Patent Publication Number: US-2022223232-A1

Title: System and method for evaluating biological data using and applying a virtual landscape

Description:
CROSS REFERENCE TO RELATED APPLICATION 
     The present application claims the benefit of U.S. patent application Ser. No. 63/135,382, filed Jan. 8, 2021 which is hereby incorporated by reference in its entirety. 
    
    
     FIELD OF THE INVENTION 
     The present invention concerns a system and method for evaluating textual data, such as biologic sequence identifiers obtained from source documents, using a virtual N-dimensional array. The described system and method, in part, are directed to extracting from the source documents biologic sequences and converting those sequence identifiers into coded forms. Further aspects are directed to plotting, or identifying plot coordinates, such as a 2D or 3D plot, of coded forms in a low dimensional space, in which the location of each coded form in the space is based on the similarity of each of coded forms to one another. 
     BACKGROUND OF THE INVENTION 
     It is known in the art to use statistical techniques to evaluate libraries of documents to extract usable information for example, U.S. patent Ser. No. 10/013,467, herein incorporated by reference in its entirety, teaches extracting data from source documents. Furthermore, it is known in the art to convert and manipulate chemical structures using computer analyses and algorithms. These techniques fall short of providing an environment in which new chemical entities can be identified, let alone one in which new chemical entities can be identified which relate to a particular biological target or particular subject matter. 
     Currently, in machine learning and statistics, one way to assess a similarity between, say, chemical entities represented by chemical identifiers such as chemical structure formulas, is to convert the chemical structure formula into a coded representation. It is also known to use analytic procedures to convert a symbolic representation (e.g., chemical identifier) of a molecule (e.g., chemical entity) into a useful number or value for the purpose of comparing, as one example, one chemical entity to another. For example a variety of descriptors is known and can be used in lieu of keybit binary representations in order to generate values that are useful in implementing certain embodiments of the invention. As non-limiting examples, known descriptors include 0D (i.e., constitutional descriptors), 1D (i.e., lists of structural fragments), 2D (i.e., graph variants), 3D (i.e., quantum-chemical descriptors), and/or 4D (i.e., GRID). 
     When there are a large number of variables in the dataset, such as in multivariable datasets defined by the keysets mentioned above, dimensionality reduction techniques can be used to evaluate the datasets. These techniques can be used to reduce datasets to a few principal variables in order to more easily visualize the relationship between datasets. Node or diffusion mapping algorithms, for instance, can be used to embed high-dimensional data sets into, say, a Euclidean space. Using this technique, the coordinates of each data point in the Euclidean space are computed from the eigenvectors and eigenvalues (i.e., non-zero vectors or values that, when multiplied by a matrix, generate multiples of the vectors or values). Such mapping techniques are computationally inexpensive and are useful in reducing and displaying visually-complex multivariable datasets such as product reviews, internet traffic, and E-commerce reports. 
     The techniques discussed above are all appropriate for mapping chemical structures that are represented by respective datasets. Turning to the question of new chemical entity discovery, however, while there exist chemical compound discovery techniques that are useful in identifying novel chemical compounds, current systems are not able to generate additional compounds in the low-dimensional space. 
     One technique for compound discovery which is used in identifying therapeutic compounds is scaffold hopping. Scaffold-hopping is used to identify isofunctional molecular structures with significantly different molecular backbones. Some types of scaffold-hopping include, but are not limited to, heterocycle replacements, ring opening or closure, peptidomimetics and topology-based hopping techniques. Other bioisosteric replacement techniques are also useful in predicting and evaluating new chemical compounds. 
     In short, current analysis systems are configured to process large variable data sets and present lower dimensional (e.g., 2- or 3-dimensions) visualizations to a user. Yet these systems are not configured to generate additional data relating to a chemical that might be further included or missing from the data set, and are entirely unable to identify absent chemical structures that conform to a reduced dimensional space. 
     Additionally, it has been determined that biologic identifiers (such as nucleic acid or protein sequences) require additional processing and analysis in order to evaluate sequences in lower dimensional visualizations to a user. 
     Therefore, what is needed in the art is a system and a method which can construct an artificial environment which is trained around a particular biologic target or subject matter, such as a virtual manifold or a virtual array of nodes, from which common nucleotide or protein sequences, structural motifs, configurations or chemical features can be identified, transformed into new coded forms and inserted into the artificial environment for determining whether its placement within the artificial environment fits at least one prescribed criterion. What is further needed in the art is a system and method for predicting and generating nucleotide or protein sequences or portions thereof, and chemical identifiers, that describe new chemical entities not currently found within the source documents used to generate the artificial environment, yet which fill gaps in the artificial environment. The present invention addresses these and other needs. 
     SUMMARY OF THE INVENTION 
     The present invention, according to one aspect, is directed to a computer-implemented method for extracting representational data relevant to a particular subject matter, such as nucleotide or protein sequences or chemical entities, from source documents which discuss the subject matter, and populating an n-dimensional manifold, such as an n-dimensional node array, with coded representations of the representational data (e.g. chemical identifiers, nucleotide or protein sequences, textual fingerprint data, or a hybrid of the foregoing). The method comprises generating a virtual n-dimensional manifold within a memory of a computer using a manifold-generator module which comprises code executing in a processor and placing, using a placement module which comprises code executing in the processor, each of the coded representations at a location, such as a particular node within the manifold, using an unsupervised learning algorithm. 
     Thus, according to one or more implementations described herein, one or more processors are configured to generate an n-dimensional map using the results of a query for compounds enumerated within a collection of documents describing a particular biological target of interest. Here the chemical identifiers contained within the results are transformed into document coded forms and used to generate the n-dimensional map. A curated set of known small molecules and peptides (specifically their molecular structures) are converted into the same type of coded forms used to generate the n-dimensional map. In turn, the processor is configured to evaluate the distance between these curated coded forms and the closest cluster (or node) in the generated n-dimensional map by determining the distance between curated coded form and the node of a cluster of coded forms present in the n-dimensional map. The processor is further configured by one or more code modules to filter-out distant curated coded form molecules by removing those greater than a pre-determined distance from its closest cluster&#39;s weight vector. Additionally, the processor is configured in one or more implementations to remove or filter coded forms of molecules from the n-dimensional map that are populated to nodes that contains no document coded forms. The processor is also configured by code to compare the remaining curated coded forms that are within the pre-determined distance of a node to the document coded forms associated with that node by calculating the distance between the descriptor vectors for a given curated coded form and document coded form. The processor is further configured by one or more modules to filter-out relationships between curated coded forms and document coded forms over a certain threshold and also calculate the similarity between a coded form and a document coded form. Using the distance, similarity, and number of patented molecules within a pre-determined distance of a coded form, the coded forms are ranked. 
     Furthermore, according to one or more implementations described herein, the chemical structures of a curated set of compounds (such as small molecules or peptides) are converted into the curated coded forms. Additionally, one or more processors are configured to generate an n-dimensional map using the results of a query for compounds enumerated within a collection of documents describing a particular biological target of interest. Here the chemical identifiers contained within the results are transformed into document coded forms. A processor is configured by one or more modules to generate using both the curated and document coded forms, an n-dimensional map. Upon generation, the processor is configured to evaluate the distance between the curated coded forms and the closest cluster (or node) in the generated n-dimensional map by determining the distance between curated coded form and the node of a cluster of document coded forms present in the n-dimensional map. The processor is further configured by one or more code modules to filter-out distant curated coded form molecules by removing those greater than a pre-determined distance from its closest cluster&#39;s weight vector. Additionally, the processor is configured in one or more implementations to remove or filter coded forms of molecules from the n-dimensional map that are populated to a node that contains no document coded forms. The processor is also then configured by code to compare the remaining curated coded forms that are within the pre-determined distance of a node to the document coded forms associated with that node by calculating the distance between the descriptor vectors for a given curated coded form and document coded form. The processor is further configured by one or more modules to filter-out relationships between curated coded forms and document coded forms over a certain threshold and also calculate the similarity between a coded form and a document coded form. Using the distance, similarity, and number of patented molecules within a pre-determined distance of a coded form, the coded forms are ranked. 
     Optionally, the method as above, according to a further aspect, can include the additional step of adjusting a placement of each coded form within the virtual manifold in the memory using an adjustment module which implements a neural network algorithm using code executing in the processor. 
     In a further arrangement, the method also includes predicting new representational data that will occupy the manifold, such as a particular node of the array when placed within the array. In an example where the representational data are chemical identifiers, the further steps include comparing at least one chemical feature (“CF”) corresponding to the coded form contained within at least a first array node to at least one CF corresponding to the coded form contained in at least a second array node using a CF module which comprises code executing in the processor, the first and second nodes each sharing a border with each other or a third node in the virtual node array. The method according to this aspect includes identifying common CFs between the first and second array nodes using a commonality module which comprises code executing in the processor, and generating at least one new coded form based on combinations of the identified common CFs which, when inserted into the virtual node array, results in a placement in the first or second node or within a third adjacent node using a coded form generator module which comprises code executing in the processor. The method outputs a chemical identifier corresponding to the new coded form and augments a data store of chemical entities for the user. 
     In another aspect, the present invention can be embodied by a computer-implemented system utilizing a processor configured by a plurality of code modules executing therein to output representational data, such as chemical identifiers and synthesis strategies relating thereto, that is not present in a data store of representational data. In particular, the system includes instruction code in the form of software modules that configures the processor to obtain, from a collection of source documents pertaining to a particular subject matter, the representational data described therein and convert the representational data into a high-dimensional coded form. The system according to this aspect can further comprise code that generates a virtual n-dimensional manifold within a memory of a computer using a manifold-generator module and which places, using a placement module, each of the coded representations at a location, such as a particular node within the node array, using an unsupervised learning algorithm. 
     A comparison module is included or utilized to compare, with a processor, a first plotted coded form at a first coordinate location within the virtual n-dimensional manifold, with a second plotted coded form at a second coordinate location in the virtual n-dimensional manifold. In one embodiment, this comparison module is utilized when at least one coordinate location between the first coded form and the second coded form lacks a plotted coded form, and in another embodiment can be used when at least one coordinate location adjacent the first and second coded forms is vacant. The comparison of coded forms is used to identify any common features shared by the first and second coded forms, e.g., common chemical features or sequence similarities. The system further includes a generation module utilized to execute code on a processor in order to generate at least one new coded form based on combinations of common features of the entities corresponding to the coded form located at the first coordinate location and the coded form located at the second coordinate location of the virtual n-dimensional manifold. 
     Optionally, the generation module described above is further configured to generate a synthesis strategy for synthesizing representational data described by the at least one new coded form using a standard synthesis strategy, such as retrosynthetic analysis. 
     According to one or more further implementations described herein, one or more processors are configured to generate an n-dimensional map using the results of a query for sequences (nucleotide or protein) or other identifiers enumerated within a collection of documents describing a particular biological target of interest. Here the sequences or other identifiers contained within the results are transformed into document coded forms and used to generate the n-dimensional map. A curated set of known biologics (specifically their sequences) are converted into the same type of coded forms used to generate the n-dimensional map. In turn, the processor is configured to evaluate the distance between these curated coded forms and the closest cluster (or node) in the generated n-dimensional map by determining the distance between curated coded form and the node of a cluster of coded forms present in the n-dimensional map. The processor is further configured by one or more code modules to filter-out distant curated coded form sequences by removing those greater than a pre-determined distance from its closest cluster&#39;s weight vector. Additionally, the processor is configured in one or more implementations to remove or filter coded forms of sequences from the n-dimensional map that are populated to nodes that contains no document coded forms. The processor is also configured by code to compare the remaining curated coded forms that are within the pre-determined distance of a node to the document coded forms associated with that node by calculating the distance between the descriptor vectors for a given curated coded form and document coded form. The processor is further configured by one or more modules to filter-out relationships between curated coded forms and document coded forms over a certain threshold and also calculate the similarity between a coded form and a document coded form. Using the distance, similarity, and number of patented sequences within a pre-determined distance of a coded form, the coded forms are ranked. 
     Furthermore, according to one or more implementations described herein, the sequences of a curated set of biologics (such as nucleotide or protein sequences) are converted into the curated coded forms. Additionally, one or more processors are configured to generate an n-dimensional map using the results of a query for compounds enumerated within a collection of documents describing a particular biological target of interest. Here the sequence identifiers, such as a nucleic acid or protein sequence, contained within the results are transformed into document coded forms. A processor is configured by one or more modules to generate using both the curated and document coded forms, an n-dimensional map. Upon generation, the processor is configured to evaluate the distance between the curated coded forms and the closest cluster (or node) in the generated n-dimensional map by determining the distance between curated coded form and the node of a cluster of document coded forms present in the n-dimensional map. The processor is further configured by one or more code modules to filter-out distant curated coded form sequences by removing those greater than a pre-determined distance from its closest cluster&#39;s weight vector. Additionally, the processor is configured in one or more implementations to remove or filter coded forms of molecules from the n-dimensional map that are populated to a node that contains no document coded forms. The processor is also then configured by code to compare the remaining curated coded forms that are within the pre-determined distance of a node to the document coded forms associated with that node by calculating the distance between the descriptor vectors for a given curated coded form and document coded form. The processor is further configured by one or more modules to filter-out relationships between curated coded forms and document coded forms over a certain threshold and also calculate the similarity between a coded form and a document coded form. Using the distance, similarity, and number of patented sequences within a pre-determined distance of a coded form, the coded forms are ranked. 
