Patent Application
20100250218
The present invention relates to systems for the prediction of drug metabolism and the toxicity of novel compounds.
Cellular life can be represented and studied as the “interactome,” the dynamic network of biochemical reactions and signaling interactions between active proteins. A systemic network analysis is optimal for the integration and functional interpretation of high-throughput experimental data that are abundant in drug discovery yet poorly understood. The composition and topology of these complex networks are closely associated with vital cellular functions, which have important implications for life science research. Network theory development has advanced alongside the curation of reliable databases of protein interactions for human and model organisms that require comprehensive analytical tools.
The existing drug discovery and analysis systems can be classified into two categories. The first type of system analyzes high throughput (HT) data, which over the last several years has resulted in a paradigm shift for life science research due to the unprecedented scale-up of several laboratory techniques. These include automated DNA sequencing, global gene expression measurements, and proteomics and metabonomics techniques. High throughput data provides information on gene expression, protein interactions, and small molecule metabolism such that such data are ubiquitous throughout the drug discovery pipeline from target identification and validation to the development and testing of drug candidates to clinical trials.
Software such as MetaCore™ (GeneGo, Inc., St. Joseph, Mich.), PathArt and PathwayAssist (Ariadne Genomics, Inc., Rockville, Md.), and Pathways Analysis (Ingenuity Systems, Mountain View, Calif.) can be used to analyze such HT data in association with gene expression and protein pathways. These software programs can be used to predict and model which protein pathways may be affected by a small molecule. The information on protein interactions is collected from the published experimental data that is then annotated and assembled into databases on the interactions. The network data analysis software that is now commercially available is robust enough for simultaneous processing of many large data files containing thousands of data points such as whole-genome expression microarrays.
The second category of drug discovery systems has existed since the 1970s. At that time, industry and academia organized databases on proteins, enzyme-encoding genes, and metabolic and cell signaling pathways, that represented a starting point for systemic absorption, distribution, metabolism, elimination, and toxicology (ADME/Tox) studies. There have been limited efforts to organize ADME/Tox data, although separate focused databases of ADME associated proteins or pathways such as PharmaGKB, a nuclear receptor database, a human membrane transporter database and other databases could theoretically be integrated. Newer commercial drug metabolism databases, such as Metabolite (MDL Information Systems, San Leandro, Calif.), Metabolism (Accelrys, Inc., San Diego, Calif.), and BioFrontier/P450 (FQS Poland), represent a broad collection of metabolic data. These databases are useful for calculating probabilities for a metabolic reaction, predicting metabolites, or predicting the sites of metabolism using a statistical approach.
Similarly, the accumulation of drug metabolism data from the literature has also resulted in the creation of expert systems for metabolism prediction for esters, O- and N-alkyl derivatives, and aromatic fragments. This has resulted in commercial rule-based products such as MetabolExpert (CompuDrug International, Inc., Sedona, Ariz.), META (MultiCase Inc., Cleveland, Ohio), and METEOR (Llasa Ltd., Leeds, United Kingdom).
The application of quantitative structure activity [or metabolism] relationships (QSA[M]R) has been widely applied by Hansch and co-workers with generally small homologous sets of molecules. These programs require the generation of molecular descriptors which are then related to experimental data to result in an equation using one or more of many algorithm technologies. A new molecule would then have these descriptors generated to ultimately predict a value from the model. Based upon rules written into the software, a molecule is then predicted to be either biologically active as intended or not.
Early efforts divided metabolic transformations into three composite processes: binding to an enzyme, chemical modification, and release of a metabolite. In time, the properties found to be important to determine enzyme binding and transformation included lipophilicity, steric, electronic, and molecular shape properties with metabolite release likely requiring the opposite properties to binding.
There is a present and recurring need for improved drug discovery systems that allow easy input of molecular structure and/or properties, provide an intuitive interface with the user, and perform comprehensive and modifiable analysis of metabolite generation through use of comprehensive databases and metabolic and QSAR models. Such a drug discovery system is desired to provide more accurate and precise prediction of the interaction between xenobiotics and biological organisms.
