Patent Application
20250165676
The disclosure of the present patent application relates to quick methods and systems for calculating, estimating, or predicting average electron densities of molecules, or for different parts of molecules. The molecules having the desired predicted AED values can then by synthesized for further use.
In general, various chemical and other physical properties of various molecules can be classified by numerous tools. For example, methods of using electron density to quantify the mean positions of atoms in chemical compounds, their chemical bonds, and other information are currently generally known.
Creating and synthesizing certain new molecules or conformers of molecules with specific properties and reactivities is possible via the Average Electron Density (AED) tool. AED values are calculated using quantum simulations and post-optimization quantum simulations to partition the molecular space into atomic spaces. Quantum simulations are often referred to as ab-initio methods as they calculate all properties from first principles. Because the properties of an atom do not depend only on the atom itself, but also its environment, the same element can have drastically different properties depending on its environment, in other words, the surrounding atoms. Quantum methods and tools such as the quantum theory of atoms in molecules (QTAIM), by constructions, capture these changes, and this is one of the reasons for their accuracy. The drawback of quantum simulations is that they are very computationally expensive, and there is a need to find ways to compute AEDs more efficiently, yet accurately.
In this regard, the average electron density (AED) tool is based on the partitioning of a molecule into atomic basins using the quantum theory of atoms in molecules (QTAIM) partitioning scheme. The starting point of QTAIM is the topological analysis of the molecular electron-density distributions to extract atomic and bond properties that characterize every atom and bond in the molecule. These atomic and bond properties have considerable potential as bases for the construction of robust quantitative structure-activity/property relationships models. QTAIM is applicable to the electron density calculated from quantum-chemical calculations and/or that obtained from ultra-high resolution x-ray diffraction experiments followed by non-spherical refinement.
There are a number of publications that use quantum software and computational simulations to compute Average Electron Density (AED) values of various molecules, or of various parts of molecules. These include:
However, there are no currently known tools capable of quickly estimating and predicting average electron densities of different molecules to assist in selecting most likely molecules to have specific desired utility for synthesis and further analysis. Thus, a new tool solving these problems is desired.
The methods and systems described herein relate to a new quick method that can accurately estimate Average Electron Densities (AEDs) of molecules of interest. These methods stand in stark contrast to the currently available and used methods for estimating AEDs, which can involve running a large number of simulations to collect quantum AED properties for each atom of the molecules of interest depending on each atom's identity and chemical bonds of the atom and its surrounding atoms. Instead, the present methods and systems tabulate properties for all atoms in one or more molecules of interest in all their different environments These tabulated values can then be used to predict the AED of any molecule made of the atoms in the table(s), and they can be used to synthesize molecules with specific desired AEDs for any desired application, including, but not limited to, drug design, environmental studies, engineering, material sciences, medicine, vaccines, molecular chemistry and biochemistry, food additives, beverages, cosmetics, perfumeries, quantitative structure-activity relationships (QSAR), etc. The method has a high accuracy of around or about 95%.
The present methods and systems can be used to predict new drug molecules. They can also be used to estimate the quantum AED properties of drug moieties or any molecule (in part or fully) without having to run quantum simulations.
In an embodiment, the present subject matter relates to a method for quickly and accurately determining an Average Electron Density (AED) of a molecule or a part of a molecule, the method comprising:
In an embodiment, following this last step, a further process or method can include matching the average electron density (AED) of the atoms of the one or more further molecules or the part of the one or more further molecules to an average electron density of atoms in a molecule or a part of a molecule having a known desired activity to identify a matching further molecule or a matching part of the further molecule.
In certain embodiments in this regard, once the average electron density (AED) is obtained, calculated, determined, and/or generated for the one or more further molecules, or the one or more parts of the one or more further molecules, this AED of the atoms of the further molecule or the part of the further molecule can be matched with a known AED of atoms of another molecule or a part of another molecule having a desired chemical, material, pharmaceutical, and/or other property. Once a proper AED match is found, for example, a matching AED within up to 10%, up to 3%, up to 1.5%, up to 1%, or up to 0.5% of the AED of the atoms of the molecule or the part of the molecule having the desired activity, the matched molecule or a molecule having the matched part of the molecule can be created and synthesized for further analysis.