     In another aspect, the present invention can be embodied in a computer-implemented system utilizing a processor configured by a plurality of code modules executing therein to output a DNA, RNA, amino acid or other sequence data and synthesis strategies relating thereto, corresponding to biomedical or biopharmaceutical products not present a data store. In particular, the system includes instruction code in the form of software modules that configures the processor to obtain from a collection of source documents pertaining to a particular subject matter the representational data described therein and to convert each instance of representational data found in the accessed documents into a high-dimensional coded form. These high-dimensional coded forms are plotted to a virtual n-dimensional space or manifold, such as an n-dimensional node array. A comparison module is included or utilized to compare, with a processor, a first plotted coded form at a first coordinate location within the virtual n-dimensional manifold, with a second plotted coded form at a second coordinate location in the virtual n-dimensional manifold. In one embodiment, this comparison module is utilized when at least one coordinate location between the first coded and form the second coded form lacks a plotted coded form, and in another embodiment can be used when at least one coordinate location adjacent the first and second coded forms is vacant. This comparison is used to identify any common features shared by the first and second plotted coded forms. The system further includes a generation module utilized to execute code on a processor in order to generate at least one new coded form based on combinations of common features of the entities corresponding to the coded form located at the first coordinate location and the second coordinate location of the virtual n-dimensional manifold. 
     The present invention, in further aspects, can include steps or system components to synthesize a compound in which the chemical formula for the compound is determined according to a new chemical entity discovery process as described herein. 
     The present invention, in still further aspects, can comprise a compound described by a new chemical entity identifier that has been generated according to the steps of the process provided and system described herein, wherein the compound is synthesized according to a synthesis strategy generated as described hereinbelow. 
     These and other features and aspects will be understood from the discussion below of certain embodiments of the invention. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is an overview block diagram detailing the arrangement of elements of the system described herein in accordance with one embodiment of the invention. 
         FIG. 2A  is a flow diagram detailing the steps of an embodiment of the method applied to chemical entities as described herein. 
         FIG. 2B  is a flow diagram detailing the steps of an embodiment of the method applied to biologics as described herein. 
         FIG. 2C  is a flow diagram detailing the steps of an embodiment of the method applied to biologics as described herein. 
         FIG. 3  is a block diagram of an example system in accordance with an embodiment of the present invention. 
         FIG. 4  is a diagram of the operation of the plotting module according to certain embodiments of the system described. 
         FIGS. 5A, 5B, 5C, and 5D  are depictions of the visualization component of the described system and method. 
         FIG. 6  is a diagram of the operation of the new chemical entity generating module according to certain embodiments of the system described. 
         FIG. 7A  is a detailed view illustrating common structural elements of nearby or adjacent nodes of a visualization map. 
         FIG. 7B  is a flow diagram in accordance with certain embodiments of the invention. 
         FIG. 8  is an illustrated diagram of the elements of the system of an embodiment of the present invention. 
         FIG. 9  is a flow diagram in accordance with certain embodiments of the invention. 
         FIG. 10  is a flow diagram in accordance with certain embodiments of the invention. 
     
    
    
     DESCRIPTION OF CERTAIN EMBODIMENTS OF THE INVENTION 
     By way of overview and introduction, the presently provided methods of retargeting a n-dimensional trained map concerns a computer implemented system and method for ranking chemical entities based on the analysis of how similar the chemical entities are to chemical entities referenced in source documents, such as patent documents. The system and method are configured to carry out a series of steps, implemented as instructions executed by a processor of a computer, in order to generate a virtual space in which chemical entities are arranged based on their similarity and from which output as one or more rankings, of how similar new chemical identifiers corresponding to chemical entities not disclosed in the source documents are to those provided in the source documentation. Thus, according to one or more implementations described herein, one or more processors are configured to generate an n-dimensional map using the results of a query for compounds enumerated within a collection of documents describing a particular biological target of interest. Here the chemical identifiers contained within the results are transformed into document coded forms and used to generate the n-dimensional map. The chemical structures of a curated set of compounds (such as small molecules or peptides) are converted into the same type of coded forms used to generate the n-dimensional map. In turn, the processor is configured to evaluate the distance between these curated coded forms and the closest cluster (or node) in the generated n-dimensional map by determining the distance between curated coded form and the node of a cluster of coded forms present in the n-dimensional map. The processor is further configured by one or more code modules to filter-out distant curated coded form compounds by removing those greater than a pre-determined distance from its closest cluster&#39;s weight vector. 
     The processor is also then configured by code to compare remaining curated coded forms that are within the pre-determined distance of a node to the document coded forms by calculating the distance between the descriptor vectors for a given curated coded form and document coded form. The processor is further configured by one or more modules to filter-out relationships between curated coded forms and document coded forms over a certain threshold, also calculate similarity between a coded form and a document coded form. Using the distance, similarity, and number of patented molecules within a pre-determined distance of a coded form, the coded forms are ranked. 
     Throughout the following discussion, the American spelling of the singular “formula” and plural “formulas” is used instead of the British spelling convention “formulae/formula.” 
     As used herein, “representational identifier” means a format or nomenclature utilized as a representation of particular subject matter, such as nucleotide sequences, protein sequences, amino acid sequences, textual summaries or syntactic fingerprints, and/or chemical entities. 
     As used herein, “chemical entities” comprise chemical compounds, substances and non-stoichiometric compounds. 
     Also as used herein, “chemical identifiers” means any schema used to identify a specific chemical entity. For example, chemical formulas, structural formulas, chemical names derived from any chemical nomenclature, or trivial names all can be utilized in the systems and methods herein. In one particular arrangement, the chemical identifiers identify an opioid agonist (e.g. hydrocodone, morphine, hydromorphone, oxycodone, codeine, levorphanol, meperidine, methadone, oxymorphone, buprenorphine, fentanyl and derivatives thereof, dipipanone, heroin, tramadol, etorphine, dihydroetorphine, butorphanol, levorphanol). In a further arrangement, the chemical identifier identifies molecules that interact with specific G-protein coupled receptors, tyrosine kinase linked receptors, guanylate-cyclase linked receptors, nuclear steroid receptors, membrane bound steroid receptors, ligand-gated ion channel receptors or adhesion molecules. 
     As used herein, a “coded form” is a multivariable data representation of a particular set of information relating to the structural, sequential, physical and/or binding properties of a chemical entity represented by a chemical identifier. By coding such properties, an assessment of the similarities that exist among and between different chemical identifiers can be made, including automated assessments. Furthermore, as used herein, a “coded form” can further represent a multivariable data representation of a particular set of information relating to the structural, sequential, physical and/or binding properties of a biologic product, such as a particular nucleotide or protein sequence that corresponds to a particular biological function (e.g. protease inhibitor), purpose or utility. 
     In part, the present invention concerns generating datasets which associate the extracted chemical identifiers, the coded forms corresponding to these extracted identifiers, and links to the originating source documents. By maintaining an association between these datasets, systems and methods in accordance with embodiments of the present invention can derive relationships between the datasets based on the chemical identifiers, rather than in view of their coded forms. These relationships enhance the principal function of generating potential new chemical entities by managing and utilizing source document data based on the underlying relationships between data extracted from the source documents. 
     Discussion of System Arrangement 
     In one embodiment, the computer system  100  is illustrated in  FIG. 1  and includes a computer (not shown) which has a hardware processor  102  configured to access a database  104  of stored source documents. Each stored source document contains at least information relating to a particular subject matter. In one instance the subject matter is a biological target of interest (e.g., sodium channel inhibitors,), and information describing chemical structures, formulae, antigens, amino acid sequences, protein sequences or nucleotide sequences used to interact with, or related to, the biological target. 
     A search performed in a conventional manner on the database  104 , including possibly several databases of documents, yields a universe of documents that relate in one manner or another to the biological target of interest. 
     In a particular embodiment of the present system, the source documents are published patent documents, including patent applications and patents, available through the United States Patent and Trademark Office, optionally from foreign patent offices and from various commercial patent databases. Other collections of non-patent documents are suitable for use with the system and method, such as, by way of example and not limitation, technical and scientific journals, research compendiums, and other documents containing information relating to chemical compounds, any or all of which can be included in the database  104 . Particular advantages result, however, when the source documents include published patent documents because one effect of the predictive engine described herein is the potential to identify novel and inventive chemical or biologic formula, sequences or structures, including ones not documented in the patent literature in connection with a particular biological target. 
     As illustrated in the high-level block diagram of  FIG. 1 , the processor  102  is configured by code stored in its memory  110  to extract data from the source document database  104  and generate a collection of representational data objects that preserves the relationship between the representational data and the source document. While the present discussion is in relation to the processor  102  and the memory  110 , the processor can include multiple cores, or can be embodied as a plurality of processors, each being provided with code from a respective memory, as may be implemented in a distributed computer implementation of the invention. 
     In one arrangement, representational data objects are amino acid sequences. In an alternative embodiment, the representational data are chemical entity identifiers. However, for ease of discussion, the following example will use chemical identifiers to illustrate the implementation of the described embodiments. 
     Thus, for example, chemical entity data objects can be stored in a representational data object database  106 . When evaluating chemical compounds, the representational data object database is a chemical entity data object database. Alternatively, when evaluating biologic entities or identifiers, the representational data object database  106  is a biologic data object database. In an alternative context the representational data object database is a textual data object database. In one embodiment, the processor  102  executes software modules stored in the memory  110  which configure the processor to access the database and generate predictive or analytic outputs based on the contents of the chemical entity data object database  106  and based upon algorithmic logic discussed in this specification. Through the use of code modules stored in the memory  110 , the processor  102  can provide a visualization via a visualization system  108  of a virtual target landscape which is constructed and exists in the computer implementation in order to present locations in the landscape at which new or predicted chemical entities (NCEs) are predicted to reside. Such NCEs are not described within the universe of source documents that gave rise to the virtual landscape for the particular biological target of interest, and only a portion of potential NCEs would be of interest, such as those NCEs that occupy prescribed placements or locations within the constructed landscape. Based on a selection of specific chemical entities from among the entities in the representational data object database  106 , the modules configure the processor with code that executes therein to generate or “propose” new chemical not currently described in the source document database or the representational data object database  106 , but which are similar to a particular selection as a function of location within the virtual landscape (e.g., a visualization presented in a low-dimensional node array). 
     In an arrangement based on the selection of specific biologic entities from among the biologic entities in the database  106 , the modules configure the processor with code that executes therein to generate or “propose” new biologic entities not currently described in the source document database or the biologic entity object database, but which are similar to a particular selection as a function of location within the virtual landscape (e.g., a visualization presented in a low-dimensional node array). 
     As used herein, “similar” is meant to describe chemical having substantial overlap in chemical structure, sequences, domains, features and physical properties. The selection and generation can be made by the user alone, such as by interaction with the virtual landscape to guide further processing to identify new representational data with a particular placement within the landscape, programmatically, or through a combination of the two according to a pre-defined rule set or instructions. In one embodiment, as will be described in more detail below, a user can review a first visualization of chemical entities discussed in a set of selected source documents, namely, source documents that relate to a certain biological target, and select one or more chemical entities identified from that set of documents for further analysis. A range of similarities that exist between the predicted chemical forms and the chemical forms that have been selected in this way can be displayed to a user through the embodiment of the visualization system  108 . Alternatively, the ranges of similarities as determined by the system are presented in a different manner Regardless of the approach taken, the predicted and selected chemical forms can be stored in a storage device for future access or reference. 
     The processor  102  is configured to perform a series of discrete steps to access, analyze and generate outputs relating to the data in the representational data object database  106  as described. As will be apparent from the accompanying discussion of methods in accordance with aspects of the invention, prediction and identification of new chemical entities, or any other representational data, is performed in regard to a virtual landscape defined by a particular algorithmic approach and the identification includes fitting the newly identified chemical entity or other representational data into that landscape, regardless of whether there is a visualization of the landscape or not. 
     Discussion of the Principal Modules and Certain Methodologies 
       FIG. 2A  detail particular work-flows in accordance with aspects of the invention, in which the subject matter of interest is a biological target and in which new chemical entities are to be located. When the discussion permits, additional examples are included. Likewise,  FIG. 2B  details the same workflow as in  FIG. 2A  but is directed to locating new biologic entities. 
     The steps shown in  FIGS. 2A-B  can be carried out by code executing within the memory of the processor  102 , as may be organized into one or more modules, or can comprise firmware or hard-wired circuitry. For simplicity of discussion, the code is described in the form of modules that are executed within the processor  102  and which are each organized to configure the processor  102  to perform specific functions. The block diagram of  FIG. 3  provides an exemplary description of the modules that cooperate with the memory  110  and processor  102  to implement the steps outlined in  FIGS. 2A-B , and are shown for ease of illustration as all being associated with a single processor  102  and memory  110 . As noted previously, the processor  102  can comprise a plurality of cores or discrete processors, each with a respective memory, which collectively implement the functionality described below, together with associated communication of data therebetween. 