One aspect of the invention is a system for predicting the interaction between a chemical compound and a biological organism, the system comprising: a first means for predicting one or more first level metabolites of the chemical compound in the biological organism; a second means for predicting the interaction of the chemical compound and the first level metabolites with the biological organism; and a means for visualizing the interaction of the chemical compound, the first level metabolites, and the biological organism. According to an embodiment of the invention, the biological organism is a human being.
According to another embodiment of the invention, the biological organism is modeled using one or more types of biological compounds. The one or more types of biological compounds is comprised of one or more of the group composed of proteins, nucleic acids, or organic compounds. The group of proteins is comprised of one or more of the group composed of enzymes, prions, and peptides. The group of nucleic acids is comprised of one or more of the group composed of DNA, RNA, genes, and chromosomes.
According to an embodiment of the invention, the system further comprises a third means for predicting one or more higher-level metabolites of the one or more first level metabolites. The method for the third means of prediction is the same as the method for the first means of prediction. According to an embodiment of the invention, the system further comprises means for choosing the one or more predicted first level or higher-level metabolites to predict the interactions of the chemical compound in the biological organism.
According to an embodiment of the invention, the system further comprises means for inputting one of the chemical compound name, structure, or data. As a further embodiment of the invention, the system comprises a fourth means for prediction of the likelihood of the one or more predicted first level or higher-level metabolites to occur in the biological organism.
According to an embodiment of the invention, the system comprises means for analyzing high-throughput data. The system may further comprise means for generating one or more biological pathways from the high-throughput data.
According to an embodiment of the invention, a system comprises databases comprising of at least one or more of the group composed of xenobiotics, endobiotics, ligands, drugs, drug interactions, drug binding data, biological pathways, genes, proteins, and disease links to genes.
According to an embodiment of the invention, a system further comprises means for comparing predicted interactions of at least one of the chemical compound, first level metabolites and higher-level metabolites in the biological organism with the biological pathways generated from the high-throughput data. A system may further predict the interaction of at least one of the chemical compound, first level metabolites, and higher-level metabolites with the biological organism is advantageous. Additionally, a system may predict the advantageous interaction that a chemical compound can be used as a drug in the biological organism.
According to an embodiment of the invention, a system predicts the interaction is disadvantageous in the biological organism of at least one of the chemical compound, first level metabolites, and higher-level metabolites. A system may further predict whether the disadvantageous interaction of a chemical compound is toxic or has side or deleterious effects in the biological organism.
According to an embodiment of the invention, a system predicts the one or more first level or higher-level metabolites of the chemical compound using predetermined rules, QSAR models, or other algorithms. According to another embodiment of the invention, a system user may choose the predetermined rules, QSAR models, or other algorithms to predict the one or more first level or higher-level metabolites of the chemical compound.
Another aspect of the invention is a method for predicting the interaction between a chemical compound and a biological organism, the system comprising: a first step for predicting one or more first level metabolites of the chemical compound in the biological organism; a second step for predicting the interaction of the chemical compound and the first level metabolites with the biological organism; and a third step for visualizing the interaction of the chemical compound, the first level metabolites, and the biological organism. According to an embodiment of the invention, the biological organism is a human being.
According to another embodiment of the invention, the biological organism is modeled using one or more types of biological compounds. The one or more types of biological compounds is comprised of one or more of the group composed of proteins, nucleic acids, or organic compounds. The group of proteins is comprised of one or more of the group composed of enzymes, prions, and peptides. The group of nucleic acids is comprised of one or more of the group composed of DNA, RNA, genes, and chromosomes.
According to an embodiment of the invention, the method further comprises a fourth step for predicting one or more higher-level metabolites of the one or more first level metabolites. The method for the means of prediction used for the fourth step may be the same means of prediction as needed for the first step. According to an embodiment of the invention, the method further comprises a step for choosing the one or more predicted first level or higher-level metabolites to predict the interactions of the chemical compound in the biological organism.