Similarly, the presently described Quick AED tool can be used to construct robust quantitative structure-activity/property relationships models.
In one embodiment, the present subject matter relates to a method for creating and synthesizing a molecule having desired chemical, material, pharmaceutical, or other properties, the method comprising:
In an embodiment, the table of atoms in each molecule in the list of molecules in any of the present methods can have a variety of elements in different environments. For example, if a first molecule in the list has an overlapping element at a given environment with a second molecule, only one entry (one row) would be created in the table. In an embodiment, this one entry can be the average of the two molecular occurrences of the specific element in this example, or the average of more occurrences of the specific element in other examples. Accordingly, for any of the methods described herein, the steps of listing each atom of each molecule in the table of atoms, generating the table of atoms, tabulating the atoms in each molecule, or the like automatically and inherently include taking an average of each overlapping element at a given environment such that only one entry (one row) is created in the table for all of such overlapping elements.
Certain non-limiting examples of the elemental type, also referred to as elements as used herein, include carbon, nitrogen, hydrogen, sulfur, oxygen, halogens, phosphorous, and the like.
In another embodiment, the present subject matter relates to a method for creating and synthesizing a molecule having desired chemical, material, pharmaceutical, or other properties, the method comprising:
In certain embodiments, the present subject matter relates to a method for validating an average electron density (AED) for a molecule, the method comprising:
In certain other embodiments, the present subject matter relates to a method for creating and synthesizing a molecule having desired chemical, material, pharmaceutical, or other properties, the method comprising:
Various embodiments of the present subject matter include combining different portions of the various methods described herein with other portions of the various methods described herein. Such combinations could include, by way of non-limiting example, a portion of one method described herein relating to obtaining the table of atoms with a portion of a different method described herein relating to identifying a matching molecule.
In certain other embodiments, the matching molecule or the matching part of the matching molecule is a bioisostere of the target molecule or the target part of the target molecule.
In a further embodiment of the present methods, those atoms of each molecule of the one or more further molecules matching desired atoms in the table of atoms in each molecule of the list of molecules are selected based on one or more selected from the group consisting of matching average electron densities (AEDs), matching environments, matching volumes, matching electron populations, and combinations thereof. In further embodiments in this regard, the extracted data from the further molecule or the part of the further molecule can be used to select the atoms relevant to the target molecule, i.e., to a bioisosteric molecule or a bioisosteric moiety, or to a molecule having a shared or common activity with the target molecule.
In further embodiments, the present methods relate to methods of selecting, creating and/or synthesizing a molecule having desired chemical, material, pharmaceutical, or other properties comprising selecting a target molecule or a portion of a target molecule having a target average electron density (AED); identifying a matching molecule or a portion of a matching molecule having a combination and/or a ratio of a sum of atoms matching the target average electron density (AED); and obtaining the matching molecule. In this regard, it is important to note that not any combination of atoms that gives a specific AED can be built into a molecule or a part of a molecule. For example, it is important to check that the connectivity of the atoms in the identified matching molecule or matching part of a matching molecule is chemically permitted without violating the rules of chemistry (e.g., the octet rule where the matching molecule cannot have a neutral carbon atom having 5 bonds).
The present methods and systems can thus be used to aid in the development of drug design and many other applications including, but not limited to, materials science applications, the development of chemical probes, determining chemical reactivities, conducting analyses of crystalline structures, synthesizing pairs of matching molecules, predicting matching conformers, creating new molecules with the desired property of the conformer, designing new molecules with matching properties, developing a strategy of matching molecules of pairs to decide on their similarities in properties and reactivities (e.g. toxicity, polarizability), modelling conformers, and applications in computational chemistry, medicine, engineering, pharmaceuticals, material properties, drug design, cosmetics, etc.
In one aspect, the matching as described herein can relate to matching different molecules, or parts of different molecules, having an AED threshold difference of, in different embodiments, up to 10%, up to 3%, up to 1.5%, up to 1%, or up to 0.5%.
In certain embodiments, the calculated average electron densities (AED) values can be calculated as a sum of electron population divided by a sum of volumes, of all atoms in each molecule being studied, or in the part of each molecule being studied.