     With reference now to  FIGS. 2A-B  and  3 , the prediction and new chemical entity generation system is initiated and implemented by at least one search module  310  which comprises code executing in the processor  102  to access and search the records in the source document database  104  according to step  210 . 
     U.S. patent application Ser. No. 14/795,375, entitled “Chemical Formula Extrapolation And Query Building To Identify Source Documents Referencing Relevant Chemical Formula Moieties” naming inventors Kevin Brown and Kevin Brogle, which is hereby incorporated by reference as if set forth in its entirety herein, describes a system and method that can be used for constructing suitable queries. In brief, a set of specific representational identifiers that are represented or covered by a generic representational identifier found in, say, a target document, can be extrapolated and queries can be constructed and performed on a corpus of source documents for purposes of comparison of the members of the extrapolated set of specific representational identifiers to a database of known representational data. By matching known representational data in this way, any overlap between the generic representational data and specific instances of the generic representational identifier within the source documents is determined, and in specific implementations, the system and method reduces the scope of the generic representational identifier such that the reduced scope generic representational identifier encompasses only novel specific representational identifiers. 
     The database search step  210  executes to retrieve documents that discuss a subject matter of interest, such as a biological target of interest, from among the source documents. The records that reference the target of interest can be located, for instance, using text searching of the source documents or searching of an index of the source documents. As will be appreciated, the source document database  104  can comprise a single repository of records or can comprise an aggregation of data stores. In one example, the system is configured to connect through the Internet to a remote document database. In this embodiment, the system is equipped with modules capable of configuring the processor to query remote databases and parse the results. In one embodiment a network interface card (NIC) is configured to communicate with the processor  102  in order to establish a connection to an external network. In another embodiment, a wireless adapter is used to communicate with the processor  102 . 
     In a further example, the search module  310  includes code that executes so as to configure the processor  102  to search the applicable database(s) with defined search parameters such as a particular biological target of interest. Additionally, the search module  310  can include further code, as part of a single module or which may comprise sub-modules, which configure the processor  102  to return only those search results that match specific criteria. One search criterion can be the presence of chemical formulas or structures suitable for conversion into coded forms. A non-exhaustive list of search parameters that can augment or be run in addition to a search concerning a biological target of interest or other subject matter of interest includes: a publication date, inventor name, assignee name, country of filing, language, and other parameters typically included on a cover page of a printed patent, published patent application, or in a conventional patent document database. 
     In a further embodiment, the results from the database query of step  210  can be filtered using a filtering module  312  which can comprise code executing in the processor  102  in order to perform a filtering step  212 . The filtering module  312  in one particular embodiment configures the processor to only access those source documents which include in their discussion a particular sub-set of the biological target of interest. In  FIG. 2A , the filtering module  312  can configure the processor to execute a filtering step in which the results of a database query are filtered to identify those documents which relate to sodium channel inhibitors or other small molecule compounds. In  FIG. 2B  the filtering module  312  can configure the processor to identify those documents relating to an immune-mediated inflammatory disease. The results of this step and other steps can be managed within a memory of the computer, with data moved in and out of a non-transitory memory or stored elsewhere, as required. 
     In one embodiment, the results of the filtering step  212  are stored in the chemical or biologic entity data object database  106 . In an alternative embodiment, the search module  310  configures the processor  102  to store the results of the query in a non-transitory memory or an external, non-volatile storage device, either of which is accessible to the processor  102 . 
     Once the data from the source documents is stored in a storage location, it is made available to the processor  102  for analysis. In one embodiment of the system, the analysis of the data includes the use of an extraction module  320 . The extraction module  320  can comprise code (more generally, “software”) that configures the processor to perform an extraction step  220 . The extraction step  220  causes the processor to obtain, from each source document, at least one specific chemical found in the source document. For instance, the extraction module can perform a text parsing function that identifies candidates for extraction with reference to a rules base. For example, the rules base can instruct the text parsing function to parse prefixes, subscript and superscript components of a chemical name according to a pre-determined nomenclature schema. 
     In a particular arrangement, the extraction module is configured to extract alpha-blockers, beta-blockers, calcium and other ion channel inhibitors, opioids, and combinations or variants thereof. For example, the extraction module  320  is configured to extract from a source document one or more “true” alkaloids (e.g. atropine, nicotine, and morphine), alkaloids containing terpene (e.g., evonine) or peptide fragments (e.g. ergotamine) coniine and coniceine, protoalkaloids (e.g. mescaline, adrenaline and ephedrine), polyamine alkaloids, peptide and cyclopeptide alkaloids and pseudalkaloids. 
     In an alternative arrangement, the extraction module implements a natural language extraction and association algorithm, comprising code executing in the processor, to extract data from the text of the document. In this arrangement, the extraction module utilizes a dictionary of weighted subject matter terms and tokens to extract information from the text of the source documents and convert that information into a computationally useful format. For example, terms commonly used in the collection of patent documents are provided with relevancy weight, such that any extraction will provide discounted values related to the presence of terms commonly found across the collection of source documents. In one embodiment, this relevancy weight is determined by calculating the frequency or uniformity of occurrence of each term in the document or within a collection of documents, or in a larger corpus of text, by assigning weighted values to each term within the document, depending on the frequency of that term or token within the corpus or collection of corpuses selected. For example, common stop words and words common to the subject matter are given a low relevance score. In one embodiment, the relevancy scores are a binary score. In another embodiment the relevancy scores are established relative to a defined relevancy range. In this way a textual fingerprint, such as a numerical or data structure representing the underlying core concepts of the corpus, is generated using the weighted values. In this context, common terms will not be used, or will have reduced relevancy, when generating a numeric representation of the textual elements of a source document that describes the subject matter contained therein. Likewise, terms that have specific technical meanings are given higher weight as they are more likely to describe the specific subject matter of the source document. Thus, collections of terms representing the subject matter of, e.g., each patent document, are generated with each term having an associated value. In a further implementation, the terms are compared to a library of generic features or concepts found within the subject matter, and scored based on the relevance, rarity and/or specificity of the terms found within each source document. These values are then used to convert the terms into a numeric representation of the subject matter of the source documents such that it can be placed within an n-dimensional manifold. 
     In a further alternative embodiment, the extraction module implements a biologics extraction algorithm, comprising code executing in the processor, to extract biologic representational data (e.g. DNA, RNA, amino acid sequences) from the document. In this arrangement, the extraction module identifies the representational data sequences and extracts those sequences as data. In this instance, the extraction algorithm extracts adenine, cytosine, guanine, thymine, and uracil base identifiers provided in a sequence from a document. For example, sequence listings commonly used in patent documents are extracted and associated with the bibliographic data representing the source patent. These sequences are then converted into a numeric representation of the representational data such that it occupies a space within an n-dimensional manifold. In another arrangement the data extracted is data relating to antibodies and antigen binding fragments thereof including antibodies from various animals, humanized, chimeric, polyclonal, monoclonal, bi-specific, multi-specific, single chain antibodies, Fab fragments, F(ab′)2 fragments, disulfide-linked Fvs, fragments containing a VL or VH domain or a complementary determining region (CDR), wherein the antibody or antibody fragment immunospecifically binds to a peptide, polypeptide or protein that is described in a collection of source documents. 
     By way of example, the extracted biologic identifiers found in the results of step  220  are stored in a database as individual biologic data object (BDO) entries. These entries include the biological identifier (e.g. a sequence of amino acids) and bibliographic data indicating the source document. Depending on the format of the data stored in the database, the biological identifiers can be directly mapped to an n-dimensional space, or first converted into a coded or numerical form as in step  230 . 
     The extraction module  320  can be arranged to include sub-modules that transform the text and the bibliographic information into a data object or record. In particular, the extraction module or its sub-modules can include code that configures the data object to populate fields included in the data object&#39;s definition with data elements unique to the source document, such as the patent or application number, the name of the inventors, assignment status, date of filing and other bibliographic data. Alternatively, the data object can comprise a database entry, a record, a linked list, and so on, all of which can enable the operations described below in regard to data objects. 
     Optionally, the extraction module  320  can further comprise code that configures the processor to implement a secondary filtering step  222 . For instance, the second filtering can comprise code that eliminates data objects that concern salts, crystalline or amorphous forms and other duplicative or similar entries of a particular chemical entity. Upon completion of the extraction and filtering steps  220 - 222 , the process stores the filtered results in the chemical entity data object database  106  or elsewhere, as noted above. In an alternative configuration where the biologic representational data is sequence data, the secondary filtering step  222  is configured to eliminate data objects that concern duplicative entries, specific sequences, sequences motifs, and structural motifs. 
     In order to utilize the data stored in the chemical entity data object database  106 , the processor operates on the text in the source documents to convert the text that the extraction module identifies as relating to a given chemical identifier into a coded form suitable for further processing. In one example, the coded form is a numeric value (e.g., a keyset) representing the structural, physical and/or binding properties of a given chemical compound. 
     Likewise, in configurations where the processor is configured to extract and analyze biologic representational data stored in as biologic entity data object database  106 , the processor operates on the text in the source documents to convert the text that the extraction module identifies as relating to biologic data (such as nucleotide or protein sequences) into a coded form suitable for further processing. In one example, the coded form is a numeric value (e.g., a keyset) representing the structural, physical and/or binding properties of a given biologic product represented by a particular biologic identifier or sequence. 
     In one embodiment, a conversion module  330 , which can comprise code executing in the processor, configures the processor  102  to convert each biologic representational identifier into a coded form according to a conversion step  230 . 
     In one particular implementation of step  230 , biologic representational data extracted and stored in the database  108  are aligned with one another. As an example, the conversion step  230  includes a sparse binary conversion algorithm to convert the biologic identifier stored in a BDO into a numerical representation. Alternatively, affinity-fingerprint algorithms or feature-tree algorithms, or other algorithms can be implemented by the processor  102  in order to obtain coded forms of the biologics identifier. In another arrangement, the sequences are converted using a multi-bit keyset into a multidimensional identifier. For instance, each potential value for a position in a sequence is given a multi-bit value and the multi-bit values taken collectively, represent the sequence. In an alternative arrangement, the BDOs are converted using a substitution matrix employing PAM, PET91, BLOSUM algorithms to generate a specific numerical identifier for each amino acid in a sequence. The generated numerical sequences are compared to one another to determine similarity of the sequences. 
     By way of example only, as show in particular detail in  FIG. 2C , in one or more implementations and processor is configured with an alignment module to align nucleotide or protein sequences obtained from steps  220 - 222 . In a particular implementation, the processor is configured to access one or more known alignment sequence alignment algorithms to align the sequences. For example, and in no way limiting, the processor is configured to select a particular alignment sequence based on the characteristics of the biologic representational data accessed in steps  220 - 222 . For example, the alignment sequence selected is based on the type of sequence evaluated (nucleotide, protein, etc.) or the number of sequences to be aligned, or some combination thereof. In one or more implementations the sequence alignment selected is selected from commonly known and understood alignment sequence algorithms, such as but not limited to, ClustalW, ClustalOmega, MUSCLE, etc. Here, as shown in sub-step  225 , the processor utilizes the sequence alignment algorithm to produce a multiple sequence alignment of all or some of the biologic representational data obtained in steps  220 - 222 . 
     Once the sequences have been aligned, as provided in sub-step  225 , the process is configured to convert the elements (such as amino acids) in the aligned sequences into a numerical representation, such as BDO. For example, as provided in step  227 , the processor is configured to access a conversion array to covert the amino acids into the numerical format. In one particular implementation, the conversion array, is generated by utilizing a substitution matrix. In one instance, the substitution matrix is selected from one or more matrices that are designed to describe the rate at which elements within a representational sequence changes or describe the log odds of finding two specific elements aligned. In one or more particular implementations, the substitution matrix is a BLOSUM or PAM substitution matrix or variations thereof. In one or more implementations, the conversion array can be created by reducing the dimensionality of a selected substitution array using a dimensionality reduction algorithm. For example, a processor is configured to generate an amino acid conversion array by reducing the dimensionality from 2D to 1D using a dimensionality reduction algorithm such as classical (metric) multidimensional scaling or Kruskal&#39;s non-metric multidimensional scaling. However, it should be appreciated that in one or more further configurations, alternative approach would be to use multiple, different, substitution matrices and to reduce the 3rd order tensor to a 1D array either directly or stepwise via a dimensionality reduction algorithm. 
     In one particular implementation, the AA conversion array is used to convert the amino acids in the aligned biologic identifiers from a string representation to a numerical representation as BDOs as in step  227 . However, it should be appreciated that the amino acid (AA) conversion array process does not provide a value for a ‘gap’ in a sequence in the alignment. Thus, in a further implementation, the processor is configured to assign gap values. For example, the processor is configured as part of the conversion process of step  227  to provide the median of the array values or the average of the array values as the gap value. It should be further appreciated that the gap value could be selected based on values that can be correlated to the median of the array values, as such a value would be representative of all amino acids. However, the inventors have discovered that the average of the array values, as opposed to the median value, resulted in better, more logical, positioning in the plotting module. Such a non-routine and unexpected results enhance the accuracy of operation of the n-dimensional array described herein. 