According to an embodiment of the invention, the method further comprises a step for inputting one of the chemical compound name, structure, or data. As a further embodiment of the invention, the method comprises another step for prediction of the likelihood of the one or more predicted first level or higher-level metabolites to occur in the biological organism.
According to an embodiment of the invention, the method comprises a step for analyzing high-throughput data. The method may further comprise a step(s) for generating one or more biological pathways from the high-throughput data.
According to an embodiment of the invention, a method comprises databases comprising of at least one or more of the group composed of xenobiotics, endobiotics, ligands, drugs, drug interactions, drug binding data, biological pathways, genes, proteins, and disease links to genes.
According to an embodiment of the invention, a method further comprises a step(s) for comparing predicted interactions of at least one of the chemical compound, first level metabolites and higher-level metabolites in the biological organism with the biological pathways generated from the high-throughput data. A method may have additional step(s) to further predict the interaction of at least one of the chemical compound, first level metabolites, and higher-level metabolites with the biological organism is advantageous. Additionally, a method may predict the advantageous interaction that a chemical compound can be used as a drug in the biological organism.
According to an embodiment of the invention, a method may have a step(s) to predicts the interaction of at least one of the chemical compound, first level metabolites, and higher-level metabolites with the biological organism is disadvantageous. A method may have an additional step(s) that may further predict whether the disadvantageous interaction of a chemical compound is toxic or has side or deleterious effects in the biological organism.
According to an embodiment of the invention, a method predicts the one or more first level or higher-level metabolites of the chemical compound using predetermined rules, QSAR models, or other algorithms. According to another embodiment of the invention, a system user may choose the predetermined rules, QSAR models, or other algorithms to predict the one or more first level or higher-level metabolites of the chemical compound.
Another aspect of the invention is a computer means for predicting the interaction between a chemical compound and a biological organism, the computer means comprising: a first means for predicting one or more first level metabolites of the chemical compound in the biological organism; a second means for predicting the interaction of the chemical compound and the first level metabolites with the biological organism; and a means for visualizing the interaction of the chemical compound, the first level metabolites, and the biological organism. According to an embodiment of the invention, the biological organism is a human being.
According to another embodiment of the invention, the biological organism is modeled using one or more types of biological compounds. The one or more types of biological compounds is comprised of one or more of the group composed of proteins, nucleic acids, or organic compounds. The group of proteins is comprised of one or more of the group composed of enzymes, prions, and peptides. The group of nucleic acids is comprised of one or more of the group composed of DNA, RNA, genes, and chromosomes.
According to an embodiment of the invention, the computer means further comprises a third means for predicting one or more higher-level metabolites of the one or more first level metabolites. The computer means for the third means of prediction is the same as the method for the first means of prediction. According to an embodiment of the invention, the computer means further comprises means for choosing the one or more predicted first level or higher-level metabolites to predict the interactions of the chemical compound in the biological organism.
According to an embodiment of the invention, the computer means further comprises means for inputting one of the chemical compound name, structure, or data. As a further embodiment of the invention, the computer means comprises a fourth means for prediction of the likelihood of the one or more predicted first level or higher-level metabolites to occur in the biological organism.
According to an embodiment of the invention, the computer means comprises means for analyzing high-throughput data. The computer means may further comprise means for generating one or more biological pathways from the high-throughput data.
According to an embodiment of the invention, a computer means comprises databases comprising of at least one or more of the group composed of xenobiotics, endobiotics, ligands, drugs, drug interactions, drug binding data, biological pathways, genes, proteins, and disease links to genes.
According to an embodiment of the invention, a computer means further comprises means for comparing predicted interactions of at least one of the chemical compound, first level metabolites and higher-level metabolites in the biological organism with the biological pathways generated from the high-throughput data. A computer means may further predict the interaction of at least one of the chemical compound, first level metabolites, and higher-level metabolites with the biological organism is advantageous. Additionally, a computer means may predict the advantageous interaction that a chemical compound can be used as a drug in the biological organism.