In this regard, such methods may further comprise screening the matched molecules, or the matched molecule parts, for the desired chemical properties, pharmaceutical properties, or chemical and pharmaceutical properties. For example, the desired pharmaceutical properties may be one or more selected from the group consisting of potency, solubility, permeability, metabolic stability, transporter effects, bioavailability, metabolism, clearance, and toxicity. Similarly, the desired chemical properties may be one or more selected from of the group consisting of mechanical, electrical, thermal, magnetic, optical, and deteriorative properties which may impact surface chemistry as it relates to materials sciences, for example. Impacts on engineering applications are also possible, for the reasons given above.
In certain embodiments, the new methods reported herein can be used to quickly calculate an average electron density for a molecule, or a part of the molecule, irrespective of the application for the method and/or molecule, e.g. finding bioisosteres, matching conformers, finding molecules having a shared or common activity, etc. Further, the present methods can be used to not only quickly obtain an average electron for a molecule, but also to do so without conducting any quantum simulations, thereby saving a lot of money, time, manpower, expertise (even noncomputational scientists can use the table), computer resources, CO2 to the environment, energy, heat, and the like.
These and other features of the present subject matter will become readily apparent upon further review of the following specification.
As used herein, the terms “bioisostere”, “bioisosteres”, “bioisosterism”, “bioisosteric”, and the like are used interchangeably herein to refer to substituents or groups that impart similar biological properties to a chemical compound; for example, the bioisosteric replacement of a hydrogen atom with a fluorine atom within a drug molecule will generally have no influence on the drug's efficacy, but it may prolong the drug's half-life by inhibiting metabolic oxidation. Thus, the purpose of exchanging one bioisostere for another is to fine tune the pharmacokinetic or pharmacodynamic properties of a bioactive compound.
As used herein, molecules having “commonalities” or a “common structure”, one to another, are considered to be essentially homologous to each other. In the alternative, depending on context, the term “common” can be used herein to refer to multiple different molecules having essentially homologous properties, such as pharmaceutical properties, to each other. By way of non-limiting example, if a specific pharmaceutical property of two different molecules is within +/−10% of one another, they would be considered as “common” to one another. In certain embodiments, if the specific pharmaceutical property of two different molecules is within +/−5% of one another, they would be considered as “common” to one another.
As used herein, molecules having calculated AED values that are “equivalent” to or “match” one another means that the respective values are close enough to one another such that a person of ordinary skill in the art would recognize such molecules as likely to share common properties. By way of non-limiting example, if the AED values of two different molecules are within +/−15% of one another, they would be considered as “equivalent” to one another. In certain embodiments, if the AED values of two different molecules are within +/−10, 9, 8, 7, 6, 5, 4, 3, 2, 1.5, 1, or 0.5% of one another, they would be considered as “equivalent” to one another.
The presently described subject matter relate to the efficient, quick, accurate, and reliable identification and matching of different molecules. Accordingly, the present subject matter is directed to a “Quick AED” tool for quickly determining the average electron density (AED) of any molecule, any part of any molecule, or any collection of any grouping of atoms representing a molecule or a part of a molecule.
In one embodiment, the present methods and systems relate to a new “Quick AED” tool which can readily, quickly, and easily make use of the Average Electron Density (AED) to assist in developing and matching different molecules without the need of computational expertise. The activity of a molecule is linked to its chemical structure. Therefore, studying this structure is extremely important. Once the list of elements in a given environment is generated from the list of molecules, they can be used to quickly compare respective AED values, and thus obtain “matching” molecules. Further studies can be done to determine how matched molecules may impact drug design, chemical reactivities, engineering applications, and materials science applications, quantitate structure-activity relationship, among others.