     In an alternative configuration, the processor is instructed by one or more conversion modules to generate an identity matrix for the aligned sequences. This identity matrix can be used as the coded form of the biologic representational data and is provided to the plotting module for mapping as provided herein. 
     In one embodiment, a conversion module  330 , which can comprise code executing in the processor, configures the processor  102  to convert each chemical identifier into a coded form according to a conversion step  230 . 
     In yet a further embodiment, the conversion module utilizes image recognition sub-modules to obtain chemical names or formulae from a given structural formula, such as a skeletal formula. For example, the conversion module  330  configures the processor to compare a structural formula under investigation to a plurality of known structural formulas, each associated with a specific chemical formula or chemical name, and to identify the chemical formula based on a match within a prescribed criterion(ia) between the two. 
     In an alternative configuration, the conversion module  330  comprises code executing to configure the processor  102  to compare peptides, polypeptides, nucleotide, protein sequences, or any fragments, domains, or regions relating thereto. 
     In a further embodiment, the conversion module  330  can configure the processor to convert the chemical identifier of each chemical entity data object (CEDO) into coded forms and store the converted forms in a memory or other storage location while preserving the association between the CEDO and the coded form. In one embodiment, the conversion step  230  includes the embodiment of a MDL 960-bit SS-keyset numerical conversion algorithm, produced by MDL Information Systems, in order to convert the identifier into a numerical representation. Alternatively, other keysets such as, for example, those based on affinity-fingerprint algorithms or feature-tree algorithms, or the 881 bit structural keys used by PubChem, or 1- and 2-dimensional molecular descriptors can be implemented by the processor  102  in order to obtain coded forms of chemical identifiers. 
     As shown in  FIG. 2B , a biological target is selected for analysis and evaluation. In the present context, the biological target of interest is a disease or disorder. For instance, the biological target is selected from any type of cancer e.g., leukemia and lymphoma, carcinoma, sarcoma, blastoma, or germ cell tumor. In another embodiment, the biological target is an autoimmune disorder. In a further arrangement, the biological target is a disorder of the skin, heart, lung, liver, bone, brain, stomach, colon, breast, prostate, bladder, kidney, pancreas, ovary, and/or uterus, lymphatic or nervous systems. 
     In an alternative configuration, the biological target is an antigen, or a specific class thereof, e.g. Tumor Necrosis Factor (TNF). For example, the search step  210  yields all of the documents within the document database that describe TNF inhibitors. 
     In a more detailed example using TNF, the search results contain references to biological identifiers or entities, such as antibodies from various animals, humanized, chimeric, polyclonal, monoclonal, bi-specific, multi-specific, single chain antibodies, Fab fragments, F(ab′)2 fragments, disulfide-linked Fvs, fragments containing a VL or VH domain or a complementary determining region (CDR), wherein the antibody or antibody fragment immunospecifically binds to a peptide, polypeptide or protein or sequences describing the same. 
     Regardless of the conversion metric applied, the converted numerical forms are associated with the source biological identifier and are plotted to n-dimensional space according to steps  240 - 250 , as discussed previously in connection with  FIG. 2A . The distances between and among the plotted numerical forms provides a basis for a processor, executing code, to make a comparison and resulting similarity determination among the sequences, such as by calculating the smallest distance within the virtual n-dimensional space. 
     Once the numerical forms or other coded form conducive for similarity determinations have been obtained by implementing step  230 , the coded forms are evaluated for their similarity to one another. In one embodiment of the system and method, a plotting module  340  is used to configure the processor  102  to conduct a similarity analysis on the plurality of numerical forms obtained and stored in the previous steps, as described herein. In one embodiment, the plotting module  340  comprises code that configures the processor to plot each of the CEDOs, as noted at step  240 . The plotting module  340  can include code that executes so as to configure the processor  102  to plot the numerical forms to an n-dimensional, preferably low-dimensional space, such as a 2-dimensional or 3-dimensional space. That code can implement a dimensionality reduction algorithm, such as a self-organizing map algorithm or other form of neural network/machine learning algorithm. 
     Discussion of Chemical Entity Data Object Examples 
     The following discussion uses CEDOs as an example of the functioning of the system and method provided. However, it will be appreciated by those possessing the requisite level of skill in the art that BDOs or TDOs can be substituted for CEDOs when used in conjunction with corresponding databases  106 , according to the following steps. 
     As used herein, neural networks are machine learning systems used to derive rule bases for evaluating unclassified data using pre-classified or “training” datasets. These rule bases are instructions that configure a data analysis agent, such as a processor, to classify new data passed to the system. Furthermore, the rule base is configurable such that the rule base itself is updatable, extensible or modifiable in response to new unclassified data. In the embodiment provided, the CEDOs are used both as the training data and the unclassified data. 
     In the illustrated embodiment, the plotting module  340  configures the processor  102  to generate an n-dimensional space as the landscape and seed it with placeholder values, as noted at step  242 . The placeholder values in this example are selected to cover the range of potential numerical values for the converted coded (e.g., numerical) forms of the CEDOs. In a particular embodiment, the plotting module  340  includes code to further configure the processor to insert each CEDO at a location in the n-dimensional space, such as according to step  250 . In the illustrated example, the particular location for the insertion operation is a function of the degree of similarity that the coded form shares with the placeholder data or to other coded forms previously placed in the n-dimensional space. Here, the coded forms are used to plot the CEDOs to a given coordinate location in the n-dimensional space according to the similarity of the coded forms of each of the CEDOs to one another and to the placeholder values. It should be understood, however, that one embodiment of the invention utilizes the plot coordinates to compute the degree of similarity without actually plotting the CEDOs to an output device. 
     Discussion of Plotting and Placement Module Example 
       FIG. 4  provides a detailed view of the subsidiary steps that can be performed as part of the operation of the plotting module  340  when the dimensionality reduction algorithm comprises a self-organizing map. The steps implemented therein as  410 - 450  are provided for illustrative purposes and are not restrictive, and may not be representative of the steps that would be utilized in order to implement a different algorithm. 
     In this particular embodiment, the plotting module  340  configures the processor to execute code in order to compute a degree of similarity (determined by calculating the Euclidean distance between the coded form and a weighted vector placeholder value) and placement of CEDOs according to the following self-organizing mapping algorithm: 
         Wv ( s+ 1)= Wv ( s )+Θ( u,v,s )α( s )( D ( t )− Wv ( s ))while  s≤   EQ(1):
 
     In the example equation above, the algorithm is implemented as an iterative calculation. For example, the processor  102  is configured by code to iterate over the collection of CEDOs according to the above equation so long as S (the current iteration) is less than a user or computer provided iteration limit (e.g., λ). 
     In the above equation, Wv represents the current weight vector of node v. Furthermore, v represents the index of the node in the map, while u represents the index of the best matching unit (BMU) in the map. Θ(u, v, s) represents a restraint function value derived from the distance between the BMU and the input vector. Typically, this restraint value is called the neighborhood function and is used to calculate how the weight vector of a node is modified during the course of each iteration. α(s) represents a learning restraint due to iteration progress. Collectively, the CEDOs operate as data vectors and as such the entire CEDO collection is regarded as input data set D and the particular CEDO under analysis operates as D(t), where t operates as the index value of the target input. 
     In the process steps of  FIG. 2 , the processor is configured by the plotting module or its respective sub-modules, to generate a landscape in the form of a two dimensional virtual node array, as indicated at step  410  of  FIG. 4 . The node generation step can operate to produce a two-dimensional node. However, the sub-modules are configurable to generate multi-dimensional nodes, such as, for example, a three-dimensional node. In this particular embodiment, the nodes of the array are pre-seeded with weight vectors. The weight vectors, in one embodiment, are assigned random variables within the range of possible values based on the data set. 
     The plotting module  340  can further comprise code that configures the processor  102  to implement a CEDO selection process, as indicated at step  420 . In this step, the processor selects a CEDO from the chemical object database and assigns the selected CEDO to a given coordinate location within the virtual node array. In one example, the CEDO is placed in the virtual node array according to the numerical form of the chemical identifier unique to that CEDO, in a virtual location defined by the self-organizing map, for example. 
     Optionally, the plotting module  340  further configures the processor to implement a similarity/identity analysis using algorithm EQ1 (above), as indicated at step  430 . Thus, in one embodiment, the processor is configured to compare the input vector for a given CEDO (coded form) and the weight vectors. For example, the processor is configured to calculate the distance between the input vector and the weight vector, as indicated at step  430 , where the distance between the input vector of a CEDO and the weight vector of the map&#39;s node is related to the degree of similarity between the weight vector and the input vector values. In a particular embodiment, the distance formula is a Euclidean distance formula. In a further embodiment, the processor  102  determines which node in the map provided by the virtual node array produces the smallest distance between a given CEDO and the weight vector of any node (i.e. a “best matching unit,” or BMU). 
     The plotting module  340  further includes code executing within the processor in order to implement a placement step  440 . The processor  102  is configured to place selected CEDOs, according to the input vector, into a virtual node having a weighted vector with the closest similarity. Once an initial placement occurs, the placement step can subsequently adjust the weight vector value of each node in the array depending on the current placement, as indicated at step  450 . In one embodiment, the processor configured to make the adjustment of step  450  using code executing therein to update the placement of the CEDO in the map nodes that are in the neighborhood of the BMU, including the placement of the BMU itself. In a further embodiment, this is accomplished by adjusting the node weights stored in the processor memory relative to the recently added CEDO. In this way the CEDOs placement in a particular node of the virtual array is revaluated based on the BMU value and each input vector value. 
     The “neighborhood” as described herein, defines a set of neighboring nodes characterized by certain parameters such as distance from a BMU (best matching unit) and a shape of the neighborhood function. In a further embodiment, “neighborhood” references the maximum distance that an input vector can be moved while still remaining within a particular node. 
     Once a CEDO has been placed according to the adjustment step  450 , the process iterates from step  420  to  450  with each new CEDO. This iterative process is commonly referred to as “training” or “seeding” the node map. 
     In some embodiments this map seeding subsequence is iterated several times for each CEDO in order to properly adjust the node weights for all of the CEDOS that have been included into the map so far. 
     Once all of the CEDOs have been seeded into the map, a final placement step  460  is implemented by a processor  102  configured to execute a final placement sub-module of the plotting module  340 . In this embodiment, each of the CEDOs is finally placed at a given coordinate location within the virtual node array according to the input vectors and the weight vector of the nodes. 
     Placement includes the intermediate step of assigning the coordinate locations according to the input vectors and the weight vectors of the nodes and the step of plotting the CEDO at the given coordinate location on an output device (e.g., display, printed report or data file). 
     The self-organizing mapping functions that have been detailed at steps  410 - 450  comprise one non-limiting embodiment of a dimensionality reduction algorithm. Other dimensionality reduction algorithms using neural networks or other analytic techniques are also useful in converting high dimensional datasets to low dimensional datasets. Examples of such techniques, such as feature extraction algorithms, and feature section algorithms are useful for organizing and visualizing the data according to the present system and methods. 
     In a further embodiment, a combined latent class and trait model, as described in Ata Koban, A combined Latent Class and Trait Model for the Analysis and Visualization of Discrete Data, 23 IEEE Trans. Pattern Anal. Mach. Intell. 859 (2001), incorporated by reference herein as if fully set forth in its entirety herein, is used to analyze and evaluate the CEDO data obtained from the source document. In this embodiment, the latent class distribution can be represented using the Koban equation as taught in the above journal article, as: 
         p ( c )=Σ k=1   K   δ(c−c     k     )P(c=c     k     )  
 
       Where Σ k=1   K   P ( c=c   k )=1  EQ2:
 
     In the provided equation, the L-dimension variables c can be considered as a uniform sampling from the corners of a K-dimensional hypercube, with  6  being a distribution function. Furthermore, the latent dimension is K and one value of c is denoted by ck. 
     This latent class model can be combined with a latent trait model represented by a 2-dimensional grid of points X, where X=M×K and M=2 (e.g., 2-dimensionl) and this model is mapped by a set of L nonlinear and linear basis vectors Φl such that 
         C =ϕ( X )  EQ2:
 
     Where C is an L×K dimensional matrix. 
     The CEDOs are evaluated according to the above equations and mapped to a 2-dimensional grid based on the relationship of each individual CEDO to one another. 
     Other placement algorithms, including without limitation, smallest distance metric algorithms, can likewise be utilized by the systems and methods described herein. 
     Discussion of Visualization Module Example 
     Once the data objects that represent the unique numerical forms, or other coded form of the chemical identifiers have been plotted to the n-dimensional virtual space, the results of the plotting module  340  can be presented to a user through data visualization. In one embodiment, a visualization module  360 , operating as code executing in the processor  102 , configures the processor to generate visualizations of the data plotted according to the plotting module  340 . In another embodiment, the plot coordinates are stored without use of a visualization engine. 
     For example, the processor  102  can be configured to implement step  260  in order to provide a user with a visual display of the CEDOs based on the similarity of the input vectors. Depending on user input and selection concerning what is to be depicted within the virtual node array, the visualization presented to the user can provide markers which represent each CEDO stored in the chemical entity data object database  106  (or elsewhere) and the corresponding placement of that CEDO within the virtual n-dimensional space. Visualization modules suitable for use in embodiments of the invention can include a variety of commercially available visualization systems  108 . One such example is the Spotfire product of Tibco Inc., Palo Alto, Calif. Alternatively, the visualization module can be constructed as described herein for NCE visualization purposes. 