According to an embodiment of the invention, a computer means predicts the interaction of at least one of the chemical compound, first level metabolites, and higher-level metabolites with the biological organism is disadvantageous. A computer means may further predict whether the disadvantageous interaction of a chemical compound is toxic or has side or deleterious effects in the biological organism.
According to an embodiment of the invention, a computer means predicts the one or more first level or higher-level metabolites of the chemical compound using predetermined rules, QSAR models, or other algorithms. According to another embodiment of the invention, a user may choose the predetermined rules, QSAR models, or other algorithms to predict the one or more first level or higher-level metabolites of the chemical compound.
Further features and advantages of the present invention as well as the structure of various embodiments of the present invention will be more fully understood from the examples described below with reference to the accompanying drawings. The following examples are intended to illustrate the benefits of the present invention, but do not exemplify the full scope of the invention. All references cited herein are expressly incorporated by reference.
Existing drug discovery systems may be categorized into distinct groups. The first group utilizes and analyzes HT data, which elucidates chemical interactions in the body, including protein pathways and gene expression. The difficulty with using this type of software is the upfront biological testing that must be performed to obtain the massive quantities of data to allow a predictive capability with other molecules. HT data is usually generated for one pathway at a time and therefore the interaction of the pathways is also often lacking. This type of software may be considered to be based largely upon biochemical data generated by studying one biochemical pathway at a time.
A second type of approach is a rule-based system that uses a knowledge base of molecules with known information such as metabolism, binding etc. In this case the similarity of a new molecule to one that exists in the database can be used as a method to suggest similar metabolism of activity at a biological target. A third type of drug discovery systems that uses chemical interaction rules or QSAR methodology can be used to predict the metabolism of xenobiotic compounds in the body as well as affinity for other proteins such as enzymes, transporters, channels and receptors. This type of approach looks at a biological organism as a chemical system with distinct and unconnected chemical reactions.
As used here, a biological organism is defined as a living organism, such as a plant, insect, or mammal. Preferably, the biological organism of the invention is a human being. Alternatively, a biological organism may be a subsystem of a living being, e.g. the citric acid cycle or the lymphatic system; the subsystem of interest for study using the drug discovery system may be defined by the system user.
In general, databases and expert systems that contain combined data or rules from many different mammalian species may be less useful for predicting human metabolism alone. Ideally the data and rules for each species should be separate. Consequently, the programs using this information in combined databases tend to predict all the metabolic possibilities for an exogenous molecule, essentially creating an ‘average’ mammal that may be dissimilar to the human situation. The metabolic pathways and corresponding networks can be very different even in close mammalian species. Metabolism of the same drug may further vary substantially between individuals depending on the expression level of particular enzymes, polymorphisms in enzyme-encoding and regulatory genes and the presence of particular enzymes in normal and disease states as well as different tissues.
Effective drug discovery systems for the complexity of biological organisms require a system-wide approach to data analysis, which can be defined as the integration of “OMICs” data with computational methods or chemical modeling. A system-wide approach uses the relationships of all elements rather than approaching them separately. This approach can be taken from the “top down” (using a conceptual framework to integrate data) or from the “bottom up” (combining individually modeled biochemical processes). A system-wide approach states that the identification of the “parts list” of all the genes and proteins is insufficient to understand the whole. Rather, it is the assembly of these parts (the general schema, the modules, and elements) and the dynamics of changes in response to stimuli that is truly the key to understanding a biological organism. The assembly of “cellular machinery” can be called the “interactome”, the network of interconnected signaling, regulatory, and biochemical networks with proteins as the nodes and physical protein-protein interactions as the edges.