This approach permits the quick identification, classification, evaluation, and matching of average electron density (AED) values of different molecules. Previously, it was only possible to evaluate and classify AED values by using time consuming and difficult quantum simulations. The present tool represents a new highly accurate and specific quantitative method for evaluating, measuring, classifying, and working with AED values to develop, obtain, find, create, and synthesize matching molecules. Accordingly, the present methods can save a lot of time (from a process that takes hours up to days for a small molecule, if not even weeks and months for large complex molecules to a few seconds assuming the environment of the atoms in the new molecules of interest is already available in the generated table of atoms in different environments), money (of hiring experts, to purchase software, to have computing facilities, and to run these machines, this is a reduction of thousands of dollars to nearly no cost), computer resources (does not need more than a simple calculator as opposed to big computing machines or even big High Performance Computing (HPC) infrastructure for large complex molecules), and it heat and CO2 emission reductions (this is a huge savings that can have serious impact on our environment). In addition, this method is simple and can be very easily used by anyone; no quantum expertise nor computational expertise are needed for someone to know how to apply this method to accurately predict quantum properties.
In this regard, the uniqueness of the present methods is in their power to quickly predict AEDs of any molecule in the world, and they are designed in such a way to consistently give accurate predictions. A person employing the new methods presented herein does not need high expertise in computational sciences, in chemistry, in molecular sciences, in quantum theories, nor in using advanced software. The idea is inventive as it presents a new method that offers a lot of advantages.
Overall, the present methods can be used to:
1) speed up the entire process of AED prediction while saving money, CO2, expertise, and time. This is based on the development of a new concept of AED values for elements in different environments.
2) synthesize new drug molecules, as the new molecules can be developed in a minimal amount of time and expertise and then check if their AED is the same as the target molecule. This result can then be used in designing and synthesizing new molecules with desired application in drug design for any disease or even for applications in any other field.
3) target the design and synthesis of a new molecular model of a full molecular structure or partial groups of a molecule that have a specific AED, through the table, in other words generate and design a structure that has a target AED.
4) treatment: this is one example (but this reported method is not limited to this example, and can be applicable in many other treatments), as the present methods can quickly calculate AEDs and use the estimated AEDs to tailor different reactivity and mechanical behavior to develop biocompatible materials in medical devices and tissue engineering.
The average electron density (AED) tool is based on the partitioning of a molecule into atomic basins using the quantum theory of atoms in molecules (QTAIM) partitioning scheme. The starting point of QTAIM is the topological analysis of the molecular electron-density distributions to extract atomic and bond properties that characterize every atom and bond in the molecule. These atomic and bond properties have considerable potential as bases for the construction of robust quantitative structure-activity/property relationships models. QTAIM is applicable to the electron density calculated from quantum-chemical calculations and/or that obtained from ultra-high resolution x-ray diffraction experiments followed by non-spherical refinement.
QTAIM allows the evaluation of properties of atoms in molecules, and subsequently parts of molecules, which is needed in many applications including, but not limited to, e.g., biosiosteric replacements in drug designs, developing structure-activity relationships, and the like. In QTAIM, the zero flux surfaces separate atoms into their own atomic basins. Then by operating different mathematical operators on the atomic basins, followed by numerical integrations, atomic properties of atoms in molecules can be evaluated. These properties include, volumes, charges, electron densities, areas, and average electron densities.
The average electron density (AED) is defined as the total electron population of a group of a molecule or of the full molecule divided by the corresponding volume. The internal interatomic limits between two atoms are determined by the internal zero-flux interatomic surfaces within the molecular interior, and the outer limit is set at the external 0.0004, 0.001, or 0.002 atomic unit isodensity envelope. The volumes and electron populations used to calculate the AED are those defined within Bader's quantum theory of atoms in molecules, a theory that partitions the molecular electron density into separate atomic basins separated by surfaces of zero-flux in the gradient vector field associated with the density. The atomic properties are then obtained by numerical integrations over each atomic basin. The AED properties of a specific molecular group are the sum of the properties of the atoms constituting this group.
The average electron density of a group is given by the formula:
where Ni is the electron population of each atom i, and Vi is the volume of each atom i.
In one embodiment, the wavefunction file obtained from the QM simulation can be further analyzed and processed using AIMAll software, from TK Gristmill Software (Overland Park, KS), based on the QTAIM theory. The AIMAll software package can be used for atomic integrations based on QTAIM. The interatomic basins can be delimited by zero-flux surfaces, and the outer limit of the atomic basins can be defined at three different isodensity envelopes of 0.0004, 0.001, or 0.002 a.u . . . . AIMAll software is typically used for performing quantitative and visual QTAIM analyses of molecular systems, starting from molecular wavefunction data.