       FIG. 5A  depicts one non-limiting type of visualization of a collection of CEDOs for a given biological target. The data is arranged as a 2-dimensional array, where each individual CEDO has been plotted based on the similarity of the CEDOs to one another and to the weighted value of the node. In the illustrated example, the visualization module  360  provides a 2-dimensional grid on a display  400  or other output of the computer. However, in alternative embodiments, the visualization module  360  may display higher dimension visualizations. What can be appreciated, more generally, is that the placement of a given CEDO within the virtual node array is a function of the training that the array undergoes as each CEDO is added. As such, the similarity comparisons of chemical features, as described below, is a function of the virtual distances which owe their values to the placement and repositioning of CEDOs during training as the node-array is populated. 
     In one non-limiting embodiment, the visualization data presented to the user includes bibliographic data relating to the source document and the linked chemical identifier. In the illustrated data visualization ( FIG. 5A ), each marker  402  represents a different chemical entity. Likewise, a color of each marker  402  represents a different assignee for the originating source patent document. The shape of each marker, such as the triangle, corresponds to a single, common source document for each assignee. More generally, the markers are specified by rules stored in a memory  110  which are used by the visualization module  360  to influence output by the system to a display screen, printer or other such device. 
     In another implementation, the visualization data presented to the user includes additional content information obtained from external content sources. For example, the visualization module includes an external content sub-module or associated module that configures the processor to obtain external content relating to the CEDO, or the patent document that is the source of a particular CEDO. For example, the external content sub-module is configured by code executing in the processor to identify external content relating to the CEDO or its source document. Such sources can include legal judgment databases, social media networks, regulatory (e.g. FDA, SEC) databases, scientific and technical journals, sales and marketing databases, and business development resources, license agreement records. These external content sources provide primary or secondary identifiers that can be applied to the CEDO. In one instance, a CEDO is marked with a particular color representing ownership status, but is also marked with a particular symbol indicating that it has been licensed to a third party. In this way multi-dimensional information is conveyed in the visualization. 
     As shown  FIG. 5A , markers  402  are clustered to particular coordinate space locations within the provided 2-dimensional space. In the illustrated embodiment, each cluster of markers  405  represents a collection of markers having a similar chemical identifier, such as can be determined by a self-organizing mapping algorithm as discussed above. In the event that no chemical identifiers have a similarity within prescribed criteria to a given coordinate location, the node is rendered in the visualization as an empty node or gap  406 . 
       FIG. 5A  provides a complete view of all the CEDOs obtained as a result of the inquiry in accordance with one embodiment of the invention. In various alternative embodiments, it is useful to organize the data according to the input vector (such as the numerical value representation of the chemical identifier), yet also display the data according to other features of the CEDO. 
     As shown in  FIG. 5B , the visualization module operates to output—to the display  400  or other output device—the original biologic or chemical identifier (e.g., formula or sequence  408 ) rather than the coded form. 
     In the illustrated embodiment, the CEDO having the smallest distance (greatest similarity) to the weighed vector is displayed in the node as the representative member of each cluster. In an alternative embodiment, additional or alternative chemical identifiers or other data elements of the CEDOs can be output by the visualization module for review by a user based on one or more user-selectable criteria, including interaction with individual nodes, data objects, or menus provided by an interface in communication with the visualization module  360 . 
     In an alternative visualization, the marker clusters can be depicted as pie charts. In this embodiment, the relative presence of a desired element of each CEDO is shown. In  FIG. 5C , the segments of the pie chart illustrate the number of CEDOs belonging to a specific source document. 
     In the provided visualization, it should be understood that the space between the clusters of markers  405  is non-linear. Thus, in one embodiment of the system, relative distances between clusters can be represented by the presence, color and/or thickness of grid lines  410 . For example, if grid lines are provided, the darker the grid lines, the greater the distance between each of the clusters  405 . As another example, color coding can be used to represent closeness (red) and separateness (blue) with a spectrum in-between. The visualization module can include code that executes in the processor to support any of these, or other alternative visualization techniques. 
     In a further embodiment, the visualization module  360  comprises code that configures the processor to display CEDO data as a function of time. For example, the visualization module can configure the processor to display a time series of plots, where each of the elements of the series relates to the CEDOs from a common assignee at a given period in time. In this arrangement, the visualization module is configured by code executing in the processor to produce time-series animations based upon, among other features, the publication, issue, grant, or license of the underlying source documents linked to the CEDOs. 
     In a still further embodiment, the visualization module  360  further includes a statistical analysis sub-module. This sub-module configures the processor, in one implementation, to cooperate with the time-based data to produce predictions and evaluations of the data. For example, the statistical analysis sub-module comprises code executing in the processor to configure the processor to evaluate collections of time-series data across the collection of CEDOs and generate predictive models of how and how many source documents are generated over time that relate to particular biological targets and other associated data, or to any other subject matter of interest that is being visualized. 
     In one implementation, the statistical analysis sub-module configures the processor to implement one or more linear classifier algorithms (e.g. Support Vector Machine Algorithm, Naïve Bayes Classifier, unsupervised learning algorithms and/or logistic regression) on data related to the CEDOs. In one implementation, the unsupervised learning algorithm (e.g., the self-organizing map algorithm previously described) is determines, using code that configures the processor, how a portfolio of CEDOs owned by an entity is developed over time, such as by identifying latent traits or parameters that are useful in predicting future development. For example, the processor implements an unsupervised learning algorithm to evaluate the changes in chemical identifiers described in source documents owned by an entity over time and extracts predictive information related to the changes. In another arrangement the processor is configured by code to evaluate the change in the number of nodes occupied by chemical identifiers described in source documents owned by an entity over time and to identify variables or parameters that are statistically linked to the change in the number of nodes. In these manners, predictive models can be generated and utilized by the statistical analysis sub-module. 
     With reference to  FIG. 5D , the n-dimensional space is visualized as a grid  400  containing nucleotide sequences. In an alternative arrangement, amino acid sequences are provided. For clarity, when larger sequences are the subject the analysis, only a portion of the sequence can be shown. In another arrangement only the sequence at a particular location, e.g. complementary determining regions, is shown. 
     The n-dimensional mapping of the biologic information can be used to determine new biologic identifiers not disclosed in the original search results as in step  270 . In one example, a plotted BDO in a first node of the n-dimensional map is compared to a second plotted BDO to determine the similarity of the sequences and to predict a new sequence that shares features of both sequences. In a particular approach, the prediction step  270  uses a common biological feature sub-module (“BF”) that configures the processor to align the sequences for optimal comparison purposes (e.g., gaps can be introduced in the sequence of a first amino acid or nucleic acid sequence for optimal alignment with a second amino acid or nucleic acid sequence). 
     Here, when a position in the first sequence is occupied by the same amino acid residue or nucleotide as the corresponding position in the second sequence, then the molecules are identical at that position. In one arrangement, the percent identity between the two sequences is a function of the number of identical positions shared by the sequences (i.e., % identity=number of identical overlapping positions/total number of positions×100%). The determination of percent identity between two sequences can also be accomplished using a mathematical algorithm. A non-limiting example of a mathematical algorithm utilized for the comparison of two sequences is the algorithm of Karlin and Altschul, 1990, Proc. Natl. Acad. Sci. U.S.A. 87:2264 2268, modified as in Karlin and Altschul, 1993, Proc. Natl. Acad. Sci. U.S.A. 90:5873 5877, which are implemented in various BLAST and derivative programs, each of which is incorporated by reference as if fully set forth in its entirety herein. 
     Upon identification of non-similar portions of the sequence, the prediction module  370  can implement a modification process that replaces, deletes, adds or otherwise modifies either the first node sequence or the second node sequence in order to generate a new sequence not found in the n-dimensional space. 
     For example, the submodule can implement a substitution of amino acids within an amino acid sequence such that amino acid members of the same groups: (1) acidic (negatively charged) amino acids, such as aspartic acid and glutamic acid; (2) basic (positively charged) amino acids, such as arginine, histidine, and lysine; (3) neutral polar amino acids, such as glycine, serine, threonine, cysteine, tyrosine, asparagine, and glutamine; (4) neutral nonpolar (hydrophobic) amino acids, such as alanine, leucine, isoleucine, valine, proline, phenylalanine, tryptophan, and methionine; (5) amino acids having aliphatic side chains, such as glycine, alanine, valine, leucine, and isoleucine; (6) amino acids having aliphatic-hydroxyl side chains, such as serine and threonine; (7) amino acids having amide-containing side chains, such as asparagine and glutamine; (8) amino acids having aromatic side chains, such as phenylalanine, tyrosine, and tryptophan; (9) amino acids having basic side chains, such as lysine, arginine, and histidine; (10) amino acids having sulfur-containing side chains, such as cysteine and methionine; and (11) amino acids having similar geometry and hydrogen bonding patterns, such as aspartic acid, asparagine, glutamic acid and glutamine, may be substituted for one another based on the sequence and the properties of each amino acid. Similar substitutions can be made for nucleotides, or peptides to achieve new biologic identifiers not described in the search results. 
     The resulting newly generated sequence is placed into map and the location noted. In the event that newly generated sequence is placed in the desired location, such as an empty node  406 , the process is complete. However, when the newly generated sequence results in a non-desired placement, the process can precede iteratively until a sequence resulting in the desired placement is generated. 
     Once new sequence is generated, it can be synthesized. In one instance, the synthesis is carried out using hybridoma processes and procedures or other similar techniques. 
     Visualization Module Features 
     The map produced by the visualization module is adjustable based on additional user defined variables. For example, the display of CEDOs on the map can be filtered according to information relating to the source document or bibliographic information. In one arrangement, the map is configured to only display CEDOs from patent owners that have a certain financial position, such as profitability, or negative balance sheets. These additional data features are accessible through the source documents, or through third party databases that provide information corresponding to the inventors, assignees, owners, or licensees of specific or classes of CEDOs. In this regard, the system can link databases other than, say, patent and literature databases, in order to produce a map informed by another type of database, say, a financial database such as those available from Thomson Reuters or Bloomberg, Inc. 
     In a further visualization, the map is configured to display the results representing the analysis of multiple subject areas. For example, the CEDOs originating from searches relating to different subject matters are visualized in a single 2 dimensional map. In this arrangement, the node array will contain CEDOs relating to a number of subject matter targets (e.g. sodium channel inhibitors, potassium channel inhibitors, etc.). 
     Discussion of a Prediction Module 
     In a more particular aspect that can be implemented in certain embodiments of the invention, a predictive analysis is conducted on the organized data. In the illustrated block diagram of  FIG. 3 , a prediction module  370  is implemented after the visualization module  360 . Once the data has been organized by the modules, a predictive analysis can be performed before, or in lieu of, the visualization steps. 
     Referring again to  FIG. 3 , the prediction module  370  can comprise code which executes to configure the processor to predict, as indicated at step  270 , at least one new chemical entity. In embodiments that include the visualization module, the new chemical entities that are predicted can be inserted into the n-dimensional space based on the results of the plotting module  340 , utilizing the iterative insertion as described in step  250 , into a desired location of the plot. For example, the user might select any empty node as the desired location, or might select a sparsely populated node, or might select an adjacent node as a location of interest. Alternatively, in an embodiment of the present invention that uses a non-node based dimensionality reduction technique, a coordinate location that is not currently occupied or that is of particular interest is selected in the continuous 2- or 3-dimensional space. Such selections can be made by interacting with the virtual node array as by manipulating a pointer or otherwise identifying a location within a graphical user interface that is presently displaying the virtual node array, or using a rule-based node selection algorithm. When locations are selected made using rule-based approaches, computational resources are allocated for the subject matter of interest (e.g., new chemical entity discovery) at locations in the landscape associated with rule-based discovery objectives, rule-based constraints on the new subject matter possibilities, or both, which improves the efficiency of the computer and of the downstream clinical investigation of any newly identified subject matter (e.g., new chemical entities) by focusing the machine&#39;s operation. 
     The prediction module  370  can further include code that causes the processor to generate and predict chemical identifiers to add to an established population of representational data (e.g. CEDOs) that have been plotted to a 2-dimensional node map. 
     More generally, the prediction module  370  configures the processor to implement a series of steps. In one non-limiting example, as shown in  FIG. 6 , the steps carried out by the processor configured by the prediction module generate a new chemical entity formula based on the results of the plotting module. The prediction module implements a comparison step  620  to compare the chemical identifier, such as a chemical structure or formula (“CS”) or other chemical identifier of a CEDO located in one node of the virtual array to another CEDO located in a different node of the virtual array. 
     In an arrangement utilizing other representational data, the commonalities present between the coded forms of representational data found in adjacent nodes are used as a basis to generate new representational data not found in the manifold. For instance, the commonalities between nucleotide or protein sequences are used to generate a new nucleotide or protein sequence not described in the source documents, that when converted into the coded form occupies a desired location in the node array. 