Interpreting ADME/Tox and particularly drug metabolism using a system-wide approach may improve understanding and ultimately predictions associated with a biological organism. The perturbing effect of a molecule on the complete biological organism can be observed either experimentally (using high-throughput screening against many proteins) or theoretically (using many computational models) and across all metabolic and signaling pathways. In this way an understanding of the effects of binding to multiple proteins simultaneously can be provided. The iterative approach based on multiple cycles of data generation and modeling can also create dynamic hypotheses which are advantageous compared with purely static models. This approach also requires the collection of high-throughput and high content screening data, including global gene expression, protein content, and metabolic profiles for the same samples as well as individual genetic, clinical, and phenotypic data.
Considerable quantities of empirical ADME/Tox data have been generated and used for computational model building to score large numbers of virtual molecules against individual properties. These predictions require multi-dimensional analysis alongside target affinity and represent a means to improve lead selection efficiency.
According to one aspect of the invention, systems for predicting the effect of a molecule on a biological organism predicts the interaction of the molecule with the interactome using basic rules, QSAR modeling, or analysis of high-throughput data. Furthermore, the prediction of the system using basic rules, QSAR modeling, or analysis of high-throughput data may be improved through comparison with chemically or structurally similar xenobiotics. Information on xenobiotics may be contained within a database. The drug discovery system of the invention may be used to predict any advantageous or disadvantageous interaction of a specific molecule on a biological organism, including the effectiveness or non-effectiveness of a molecule as a drug; the effect specific molecule on a biological organism, pathway or protein; the side effects that may be expected for a specific molecule in a biological organism; or the mode of action of a specific molecule to cause a known effect in a biological organism. For this invention, the inventive system may perform any of the above functions and will be generically called a drug discovery system.
Some of the individual steps of the drug discovery system of
According to an embodiment of the invention, the metabolite(s) of the molecule of interest are also predicted. The drug discovery system may also predict the one or more metabolites of the molecule by using predetermined rules for metabolic pathways. These predicted metabolites may then be further similarly processed by the system to determine their metabolites and so on. The metabolites may then be visualized associated with the target proteins that undergo the chemical reaction within the stored biological pathways. This presents predicted metabolites in the context of the empirical data. According to another embodiment of the invention, the system may then predict the interaction of each of the predicted metabolites with the interactome.
Component may be defined as the functional groups of molecules in biological organisms and is related to a molecular entity, localization, cell/tissue, and organism. Thus, a Component represents biological molecules within their biological context. For this invention, the molecular entity may be treated in a broader sense than just being a specific chemical compound. In our representation, a molecular entity may also be a group of molecules (e.g. a protein family or class of chemical compounds) or a molecular complex. This is particularly useful for representing the cellular processes, when the exact chemical composition or particular isoform of a protein participating in a pathway is unknown or ambiguous.
Transformation is defined as a biochemical reaction, transport, transcription and translation, or any biological process with a primary function being to change the amount of a Component (e.g., through a reaction) that is considered in its particular environment as linked to a sub-cellular compartment, tissue, and organism. During the Transformation of a Component, one or more other molecules (i.e., metabolites) may be generated.
Effect is defined as the influence that a Component(s) exert on either a Transformation(s) or another Effect(s). Each Effect has an agent (Component) and a target (Transformation, another Effect or Functional Block). Effect is the description of biological activity, whether or not its exact mechanism is known.
The three types of elements (i.e., Components, Transformations, and Effects) may be provided as packaged databases for analysis by the drug discovery system. Such databases are well-known and are becoming increasingly more abundant. Any known and appropriate database developed for human or other biological organisms may be used. Such databases may be based upon any type of element, including databases of proteins, genes, pathway maps, xenobiotics, and interactions. Examples of databases include BIND, DIP, the Human Protein Reference Database (HPRD), MetaCore, MINT, HomoMINT, MIPS, PathArt, Pathways Analysis, BIOCarta, Gene Ontology, GenMAPP, and KEGG. Additionally, data mining packages may be utilized. The drug discovery system may use the data as provided by the databases or additional manual or automated processing is performed to further parse the data.
