plurality of ion signatures based on their corresponding precursor ion codes to generate an initial match set, Correlating the ion signatures of the initial set of matches based on their corresponding product ion codes, retaining bits from the associated ion signatures that show a match rate with a predetermined statistical significance, at least one encoded representation comprising the retained bits, and The at least one encoded representation is added to an encoded molecule library configured to provide automated high throughput screening by providing the at least one encoded representation for use as a target for identifying or quantifying a molecular species in a sample.

2. The apparatus according to claim 1, wherein the analytical information is generated during mass analysis of the sample by at least one analytical instrument, wherein the at least one analytical instrument comprises a liquid chromatography (LC) system, a gas chromatography (GC) system, a mass analyzer system, a mass spectrometer (MS) system, an ion mobility spectrometer (IMS) system, a high performance liquid chromatography (HPLC) system, an ultra-high performance liquid chromatography (ULHPLC) system, system, and at least one of an ultra high performance liquid chromatography (UHPLC) system. The apparatus of claim 1 , wherein the plurality of symbols comprises binary symbols. 4 . The apparatus of claim 1 , the at least one compound comprising a product ion and the at least one property comprising a precursor charge or a precursor mass-to-charge ratio.

5. The apparatus of claim 1, the at least one encoded representation comprising a 128-bit string.

6. The apparatus of claim 1, the at least one encoded representation comprising a primary key configured to associate a plurality of samples with the encoded representation and parsed information comprising at least one of analytical information or operational information.

7. The apparatus of claim 1, the at least one fragment comprising a plurality of fragments, wherein at least one first fragment encodes information associated with a precursor ion of the compound and at least one second fragment encodes information associated with a product ion of the compound.

8. The device of claim 1, the at least one fragment comprising at least one of a charge associated with the at least one compound in a three-bit fragment, a relative time associated with the at least one compound in a ten-bit fragment, or a mass-to-charge ratio associated with the compound in an eleven-bit fragment.

9. The apparatus of claim 1, wherein the logic is configured to: receiving second parsed information associated with the sample, and At least one ion present in the sample is determined via comparison of the second resolved information and the at least one encoded representation.

10. A computer-implemented method, the computer-implemented method comprising: receiving analytical information associated with at least one compound, generating at least one coded representation of the at least one compound, the coded representation comprising at least one fragment representing at least one property of the at least one compound using a plurality of symbols, wherein the at least one property comprises one or more of charge, mass, mass-to-charge ratio, retention time, drift time, or collision cross section, the generating comprising: accessing a plurality of ion signatures, each ion signature comprising a plurality of bits representing a precursor ion encoding and a plurality of bits representing a product ion encoding, matching a subset of the plurality of ion signatures based on their corresponding precursor ion codes to generate an initial match set, Correlating the ion signatures of the initial set of matches based on their corresponding product ion codes, retaining bits from the associated ion signatures that show a match rate with a predetermined statistical significance, at least one encoded representation comprising the retained bits, and The at least one encoded representation is added to an encoded molecule library configured to provide automated high throughput screening by providing the at least one encoded representation for use as a target for identifying or quantifying a molecular species in a sample.

11. The computer-implemented method of claim 10, wherein the analytical information is generated during mass analysis of the sample via at least one analytical instrument, the at least one analytical instrument comprising a liquid chromatography (LC) system, a gas chromatography (GC) system, a mass analyzer system, a mass spectrometer (MS) system, an ion mobility spectrometer (IMS) system, a high performance liquid chromatography (HPLC) system, an ultra-high performance liquid chromatography (ULLC) ... a mass spectrometer (MS) system, an ion mobility spectrometer (IMS) system, a mass spectrometer (MS) system, an ion mobility spectrometer (IMS) system, a mass spectrometer (MS) system, an ion mobility spectrometer (IMS) system, an ion mobility spectrometer (IMS) system, an ion mobility spectro system, and at least one of an ultra high performance liquid chromatography (UHPLC) system.

12. The computer-implemented method of claim 10, the plurality of symbols comprising binary symbols.

13. The computer-implemented method of claim 10, the at least one compound comprising a product ion and the at least one property comprising a precursor charge or a precursor mass-to-charge ratio.

14. The computer-implemented method of claim 10, the at least one encoded representation comprising a 128-bit string.

15. The computer-implemented method of claim 10, the at least one encoded representation comprising a primary key configured to associate a plurality of samples with the encoded representation.

16. The computer-implemented method of claim 10, the at least one fragment comprising a plurality of fragments, wherein at least one first fragment encodes information associated with a precursor ion of the compound and at least one second fragment encodes information associated with a product ion of the compound.

17. The computer-implemented method of claim 10, the at least one fragment comprising at least one of a charge associated with the at least one compound in a three-bit fragment, a relative time associated with the at least one compound in a ten-bit fragment, or a mass-to-charge ratio associated with the compound in an eleven-bit fragment.

18. The computer-implemented method of claim 10, the method comprising: receiving second parsed information associated with the sample, and At least one ion present in the sample is determined via comparison of the second resolved information and the at least one encoded representation.

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