     In one specific embodiment, the user selects a specific node as a starting point for the prediction module  370 . In an alternative configuration, the prediction module  370  includes a target sub-module that configures the processor to automatically select a target node for analysis. For example, the target sub-module is configured to select as a target any empty node in the virtual array. Alternatively, the target sub-module configures the processor by code executing in the processor to select as a target any empty node that shares borders with the nodes filed with coded forms of representational data, here CEDOs, having the same bibliographic information. In a further arrangement, the target node is selected based on external content obtained from the external content sub-module. 
     For example, the CEDO locations are virtual array nodes generated by a self-mapping algorithm. In a further embodiment, the first and second nodes share a border with each other or a common third node in the virtual array. In an alternative embodiment, the first and second location nodes instead comprise first and second coordinates points in a virtual n-dimensional space, when the n-dimensional space is generated using a non-node based dimensionality reduction technique to define that landscape. 
     Discussion of Common Feature Identification 
     In a further embodiment, the processor is configured to implement a common CS feature identification, as indicated at step  640 . According to step  640 , the chemical features for the CEDOs of the first and second location in the virtual array are identified. In one embodiment, the processor implements an algorithm configured to extract the number and form of chemical sub-units of which the compound is composed. This can include chemical features corresponding to Murcko derived scaffolds, graphs and molecular frameworks. 
     In an alternative embodiment, an image processing system (“IPS,” not shown) can be used to extract common structural elements between the first and second numerical forms. An IPS can capture a segment of the map (e.g., a node) and characterize the CS based on the image using rules that match the features within the target viewing area (such as ring structures, single and double bonds, and so on). 
       FIG. 7A  illustrates two sample chemical forms sharing a common border with a third, empty node, as determined by a self-organizing mapping algorithm. The chemical forms illustrated, for example, include the types of features that an IPS can be programmed to recognize. As shown in  FIG. 7A , the common structural forms of first chemical structure  701  and second chemical structure  702  are analyzed, such as by an image processing algorithm comprising code executing in the processor  102  to configure the processor to extract the structural features that are in common with both. In this embodiment, the processor identifies common structures (A, B) and non-common structures C, according to a look-up table or a database of known or expected chemical structures, and optionally using rules that govern how the processor is to process the structural forms. 
     Returning to  FIG. 6 , the predictive module is further configured to implement within a processor a new chemical form generation, as indicated at step  660 . In the described embodiment, a new chemical form is generated based on the shared common chemical features between the CEDOs at the first location and the second location of the virtual array. In particular, the new chemical form is generated by replacing sub-units of the common chemical&#39;s structural features. Alternatively, the new chemical form is generated by selecting, augmenting or modifying the non-similar chemical sub-units and combining those units with the commonly identified structural features. In a one particular embodiment, a chemical formula is generated corresponding to the new chemical form. 
     Selecting and obtaining chemical formulas based on the known chemical structures of each CEDO is not limited to image analysis functions. There exist a number of different computational chemistry methodologies, including but not limited to: scaffold-hopping, and other bioisosteric replacement techniques such as fragment replacement, computer assisted organic synthesis methods, Ab initio methods, density functional methods, semi-empirical and empirical methods, molecular mechanics, molecular dynamics methods, any of which can be used to determine the form of the new chemical entity. 
     In a further arrangement, any new chemical forms generated according to the above steps are then subject to a pharmaceutical suitability analysis, such as by evaluating proposed chemical forms using Lipinski&#39;s Rule of Five, or another drug likeness rule to determine if the proposed chemical entity has properties that would make it likely to be orally active in humans. 
     As shown in  FIG. 6 , the processor executing the prediction module is further configured to convert the new chemical entity chemical formula to a coded form according to the conversion step, as indicated at step  670 . In a particular embodiment, the processor converts the chemical form using a specified key-digit solution suitable for use in the dimensionality reduction algorithm being used in that particular embodiment of the invention. The prediction module  370  further configures the processor to implement a placement step  680  to place the numeric form of the new chemical entity in a given location of the n-dimensional space of the virtual node array. Upon placement in the virtual node array, if the numerical form is located in the desired coordinates of the plot, then the processor associates the new chemical form with a unique visual marker and updates the visualization. Again, the “desired coordinates” could be those coordinates which are between the first and second virtual nodes, within the first or second virtual node, or within a third node in the virtual array which shares a border with the first and second virtual nodes. 
     Alternatively, if the newly formed chemical entity does not result in placement in the desired coordinate space, then the processor can be configured by further code, such as in an iterative sub-module, to generate new chemical entities. This iterative process is controlled by the processor and is configurable to continue generating new chemical entities until one of the entities, when converted into a coded format and inserted into the node, results in the desired placement has been generated, or, alternatively, until a pre-set time limit, or number of attempts has been met or exceeded. In a further embodiment, each newly generated coded form that fails to have the desired placement in the virtual node array is stored in a memory storage location for later retrieval and use by the system. 
     In a further aspect, the prediction module can configure the processor to implement a verification step  690 . In an embodiment that includes this step, the processor executes instructions in order to evaluate the probability that each chemical identifier of each newly generated chemical entity is able to be synthesized. In a particular embodiment, the verification step  690  compares the chemical formula of the new chemical entity to a database of known chemical structure, structural interactions and/or chemical reactions and formulae. According to this embodiment, each new chemical identifier is evaluated for the probability of successful synthesis, e.g., whether it is above a pre-set threshold. The synthesis probability is provided by the processor to the user as part of the visualization update. The probability evaluation can utilize stochastic algorithms to identify subsets of NCEs that are more likely to be synthesizable. In one embodiment, only synthesis probabilities above the pre-set threshold are provided as part of the visualization update. 
     The verification as to whether a predicted chemical entity can be synthesized can be informed by chemical synthesis machines, such as the Revblocks™ platform being developed or offered by Revolution Medicines of Redwood City, Calif. Platforms such as this are said to synthesize original compounds. Turning to  FIG. 7B , a detailed example of the predictive portion of the system described is in connection with a flow diagram. Step  704  follows the generation of new chemical formulae for insertion into the node map based on chemical formulae of neighboring nodes (chemical structures  701  and  702 ). The processor, configured by the prediction module code, generates a new chemical identifier, such as a new chemical formula (NCF), by modifying the chemical formula of sub-unit C ( FIG. 7A ) according to known chemical rules and libraries. 
     Once NCFs are generated, they are converted by the processor into numerical forms as in step  705 . The converted forms are then placed in the node array stored in the memory of the processor, as in step  706 . Furthermore, the processor is configured by code to filter the NCFs, selecting only those that result in placement in the node array at a desired coordinate location in the virtual array, as shown in step  707 . 
     Discussion of Synthesis of Newly Identified Subject Matter 
     Step  708  details the actions taken by the processor to access, from a database or other memory storage location, the original NCFs corresponding to the filtered coded forms. In step  709 , the processor is configured by code to evaluate the NCFs in light of stored or accessible chemical synthesis rules in order to determine likelihood of synthesis. The processor is further configured by code executing therein to output NCFs with a synthesis probability above a threshold to a visualization module for display, as indicated at step  710 . A collection of new chemical entities with associated synthesis probabilities can be presented to a user as a visualization within the virtual array of the stored chemical identifiers in the storage location. Alternatively, a user supplied metric, such as synthesis time, cost, or difficulty is implemented and used to filter the results displayed or provided to the user. 
     In yet a further embodiment, the prediction module configures the processor to predict a location of potential interest within the virtual array and generate a new chemical formula corresponding to that location. In one example, the prediction module is configured to generate a time series plot indicating the publication of source documents over time. In a further example, the prediction module is configured to extrapolate, based, e.g., on the rate of publications of source documents, a development path for a common inventor or assignee. The system described may be configured to generate a new chemical entity which, when placed in the virtual array, occupies a location in line with the development path or which is clear of that path. 
     In a further embodiment, the prediction module is configured to extrapolate a location or locations in the virtual node array at which the development path of a plurality of assignees or inventors will intersect, and generate a chemical formula which, when placed in the virtual array, occupies or is clear of that intersection location. 
     As a further embodiment of the system and method of the present invention, the processor is further configured by code to generate a synthesis strategy along with the new chemical identifier, such as may occupy or be clear of a development path of one or more assignees, inventors, and so on, as discussed above. For instance, the new chemical formula generation step  660  includes sub-steps designed to generate a synthesis strategy or plan based on organic compound synthesis analysis of the desired chemical compound described by the new chemical identifier. 
     The techniques for utilizing and designing computer-assisted synthesis strategies include, by way of non-limiting example, computer based retrosynthetic analysis. For example, “Route designer: a retrosynthetic analysis tool utilizing automated retrosynthetic rule generation” James Law, et al., J. Chem. Inf. Model., 2009, 49 (3), 593-602, the content of which is hereby incorporated by reference in its entirety, describes the utilization of software tools and processes to generate a proposed chemical synthesis strategy based on breaking down of a chemical identifier into idealized compound fragments. These idealized compound fragments are substituted with synthetic equivalents having known synthesis strategies and have similar characteristics to the characteristics of the idealized fragments, e.g., the same elemental composition, binding affinity, etc., according to a database of chemical data. In this way, the software tool can execute to cause a processor to provide a synthesis strategy for the new chemical identifiers using synthesis pathways already known in the art. 
     In the event that the idealized compound fragments do not have known synthetic equivalents, these idealized components are broken down into smaller fragments until the synthesis of each of the fragments, or their substituted synthetic equivalents are described in a database of synthesis strategies or pathways. Thus, the synthetic pathway to achieving the new chemical entity is derived using the new chemical entity identifier in lieu of a process of trial and error using common starting reactants. 
     In one potential arrangement, the fragment data used to determine the synthesis are the same fragment data used to generate the new chemical entity. For example, each of the modified sub-units (See C in  FIG. 7A ) used to generate the new chemical identifier are utilized as idealized or synthetic equivalent fragments in order to determine a synthesis pathway of the resulting new chemical entity. Owing to the fact that the sub-units utilized to generate the new chemical entity are known, they are derived in part from the chemical database associated with a given embodiment of the system. As a consequence, embodiments of the present invention allow for the generation of both the new chemical entity as well as a synthesis solution to synthesize the chemical compound. In other embodiments, different strategies for synthesizing or designing a usable synthesis strategy, such as, but not limited to, functional group analysis, stereochemical and chirality analysis, structure-goal seeking strategies, topological analysis strategies and transform-based strategies can be employed to synthesize a chemical compound described by the chemical identifier. 
     In one arrangement, once a chemical formula and the synthesis strategy are generated, this information is then used to synthesize the chemical compound described by the chemical formula or identifier according to the synthesis strategy. 
     For example, in one particular embodiment of the invention, the new chemical entity identification method includes a further synthesis step, carried out to enable synthesizing a compound described by the newly generated chemical identifier. In a further embodiment, when the newly generated chemical identifier is intended to have a therapeutic effect on a biological organism, a further step includes preparing a pharmaceutical composition comprising an effective amount of the chemical compound corresponding to the new chemical formula generated according to the chemical entity generation module, or an acceptable salt thereof, and a pharmaceutically acceptable excipient. A further step can include coating the so-prepared composition, such as with an enteric coating. The method can include a variety of additional steps to prepare the composition in a form suitable for administration to a person. 
     In a further arrangement, the chemical compounds are synthesized using a device or machine configured to implement continuous-flow multi-step organic compound synthesis utilizing a feed stock of standard reactants commonly used in the type of synthesis reactions necessary to achieve the desired end chemical compound. For example, the processor of the present invention can be further configured to provide instructions to a computer controlled continuous flow reactor, such the chemical compound described by the new chemical entity identifier is synthesized according to a retrosynthetic plan determined by a synthesis plan module configured as code executing within the processor. 
     Utilization of the Trained Map 
     It is appreciated and understood by the inventors that the trained map detailed and described herein can, in particular implementations, be extended, revised or utilized for further or deeper analysis of a biological target or chemical compound. By way of non-limiting implementation, the trained map generated according to steps  210 - 260  and provided herein, can be utilized for the purposes of evaluating the suitability of a compound for treatment of a particular disease or interaction with a particular biological target. Without being limited to any theory or explanation, the trained map can be retargeted to as to identify which, if any, of a collection of compounds under analysis might have utility in the treatment of a particular disease or interaction with a particular biological target. For example, a trained map generated for a biological target can be used to evaluate new compounds. In one implementation, these newly evaluated compounds are inserted into an already trained map. By identifying the placement location in the generated trained map information can be derived about the suitability of the new compound for the treatment of a particular disease or interaction with a particular biological target. 
     While in no way limiting, in one arrangement a trained map is generated to determine the similarity of various compounds used to treat a disease, illness, syndrome or other ailment (such as but not limited to a Sudden Acute Repository Syndrome (SARS)). New compounds, which are not indicated for treatment of such an illness, are introduced into the trained map. Based on the position of the new compounds in the trained map, the new compounds can be ranked as potential screening candidates for additional therapies or treatments for the ailment. 