The summary of a database is listed in Table 1. Table 1 lists examples of known enzymes and the number of known molecules that are associated with each enzyme. An associated molecule may be an input chemical, a metabolite, an enzyme regulator, or any other molecule that has an action on or is acted upon by the enzyme. A database associated with the summary in Table 1 may also include the actual names, structure, and/or properties of the associated molecules with each enzyme.
Components, Transformations, and Effects may be grouped into Functional Blocks, which are functional units, be it a particular category of metabolism or any other functional process. Thus, Functional Blocks link together Components, Effects and Transformations that are functionally related. Functional Blocks are hierarchical as they may contain other Functional Blocks as elements. Additionally, every element may be a part of more than one block. Therefore, Functional Blocks are linked to each other by shared elements. Assembling different elements within Functional Blocks enables rapid search of functional links and function-centered analysis of expression and other high-throughput molecular data. Functional blocks may be provided by the databases providing information on the three elements but preferably are generated specifically for or by the drug discovery software.
In
The chemical may also be input using a chemical structure format including sdf and mol files or through a structure drawing program such as ChemDraw (CambridgeSoft Corporation, Cambridge, Mass.). If only the chemical structure is input, the drug discovery software may determine or assign a chemical identifier to the molecule.
A scientist may interact with the drug discovery system using wireless or line telephone with display, handheld device, kiosk, or computer. For example, a scientist may operate a computer system that has an Internet-enabled interface (e.g., using Macromedia Flash or Java) and the computer system may display streamed information within that interface. It should be appreciated that any interface may be used to interact with the drug discovery system and that the invention is not limited to any particular interface. Depending upon the medium used for interaction with the drug discovery system, it may be necessary to download information or executable subprograms prior to interacting with the drug discovery system while another medium may allow continuous interaction with the drug discovery system without such downloads.
As used herein, a “database” is an arrangement of data defined by computer-readable signals. These signals may be read by a computer system, stored on a medium associated with a computer system (e.g., in a memory, on a disk, etc.) and may be transmitted to one or more other computer systems over a communications medium such as, for example, a network. Also as used herein, a “user interface” or “UI” is an interface between a human user and a computer that enables communication between a user and a computer. Examples of UIs that may be implemented with various aspects of the invention include a graphical user interface (GUI), a display screen, a mouse, a keyboard, a keypad, a track ball, a microphone (e.g., to be used in conjunction with a voice recognition system), a speaker, a touch screen, a specialized controller (e.g., a joystick), a track pad, etc., and any combinations thereof.
At step 104, the input molecule is then processed through the drug discovery system using predetermined rules or QSAR models to predict the first-level metabolites of the chemical. Examples of some of the predetermined rules or QSAR models are given in Table 2. At step 104, the drug discovery system may also provide a statistical probability that each predicted metabolite will occur. At step 104, there may be a number of readily interpretable molecular descriptors that are calculated for the input molecule such as the number of rotatable bonds, hydrogen bond acceptors and hydrogen bond donors. The input molecule may also be processed through rules developed specifically to predict likely reactive metabolites (such as quinones, aromatic and hydroxylamines, acyl glucuronides, acyl halides, epoxides, thiophenes, furans, phenoxyl radicals, phenols and aniline radicals) and readily highlight these for the user. At step 104, the predicted QSAR values can be filtered with user defined cutoff values; these values may also be used to prioritize metabolites.
At step 106, the drug discovery system or the scientist may then determine which metabolites to continue to process through the drug discovery system. The determination of which metabolites to continue processing may be based upon the statistical probability mentioned above, on instinct, on experimental data, or on any other criteria. The metabolites still remaining after stop 106 may then each be run back through steps 104 and 106. Steps 104 and 106 may occur as many times as desired; more iterations of steps 104 and 106 predict higher and higher-level metabolites.
At step 112, the drug discovery system processes the initial chemical and all the predicted metabolites left in the model and predicts the total effect of the initial chemical on the biological organism (e.g., homo sapiens) being studied. The total effect is determined after considering protein pathways, gene regulation, enzyme regulation or any biological interaction. The drug discovery system may automatically determine which interactions to consider or a scientist may make choices.