Likewise, AED values can be calculated by starting with, for example, a Gaussian software package, such as, for example, Gaussian16, with molecules optimized in the gas phase. In one embodiment, the level of theory used is the B3LYP density functional theory, namely B3LYP/6-311++G (d,p)//B3LYP/6-311++G (d,p) with ultrafine pruned (99,590) grids and ‘tight’ self-consistent field optimization criteria. Vibrational frequency analysis was completed to confirm that the optimized geometries have no imaginary frequencies, in other words, they are not transition states. Here even if other (reasonable) details of the QM simulation are used, they will still give the same result.
In another embodiment, the Hershfield scheme may be used for partitioning the basins of atoms in molecules. The Hirshfeld (1977) method apportions the electron density among the atoms by the appropriate weighting. The weights are related by the atomic contribution to the promolecular density:
The fragment of the density apportioned to atom A is
An alternative scheme is based on the atomic contributions to the total promolecular potential Vpro defined as the sum of the electronic and nuclear contributions.
In one embodiment of the present methods, AED values are determined for the entire molecules being studied. In another embodiment, the AED values are determined for a portion or part of the molecules being studied, i.e., an active group of the molecules being studied.
In certain embodiments, the present subject matter also relates to the use of the present methods in discovering new bioisosteres of a molecule at very fast rates.
By way of non-limiting example, one such embodiment relates to practicing the methods as described herein, wherein the matching molecules, or parts of molecules, are considered as bioisosteres of one another.
Once the present methods are performed, the knowledge can be used to determine which new molecules, or parts of new molecules, specifically would be best suited for drug design. This knowledge can then be extended to other molecules matching a first molecule identified as best suited for drug design. The selected matching molecule(s) can then be synthesized to conduct further analysis on their suitability for drug design.
The present tool can be used to improve lead optimization and drug design by optimizing pharmaceutical properties, improving potency, enhancing specificity, and reducing side effects, all of which can improve the wellbeing of patients, reduce their pain, reduce inconveniences of facing side effects, and save money for the pharmaceutical companies in developing new drugs.
The screening process to determine potential drug candidates can be conducted according to any method known to those of ordinary skill in the art such as, for example, using high-throughput screening arrays. The present methods and systems can speed up what is commonly a long and difficult process by targeting specific conformers of the selected molecules that are more likely to possess a desired activity, removing many of the steps from the typical screening process. The present methods and systems act as a filter to enrich the hit rate compared with typical random screening of conformers of a given molecule, but not different molecules, although the latter is also possible.
Further, the present methods can assist in rapidly identifying potential candidates for treating a specific disease, disorder, or condition. This can be particularly useful when there is an urgent need for treating such a disease, disorder, or condition, for example, when a new disease starts spreading among humans, thus requiring urgent new drugs for treatment. COVID-19 would be one non-limiting example in this regard.
In one example, COX-2 inhibitors are known to be effective as anti-inflammatory drugs. However, may COX-2 inhibitors have been shown to have potential serious side effects and/or safety concerns, causing them to be taken off the market. Rofecoxib is an example of an effective COX-2 inhibitor taken off the market due to such safety concerns, as it was linked to increases in heart attacks and strokes. In contrast, celecoxib is a COX-2 inhibitor that remains available in the US due to relatively diminished safety concerns. The Quick AED tool described herein can be used to distinguish those COX-2 inhibitors that have a combination of suitable effectiveness and a reduced incidence of side effects, i.e., to identify further molecules that behave more like celecoxib than like rofecoxib.
Likewise, the quick AED tool described herein can be used to identify the components responsible for determining whether murine adipose tissue deposits are comprised of cellulose fat or mitochondrial content, and thus can be used to differentiate between molecules targeting the desired type of adipose tissue deposits.
Similarly, in cancer treatment, the efficacy of the methotrexate drug to inhibit dihydrofolate reductase (DHFR) is influenced by the rate of proton tunneling, which itself depends on the conformer of the molecule. The quick AED tool can differentiate between molecules that could or could not have proton tunneling and synthesize ones that have the desired properties.