     Re-Targeting Process 
     Turning now to the flow diagram of  FIG. 9 , the retargeting or refocusing process begins by establishing a trained map of compounds exemplified from source documents that are directed to a particular biological target of interest, as shown in step  802 . For example, a trained map is generated according to the steps  210 - 260  as provided herein. In one implementation, the trained map is generated using the results of a query of source documents. As noted, the source documents can be published (or unpublished in the case of an internal review) patent documents, including patent applications and patents, that have been filed at the United States Patent and Trademark Office, or other foreign patent offices and from various commercial patent databases. Other collections of non-patent documents are suitable for use with the system and method, such as, by way of example and not limitation, technical and scientific journals, research compendiums, and other documents containing information relating to chemical compounds, any or all of which can be included in the database. Here, as provided in steps  210 - 260 , a query of the sources documents described a known biological target is executed against one or more databases of source documents. 
     The results of that query are processed such that the chemical identifiers (structural, nomenclature, representational) referenced in the source documents are extracted and converted into coded forms that can be placed in the trained map. As noted with regards to generating a trained map, one or more machine learning methods are used to place each of the coded forms within a two or three dimensional representation (the trained map) of the relationship of all of the coded forms to one another. 
     Turning now to step  804 , one or more additional chemical identifiers or compounds are identified and provided to the trained map for evaluation. It will be appreciated that there exists in the art data and document sources that indicate the intended use, suitability or experimental results of a clinically approved, marketed or tested compounds. For example, investigational studies, investigatory new drug documents, publications or articles can reference the suitability of a compound for treatment of a particular ailment, symptom, or disease. Even when such investigations prove to not bear fruit with the intended ailment, data on these investigated compounds can enter into the public domain or the records of an institution or organization. Likewise, clinically approved compound may have efficacy for a number of different ailments or diseases. Such “off-label” uses are known and documented to persons of ordinary skill in the art. In one implementation of the retargeting process, one or more queries are conducted on databases of clinically approved, marketed or investigated compounds based on one or more search criteria. For example, where the biological target of interest (such as conditions relating to SARS) includes one or more features or conditions of note (i.e. a particular agonist or antagonist) the search query is constructed to identify small molecules or peptides that were investigated as relating to such a feature or condition. That is, where a studied disease is believed to be treated by a compound that acts as an agonist or antagonist to a particular biological target, the query in step  804  would return such a compound in the search results. Such is true even if the overall disease that the compound was intended to treat was different than the biological target used to generate the trained map. 
     As shown in step  806 , upon receiving the compounds from the query conducted in step  804 , a suitably configured processor converts the compounds into numerical or coded forms (herein “curated coded forms”) suitable for incorporation into the trained map. For example, the conversion module  330  is used to configure the processor (such as processor  1302 ) to convert the chemical identifier returned in the query of step  804  into curated coded forms and store the curated coded forms in a memory or other storage location while preserving the relationship between the chemical identifier and the coded form. In one particular implementation, the processor configured by the conversion module utilizes a MDL 960-bit SS-keyset numerical conversion algorithm, produced by MDL Information Systems, in order to convert the chemical identifier into a numerical representation. Alternatively, other keysets such as, for example, those based on affinity-fingerprint algorithms or feature-tree algorithms, or the 881 bit structural keys used by PubChem, or 1- and 2-dimensional molecular descriptors can be implemented by the processor  102  in order to obtain coded forms of chemical identifiers identified in step  804 . 
     It should be appreciated that, as part of the map generation process (step  802 ), the plotting module  340  configures a processor to seed the n-dimensional map it with placeholder values (as noted at step  242 ). The placeholder values in this example are selected to cover the range of potential numerical values for the converted coded (herein “document”) forms of the chemical identifiers from the original search for the biological target, as identified in step  802 . In a particular embodiment, the plotting module  340  includes code to further configure the processor to insert each document coded form (obtained in step  802 ) at a location in the n-dimensional space, such as according to step  250 . In particular, the location selected for the insertion operation is a function of the degree of similarity that the document coded form shares with the placeholder data or to other document coded forms previously placed in the n-dimensional space. 
     Turning now to step  808 , each of the curated coded forms of the chemical identifiers generated in step  806  are assigned to a cluster or node of the trained map generated in step  802 . Here, the curated coded forms of the chemical identifiers generated in step  806  are placed, using a processor, at a coordinate location in the n-dimensional space according to the similarity of the curated coded forms (of the chemical identifiers obtained in step  806 ) to either the placed coded document forms or the placeholder nodes. It should be understood, however, that one embodiment of the invention utilizes the plot coordinates of the curated coded forms to compute the degree of similarity to the already existing coded forms placed within the n-dimensional array without actually plotting the new coded forms obtained in step  806  into the trained map. In this manner, the coordinate where a particular curated coded form would be placed is determined, but the trained map itself is not updated to incorporate the curated coded form. 
     It will be appreciated that instead of generating a trained map as in step  802  and then adding the curated coded forms to the generated map as in step  808 , the trained map can be generated that incorporates both document coded forms and curated coded forms at the time of map generation. Turning to the flow diagram of  FIG. 10 , a collection of chemical compounds is identified for inclusion into a data set for use in generating the trained map from document sources (e.g. the source documents for the document coded forms). As shown in step  902 , one or more additional chemical identifiers or compounds are identified or curated from additional sources (e.g. the source documents for the curated coded forms). 
     The identified compounds in step  902  are then converted into curated coded forms in step  904 . For example, the conversion module  330  is used to configure the processor (such as processor  1302 ) to convert the chemical identifiers identified in both the biological target query (document forms) and the curated review of clinically approved or marketed compounds (curated forms) into coded forms in a memory or other storage location while preserving the relationship between the chemical identifier and the coded form. For example, the conversion process is applied to a first database or array of document sourced chemical identifiers. The conversion process is applied to a second database or array of curated chemical identifiers. The results of this conversion process is stored to a third database or array that combines the document and curated forms while maintaining a link to the originating array or database. As previously noted, In one particular implementation, the processor configured by the conversion module utilizes conversion a MDL 960-bit SS-keyset numerical conversion algorithm, produced by MDL Information Systems, or other 1- and 2-dimensional molecular descriptors implemented by the processor  102  in order to obtain document and curated coded forms of chemical identifiers identified in step  902 . 
     Turning now to step  906 , a trained map is generated according to the steps  210 - 260  as provided herein that includes the both the document and curated coded forms. As noted with regards to generating a trained map in step  210 - 260 , one or more machine learning methods are used to place each of the coded forms, including the curated forms obtained in step  904 , within a two or three dimensional representation (the trained map) of the relationship of all of the coded forms to one another. 
     As shown is step  810  (in both  FIGS. 9 and 10 ), in some instances the coded form obtained in step  804  is not located close to any of the placeholder data or the document coded forms used to generate the n-dimensional map. That is, the distance between a particular curated coded form (such as one obtained in step  804  or  904 ) and one or more of the nodes or placeholder values used to generate the n-dimensional map is greater than a pre-determined threshold value. For example, when the curated converted forms are placed within the n-dimensional map they are not placed “close” to any other node or cluster of document coded forms. In one implementation, a filtering module  805  configures the processor to remove any curated coded forms from the trained map that are not placed within a pre-determined threshold distance of any node. 
     In a further filtering step, the filtering module is further configured to remove any curated coded form molecules that are placed at a node that that is not associated with any document coded forms. For example, where the curated coded form populates a node that is not populated by at least one document coded form, the filtering module  805  configures a processor to remove such a populated curated coded form from the virtual map. 
     Alternatively, where the coded forms are not introduced into the map, as noted in step  808 , the filtering module  805  configures the processor to remove any coded forms not meeting the previously described filtering criteria from the list of coded forms under analysis. Such a list is then stored in the memory of a processor for further use. 
     Turning now to step  812 , the filtering module  805  also configures the processor to determine the similarity between the filtered list of curated coded forms and the document coded forms. For example, the distance between each curated coded form and the closest document coded form is determined. As in step  810 , here those curated coded forms that are a greater distance than a pre-determined threshold are removed from the n-dimensional map. Alternatively, a list of curated coded forms is updated to indicate which curated coded forms are not within a pre-determined distance to a coded from used to generate the n-dimensional map. Such a list is then stored in the memory of a processor for further use. 
     Using the results of filtering step  812 , the processor is configured by a ranking module  807  to rank the remaining curated coded forms according to one or more ranking criteria as in step  814 . For instance, the ranking module  807  configures the processor to evaluate each of the curated forms remaining after the filtering step  812  according to the distance, similarity and how many document coded forms/nodes are within a set distance metric from the curated coded. For example, where the similarity between a curated coded form and one or more document coded forms is high, the distance between these coded forms will be small. The ranking module  807  determines, for each curated coded form, the number of document coded forms that are within a pre-set distance, how close the curated coded form is to each of the document coded forms within the pre-set distance, and how close the curated coded form is to the node or cluster at the given location in the pre-trained map. Given such data, the ranking module  807  ranks the curated coded form (and thus the associated compound, based on these metrics. The ranked list of coded forms is then stored in one or more memory storage locations and provided to an output device (such as a display or database) for further evaluation. For example, where the coded forms are provided as a linked list or array that is linked to a list of the compounds, the output device displays the list of compounds in ranked order based on the curated coded form array. 
     Thus, according to one or more implementations described herein, one or more processors are configured to generate an n-dimensional map using the results of a query for compounds enumerated within a collection of documents describing a particular biological target of interest. Here the chemical identifiers contained within the results are transformed into document coded forms and used to generate the n-dimensional map. The chemical structures of a curated set of compounds (such as small molecules or peptides) are converted into the same type of coded forms used to generate the n-dimensional map. In turn, the processor is configured to evaluate the distance between these curated coded forms and the closest cluster (or node) in the generated n-dimensional map by determining the distance between curated coded form and the node of a cluster of coded forms present in the n-dimensional map. The processor is further configured by one or more code modules to filter-out distant curated coded forms that are not associated with a node, or a node that is not associated with any document coded forms, by removing those curated coded forms that are greater than a pre-determined distance from its closest cluster&#39;s weight vector. The processor is also then configured by code to compare remaining curated coded forms that are within the pre-determined distance of a node to the document coded forms by calculating the distance between the descriptor vectors for a given curated coded form and document coded form. The processor is further configured by one or more modules to filter-out relationships between curated coded forms and document coded forms over a certain threshold, also calculate similarity between a coded form and a document coded form. Using the distance, similarity, and number of patented molecules within a pre-determined distance of a coded form, the coded forms are ranked. 
     Furthermore, according to one or more implementations described herein, the chemical structures of a curated set of compounds (such as small molecules or peptides) are converted into the curated coded forms. Additionally, one or more processors are configured to generate an n-dimensional map using the results of a query for compounds enumerated within a collection of documents describing a particular biological target of interest. Here the chemical identifiers contained within the results are transformed into document coded forms. A processor is configured by one or more modules to generate using both the curated and document coded forms, an n-dimensional map. Upon generation, the processor is configured to evaluate the distance between the curated coded forms and the closest cluster (or node) in the generated n-dimensional map by determining the distance between curated coded from and the node of a cluster of document coded forms present in the n-dimensional map. The processor is further configured by one or more code modules to filter-out distant curated coded form molecules by removing those greater than a pre-determined distance from its closest cluster&#39;s weight vector. The processor is also then configured by code to compare remaining curated coded forms that are within the pre-determined distance of a node to the document coded forms by calculating the distance between the descriptor vectors for a given curated coded form and document coded form. The processor is further configured by one or more modules to filter-out relationships between curated coded forms and document coded forms over a certain threshold, also calculate similarity between a coded form and a document coded form. Using the distance, similarity, and number of patented molecules within a pre-determined distance of a coded form, the coded forms are ranked. 
     The rank of the coded forms can then be used to evaluate the suitability of the corresponding molecules or compounds for use in interacting with the biological target. Those compounds ranked most highly are predicted to have similar or equivalent functionality with regards to a biological target as those compounds identified in the queried documents. Thus, the output of the ranked curated coded forms can be used to select promising candidates for additional investigatory studies with respect to the biological target. For instance, a chemical synthesis apparatus can be provided with the ranked list and instructed to synthetize one or more of the curated compounds based on their ranking. 
     The above processing functions can operate as a series of programmed steps performed by a properly configured computer system using one or more modules of computer-executable code. For instance, a set of software modules can be configured to cooperate with one another to provide prediction information regarding new chemical entities to a display device as described herein. In this regard, there can be database access modules, search modules, filtering modules, extraction modules, conversion modules, plotting modules, prediction modules, and visualization modules. 
     Each of these modules can comprise hardware, code executing in a computer, or both, that configure a machine such as the computing system  100  to implement the functionality described herein. The functionality of these modules can be combined or further separated, as understood by persons of ordinary skill in the art, in analogous embodiments of embodiments of the invention. 
     The processor  102  of the described invention is configurable for connection to remote storage devices and computing devices. For example, the processor of the described computer system may, in one embodiment, be configured for communication with a mobile computing device, or connecting via the internet to a remote server. 