At step 112, the drug discovery system may also graphically represent the effect of the chemical and its metabolites on the biological organism. Due to the complexity of biological organisms, a legend may be necessary to differentiate the various elements of the map;
At step 112, the drug discovery system may also provide the input chemical, its predicted metabolites, statistics, and any other information as text, a worksheet (e.g., for Microsoft Excel), or as a new database to the user.
In
With the HT data analysis, step 112 can also be used to compare the predicted metabolic pathway for the molecule input in step 102 with the actual data generated in step 108.
A predicted biological network for Iressa, an anti-cancer drug is represented in
After evaluation of the results from step 112, the computer or the scientist may then select the most promising chemical compounds that affects or regulates the biological organism as desired. With the appropriate parameters on the biological map, the promising chemical compounds may be also be chosen that create the lowest amount of side or deleterious effects.
A drug discovery system may have any combination (including a few, some, many, or all) of the following features.
A set of databases, possibly having an Oracle-based architecture;
Software tools;
The drug discovery system, and components thereof such as the databases and software tools, may be implemented using software (e.g., C, C#, C++, Java, or a combination thereof), hardware (e.g., one or more application-specific integrated circuits), firmware (e.g., electronically programmed memory), or any combination thereof. One or more of the components of the drug discovery system may reside on a single computer system (e.g., the data mining subsystem), or one or more components may reside on separate, discrete computer systems. Further, each component may be distributed across multiple computer systems, and one or more of the computer systems may be interconnected.
Further, on each of the one or more computer systems that include one or more components of the drug discovery system, each of the components may reside in one or more locations on the computer system. For example, different portions of the components of the drug discovery system may reside in different areas of memory (e.g., RAM, ROM, disk, etc.) on the computer system. Each of such one or more computer systems may include, among other components, a plurality of known components such as one or more processors, a memory system, a disk storage system, one or more network interfaces, and one or more busses or other internal communication links interconnecting the various components.
The drug discovery system may be implemented on a computer system described below in relation to
The drug discovery system described above is merely an illustrative embodiment of a drug discovery system. Such an illustrative embodiment is not intended to limit the scope of the invention, as any of numerous other implementations of a drug discovery system, for example, variations of the databases contained within, are possible and are intended to fall within the scope of the invention. None of the claims set forth below are intended to be limited to any particular implementation of the drug discovery system unless such claim includes a limitation explicitly reciting a particular implementation.
Various embodiments according to the invention may be implemented on one or more computer systems. These computer systems may be, for example, general-purpose computers such as those based on Intel PENTIUM-type processor, Motorola PowerPC, Sun UltraSPARC, Hewlett-Packard PA-RISC processors, or any other type of processor. It should be appreciated that one or more of any type computer system may be used to partially or fully automate play of the described game according to various embodiments of the invention. Further, the software design system may be located on a single computer or may be distributed among a plurality of computers attached by a communications network.
A general-purpose computer system according to one embodiment of the invention is configured to perform any of the described drug discovery system functions. It should be appreciated that the system may perform other functions, including network communication, and the invention is not limited to having any particular function or set of functions.
For example, various aspects of the invention may be implemented as specialized software executing in a general-purpose computer system 400 such as that shown in
The storage system 406, shown in greater detail in
The computer system may include specially-programmed, special-purpose hardware, for example, an application-specific integrated circuit (ASIC). Aspects of the invention may be implemented in software, hardware or firmware, or any combination thereof. Further, such methods, acts, systems, system elements and components thereof may be implemented as part of the computer system described above or as an independent component.
Although computer system 400 is shown by way of example as one type of computer system upon which various aspects of the invention may be practiced, it should be appreciated that aspects of the invention are not limited to being implemented on the computer system as shown in
Computer system 400 may be a general-purpose computer system that is programmable using a high-level computer programming language. Computer system 400 may be also implemented using specially programmed, special purpose hardware. In computer system 400, processor 403 is typically a commercially available processor such as the well-known Pentium class processor available from the Intel Corporation. Many other processors are available. Such a processor usually executes an operating system which may be, for example, the Windows 95, Windows 98, Windows NT, Windows 2000 (Windows ME) or Windows XP operating systems available from the Microsoft Corporation, MAC OS System X available from Apple Computer, the Solaris Operating System available from Sun Microsystems, or UNIX available from various sources. Many other operating systems may be used.
The processor and operating system together define a computer platform for which application programs in high-level programming languages are written. It should be understood that the invention is not limited to a particular computer system platform, processor, operating system, or network. Also, it should be apparent to those skilled in the art that the present invention is not limited to a specific programming language or computer system. Further, it should be appreciated that other appropriate programming languages and other appropriate computer systems could also be used.
One or more portions of the computer system may be distributed across one or more computer systems (not shown) coupled to a communications network. These computer systems also may be general-purpose computer systems. For example, various aspects of the invention may be distributed among one or more computer systems configured to provide a service (e.g., servers) to one or more client computers, or to perform an overall task as part of a distributed system. For example, various aspects of the invention may be performed on a client-server system that includes components distributed among one or more server systems that perform various functions according to various embodiments of the invention. These components may be executable, intermediate (e.g., IL), or interpreted (e.g., Java) code that communicates over a communication network (e.g., the Internet) using a communication protocol (e.g., TCP/IP).
It should be appreciated that the invention is not limited to executing on any particular system or group of systems. Also, it should be appreciated that the invention is not limited to any particular distributed architecture, network, or communication protocol.
Various embodiments of the present invention may be programmed using an object-oriented programming language, such as SmallTalk, Java, C++, Ada, or C# (C-Sharp). Other object-oriented programming languages may also be used. Alternatively, functional, scripting, and/or logical programming languages may be used. Various aspects of the invention may be implemented in a non-programmed environment (e.g., documents created in HTML, XML or other format that, when viewed in a window of a browser program, render aspects of a graphical-user interface (GUI) or perform other functions). Various aspects of the invention may be implemented as programmed or non-programmed elements, or any combination thereof.
Having now described some illustrative embodiments of the invention, it should be apparent to those skilled in the art that the foregoing is merely illustrative and not limiting, having been presented by way of example only. Numerous modifications and other illustrative embodiments are within the scope of one of ordinary skill in the art and are contemplated as falling within the scope of the invention. In particular, although many of the examples presented herein involve specific combinations of method acts or system elements, it should be understood that those acts and those elements may be combined in other ways to accomplish the same objectives. Acts, elements and features discussed only in connection with one embodiment are not intended to be excluded from a similar role in other embodiments. Further, for the one or more means-plus-function limitations recited in the following claims, the means are not intended to be limited to the means disclosed herein for performing the recited function, but are intended to cover in scope any means, known now or later developed, for performing the recited function.
As used herein, whether in the written description or the claims, the terms “comprising”, “including”, “containing”, “characterized by” and the like are to be understood to be open-ended, i.e., to mean including but not limited to. Only the transitional phrases “consisting of” and “consisting essentially of”, respectively, shall be closed or semi-closed transitional phrases, as set forth, with respect to claims, in the United States Patent Office Manual of Patent Examining Procedures (Eighth Edition 2nd Revision, May 2004), Section 2111.03.
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.
This application claims priority under U.S.C. §119(e) to U.S. Provisional Patent Application No. TBD filed on Mar. 17, 2005, by Nikolskaya et al., entitled “SYSTEM AND METHOD FOR PREDICTION OF DRUG METABOLISM AND TOXICITY OF NOVEL SMALL MOLECULE COMPOUNDS”.
| Number | Date | Country | |
|---|---|---|---|
| 60662699 | Mar 2005 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 11378928 | Mar 2006 | US |
| Child | 12749429 | US |