In another example, inhibition of bacterial RNAP with the Gfh1-CTD inhibitor is reported as anti-bacterial therapies. At different pH, different conformers of Gfh1-CTD are available. The quick AED tool can distinguish all the conformers of Gfh1-CTD that can or cannot inhibit RNAP and synthesize the one that do inhibit. Such learnings can be extended to identify further molecules of interest likely to have such similar properties.
Once the best molecule for the specific drug design is selected, this can then be translated to identifying the matching other molecule which is also likely to be suitable for the drug design. Once either molecule is selected, the molecule can then be produced, synthesized, or the like to conduct further testing under real world conditions.
Once the present methods are performed to identify matching molecules of a given molecule, the knowledge can be used to determine which molecules specifically would be best suited for satisfying certain materials science needs and requirements. This knowledge can then be extended to the other molecules matching the first molecule identified as best suited for satisfying certain materials science needs and requirements. The selected matching molecule(s), or part of molecule(s), can then be synthesized to conduct further analysis on their suitability for satisfying certain materials science needs and requirements under real world conditions.
For one example, selecting a building material based on different molecules can lead to different physical properties of the material build, e.g., due to possibly different electron conductivity (melting properties, etc.). Therefore, finding matching molecules means moving one step forward in grouping molecules that would likely share similar material properties once used in building materials. In this way, both the initially selected molecule and the matching molecule can be physically tested to ensure they share similar and desirable material properties, for example, when producing building materials.
In another example, the present Quick AED tool can be used to determine refractive indices of crystalline amino acid derivatives, focusing on the role of intermolecular interactions and the crystal packing on determining the optical linear properties of organic materials.
Similarly, the topology of the average electron density at the Valence Shell Charge Concentration (VSCC) region of transition metals can be used to correlate the changes of the electronic structure induced by structural phase transitions with the bulk behavior of the materials. Accordingly, the Quick AED tool presented herein can be used in selecting those transition metal materials having certain desired properties.
In another, non-limiting example, the identification of the matching molecule can be employed to maximize the molecule's change in state, malleability, color, resistance, permeability, degradability, breaking points, and the like to match a desired use for the molecule.
Once the present methods are performed to obtain the matching molecules, the knowledge can be used to determine which molecules specifically would be best suited for various chemical reactivities. This knowledge can then be extended to other molecules matching the identified first molecule identified as best suited for various chemical reactivities. The selected matching molecules(s), or parts of molecule(s) can then be synthesized to conduct further analysis on their suitability for various chemical reactivities.
Accordingly, the present tool is extremely helpful for determining which molecules are most likely to successfully complete various chemical reactions. In this regard, both the initially selected molecule and the matching molecule can be physically tested to ensure they share similar and desirable chemical reactivities under real world conditions.
Some of the other uses for the presently described tool include the following:
The presently described subject matter can be further understood by referring to the following example.
A sample table used to generate a quick AED calculation appears as follows in Table 1:
As it appears in Table 1, s, d, and t, stand for single, double, and triple bonds, respectively; C, O, H, etc. are the elements carbon, oxygen, hydrogen, etc, respectively, listed in each environment alphabetically, and std dev is standard deviation associated with the average AED reported per element at a given environment as in the list of molecules as shown in the list of molecules. In this regard, for new molecules, it is sufficient to have for a given element (e.g., C) its direct environment, e.g., (sCsHdO). However, for conformers of molecules, for example, this description of “C” at an environment of first degree neighboring atoms “sCsHdO” is not sufficient; rather, it is necessary to go to the second degree of neighboring atoms including most importantly the non-covalently bounded second-degree neighboring atoms within 1.5 to 5 Angstroms from the first degree neighboring atoms.
A comparison of the quick AED calculation method as described herein vs. the traditional quantum methods of calculating AED is shown in Table 2, below.
It is to be understood that the methods and systems described herein are not limited to the specific embodiments described above, but encompasses any and all embodiments within the scope of the generic language of the following claims enabled by the embodiments described herein, or otherwise shown in the drawings or described above in terms sufficient to enable one of ordinary skill in the art to make and use the claimed subject matter.