     As illustrated in  FIG. 8 , the computing system  1300  and includes a processor  1302 , a memory  1304 , a storage device  1306 , a high-speed interface  1308  connecting to the memory  1304  and multiple high-speed expansion ports  1310 , and a low-speed interface  1312  connecting to a low-speed expansion port  1314  and the storage device  1306 . Each of the processor  1302 , the memory  1304 , the storage device  1306 , the high-speed interface  1308 , the high-speed expansion ports  1310 , and the low-speed interface  1312 , are interconnected using various buses, and can be mounted on a common motherboard as shown in  FIG. 8 , or in other manners as appropriate. The processor  1302  can process instructions for execution within the computing device  1300 , including instructions stored in the memory  1304  or on the storage device  1306  to display graphical information for a GUI on an external input/output device, such as a display  1316  coupled to the high-speed interface  1308 . In other embodiments, multiple processors and/or multiple buses can be used, as appropriate, along with multiple memories and types of memory. Also, multiple computing devices can be connected, with each device providing portions of the necessary operations (e.g., as a server bank, a group of blade servers, or a multi-processor system). 
     A mobile computing device  1350  may include a processor  102 , a memory  1364 , and an input/output device such as a display  1354 , a communication interface  1366 , and a transceiver  1368 , among other components. The mobile computing device  1350  can also be provided with a storage device, such as a micro-drive or other device, to provide additional storage. Each of the processor  1352 , the memory  1364 , the display  1354 , the communication interface  1366 , and the transceiver  1368 , are interconnected using various buses, and several of the components can be mounted on a common motherboard or in other manners as appropriate. 
     The processor  1352  can communicate with a user through a control interface  1358  and a display interface  1356  coupled to the display  1354 . The display  1354  can be, for example, a TFT (Thin-Film-Transistor Liquid Crystal Display) display or an OLED (Organic Light Emitting Diode) display, or other appropriate display technology. The display interface  1356  can comprise appropriate circuitry for driving the display  1354  to present graphical and other information to a user. The control interface  1358  can receive commands from a user and convert them for submission to the processor  1352 . In addition, an external interface  1362  can provide communication with the processor  1352 , so as to enable near area communication of the mobile computing device  1350  with other devices. The external interface  1362  can provide, for example, for wired communication in some embodiments, or for wireless communication in other embodiments, and multiple interfaces can also be used. 
     The memory  1364  stores information within the mobile computing device  1350 . The memory  1364  can be implemented as one or more of a computer-readable medium or media, a volatile memory unit or units, or a non-volatile memory unit or units. An expansion memory  1374  can also be provided and connected to the mobile computing device  1350  through an expansion interface  1372 , which can include, for example, a SIMM (Single In Line Memory Module) card interface. The expansion memory  1374  can provide extra storage space for the mobile computing device  1350 , or can also store applications or other information for the mobile computing device  1350 . Specifically, the expansion memory  1374  can include instructions to carry out or supplement the processes described above, and can include secure information also. Thus, for example, the expansion memory  1374  can be provided as a security module for the mobile computing device  1350 , and can be programmed with instructions that permit secure use of the mobile computing device  1350 . In addition, secure applications can be provided via the SIMM cards, along with additional information, such as placing identifying information on the SIMM card in a non-hackable manner. 
     It should be understood that various combinations, alternatives and modifications of the present invention could be devised by those skilled in the art in view of this disclosure. The present invention is intended to embrace all such alternatives, modifications and variances that fall within the scope of the appended claims. While the invention has been particularly shown and described with reference to a preferred embodiment thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention 
     The mobile computing device  1350  can communicate wirelessly through the communication interface  1366 , which can include digital signal processing circuitry where necessary. The communication interface  1366  can provide for communications under various modes or protocols, such as GSM voice calls (Global System for Mobile communications), SMS (Short Message Service), EMS (Enhanced Messaging Service), or MMS messaging (Multimedia Messaging Service), CDMA (code division multiple access), TDMA (time division multiple access), PDC (Personal Digital Cellular), WCDMA (Wideband Code Division Multiple Access), CDMA2000, or GPRS (General Packet Radio Service), among others. Such communication can occur, for example, through the transceiver  1368  using a radio-frequency. In addition, short-range communication can occur, such as using a Bluetooth, WiFi, or other such transceiver (not shown). In addition, a GPS (Global Positioning System) receiver module  1370  can provide additional navigation- and location-related wireless data to the mobile computing device  1350 , which can be used as appropriate by applications running on the mobile computing device  1350 . 
     The mobile computing device  1350  can also communicate audibly using an audio codec  1360 , which can receive spoken information from a user and convert it to usable digital information. The audio codec  1360  can likewise generate audible sound for a user, such as through a speaker, e.g., in a handset of the mobile computing device  1350 . Such sound can include sound from voice telephone calls, recorded sound (e.g., voice messages, music files, etc.) and sound generated by applications operating on the mobile computing device  1350 . 
     The mobile computing device  1350  can be implemented in a number of different forms, as shown in  FIG. 8 . For example, it can be implemented as a cellular telephone  1380 . It can also be implemented as part of a smart-phone  1382 , personal digital assistant, or other similar mobile device. 
     Various embodiments of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various embodiments can include embodiment in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device. 
     These computer programs (also known as programs, software, software applications or code) include machine instructions for a programmable processor, and can be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. As used herein, the terms machine-readable storage medium and computer-readable storage medium refer to any non-transitory computer program product, apparatus and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable storage medium that receives machine instructions as a machine-readable signal. The term machine-readable signal refers to any signal used to provide machine instructions and/or data to a programmable processor. A non-transitory machine-readable storage medium does not include a transitory machine-readable signal. 
     To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input. 
     The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server  1324 ), or that includes a middleware component (e.g., an application server  1320 ), or that includes a front end component (e.g., a client computer  1322  having a graphical user interface or a Web browser through which a user can interact with an embodiment of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet. 
     The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. 
     Additional Implementations of the approaches provided herein: 
     Point 1. A computer-implemented method for generating an artificial environment within a memory of a computer, in which chemical identifiers that relate to a particular subject matter and which are described in at least one document are extracted and analyzed, the method comprising: submitting, in electronic form, a search to at least one document database for documents describing the subject matter using a defined search strategy; extrapolating, to a first array within the memory of the computer, at least one chemical identifier described in at least one document returned from the search, the extrapolating step using an extraction module comprising code executing in a processor; transforming each chemical identifier in the first array into a respective coded form having a range of values using a conversion module comprising code executing in the processor; populating the respective coded forms into a second array within the memory of the computer; generating a virtual n-dimensional array of nodes configured to encompass the range of values in the second array using a node array generator module comprising code executing in the processor, each node of the virtual n-dimensional array having an associated weight vector value based on the range of values in the second array; placing each coded form in the second array into a node of the virtual n-dimensional array according to an unsupervised learning algorithm using a placement module comprising code executing in the processor to effect a placement; and outputting a visual representation of the virtual n-dimensional array. 
     2. The method of Point 1, further comprising the steps of: selecting a target node among the nodes within the virtual n-dimensional array; comparing, using a chemical feature (“CF”) module which comprises code executing in the processor, at least one CF corresponding to the coded form contained within a first node adjacent to the target node to at least one CF corresponding to the coded form contained in at least a second node adjacent to the target node, the first and second nodes sharing a border with the target node in the virtual n-dimensional array; identifying common CFs between the target and second nodes using a commonality module which comprises code executing in the processor; generating at least one new coded form based on combinations of the identified, common CFs which, when inserted into the virtual n-dimensional array, results in a placement within the target node, using a coded form generator module which comprises code executing in the processor; and outputting a chemical identifier corresponding to the new coded form. 
     3. The method of Point 1, further comprising the steps of: selecting a first node among the nodes within the virtual n-dimensional array; comparing, using a chemical feature (“CF”) module which comprises code executing in the processor, at least one CF corresponding to the coded form contained within the first node adjacent to at least one CF corresponding to the coded form contained in at least a second, adjacent node, the second node sharing a border with the first node in the virtual n-dimensional array; identifying common CFs between the first and second nodes using a commonality module which comprises code executing in the processor; generating at least one new coded form based on combinations of the identified, common CFs, which when inserted into the virtual n-dimensional array, results in a placement within the first or second node using a coded form generator module which comprises code executing in the processor; and outputting a chemical identifier corresponding to the new coded form. 
     4. The method of Point 1, further comprising the steps of: selecting a first node among the nodes within the virtual n-dimensional array; comparing, using a chemical feature (“CF”) module which comprises code executing in the processor, at least one CF corresponding to the coded form contained within the first node adjacent to at least one CF corresponding to the coded form contained in at least a second node, the second node sharing a border with the first node in the virtual n-dimensional array; identifying common CFs between the first and second nodes using a commonality module which comprises code executing in the processor; generating at least one new coded form based on combinations of the identified, common CFs; regenerating the n-dimensional node array to encompass the range of values stored in the second array including the new coded form such that, when inserted into the regenerated virtual n-dimensional array, the new coded form is placed in a node situated between the first and second nodes, using a coded form generator module which comprises code executing in the processor; and outputting a chemical identifier corresponding to the new coded form. 
     5. The method of Point 1, wherein the coded form is a numerical form and wherein the adjustment module operates on the numerical form. 
     6. The method of Point 1, wherein the document database contain patent documents. 
     7. The method of Point 1, wherein the neural network algorithm of the adjustment module comprises a self-organizing mapping algorithm. 
     8. The method of Point 2 wherein the chemical identifier is at least one of a chemical formula, a chemical structure, or chemical name derived from chemical nomenclature. 
     9. The method of Point 2, wherein the target node within the virtual n-dimensional array in the memory is an empty node lacking any coded forms. 
     10. The method of Point 1, wherein placing each respective coded form of the second array in the virtual n-dimensional array of nodes includes calculating a distance metric between the a particular one of the respective coded forms (“input vector”) and the weighted vector placeholder value for each node, and placing the particular coded form in the node having the smallest calculated distance metric. 
     11. The method of Point 10, wherein the placement module further comprises code that configures the processor to carry out the steps of: identifying nodes within a predetermined distance metric of the input vector using a neighborhood function (“neighborhood”); and adjusting the weighted vector placeholder value of nodes within the neighborhood using an update formula, wherein the update formula comprises adjusting, using code executing in the processor, the weight vectors of the nodes within the neighborhood such that the distance metric between each node in the neighborhood and the input vector is modified. 
     12. The method of Point 1, further comprising accessing via a commonality module comprising code executing in the processor the chemical identifier corresponding to the coded form contained within the first node from the document database and comparing the accessed chemical identifier to a chemical identifier corresponding to the coded form contained within the second node from the database, and extracting from both chemical identifiers at least one common sub-identifier. 
     13. The method of Point 1, further comprising associating, using a visualization module comprising code executing in the processor, specific visual markers to each numerical form based on at least the chemical identifier and bibliographic data found in a given patent document in the at least one document database. 
     14. The method of Point 15, further comprising generating a visual indication, using a time-series module which comprises code executing in the processor, of the occurrence of an event related to a plurality of coded forms over time. 
     15. The method of Point 14, where the event comprises the publication of source documents over time. 
     16. The method of Point 2, further comprising generating, using the generator module, specific coded forms based on the chemical identifiers present in the virtual n-dimensional array of nodes in the memory by identifying a common CF to the coded forms of the first and second nodes, and adding at least one other chemical identifier selected from a list of possible chemical identifiers to the common CF. 
     17. The method of Point 16, wherein the generator module further comprises code executing in the processor to: validate a newly generated chemical identifier corresponding to the new coded form by evaluating the new chemical identifier against a database of chemical binding and chemical structure rules in order to generate a probability that the new chemical identifier is synthesizable, and output the new chemical identifier when the synthesis probability is above a pre-set threshold. 
     18. The method of Point 2, further comprising: generating, with a synthesis design module configured as code executing on the processor to generate, based on the chemical identifier corresponding to the new coded form, a synthesis strategy for synthesizing a compound described by the chemical identifier corresponding to the new coded form. 
     19. The method of Point 18, further comprising: synthesizing the chemical according to the synthesis strategy. 
     20. A pharmaceutical composition comprising: a pharmaceutically effecting amount of a chemical compound described by the chemical identifier corresponding to the new coded form generated according to method of Point 2, or a pharmaceutically acceptable salt thereof, in combination with a pharmaceutically acceptable excipient. 
     21. A chemical composition synthesized according to the process of Point 2. 
     While this specification contains many specific embodiment details, these should not be construed as limitations on the scope of any embodiment or of what can be claimed, but rather as descriptions of features that can be specific to particular embodiments of particular embodiments. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable sub-combination. Moreover, although features can be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination can be directed to a sub-combination or variation of a sub-combination. 
     Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing can be advantageous. Moreover, the separation of various system components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products. 
     The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising”, when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. 
     It should be noted that use of ordinal terms such as “first,” “second,” “third,” etc., in the claims to modify a claim element does not by itself connote any priority, precedence, or order of one claim element over another or the temporal order in which acts of a method are performed, but are used merely as labels to distinguish one claim element having a certain name from another element having a same name (but for use of the ordinal term) to distinguish the claim elements. 
     Also, the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. The use of “including,” “comprising,” or “having,” “containing,” “involving,” and variations thereof herein, is meant to encompass the items listed thereafter and equivalents thereof as well as additional items. 
     Particular embodiments of the subject matter described in this specification have been described. Other embodiments are within the scope of the following claims. For example, the actions recited in the claims can be performed in a different order and still achieve desirable results. As one example, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In certain embodiments, multitasking and parallel processing can be advantageous. 
     While the invention has been particularly shown and described with reference to a preferred embodiment thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention.