97 results about "Molecular descriptor" patented technology

The invention relates to a method for analysing mass spectral data obtained from a sample in GC×GC (2-dimensional) mass spectrometry, comprising: (a) comparing mass spectral data of an analyte with mass spectral data of candidate compounds of known structure in a data library; (b) identifying a plurality of candidate compounds from the library based on similarities of mass spectral data; (c) predicting, for each candidate compound, a value of at least one analytical property using a quantitative model based on a plurality of molecular descriptors; and (d) calculating a match score for each candidate compound based on the value predicted in step (c) and a measured value of the analytical property for the analyte.

The present application is directed to the use of computerized data processing to plan, perform, and assess the results of high-throughput screening of multicomponent chemical compositions and solid forms of compounds. Systems utilized include databases of molecular descriptors and related compounds and their properties as determined empirically and through simulation, along with multidimensional visualization tools. Methods include methods for determining chemical compositions by performing steps including selecting a plurality of combinations of values of experimental parameters that can be varied by an automated experiment apparatus, determining a set of experimental results, and determining a second plurality of combinations of values based on the set of experimental results. Additional methods include selecting values of parameters that produce a composition, the values being relatively far from areas of rapid change or boundaries between solid forms.

Disclosed is a protein-ligand affinity predicting method based on molecule descriptors. The protein-ligand affinity is reflected through construction of perfect and systematic molecule descriptors, and the relation between the descriptors and the affinity is constructed through a supporting vector regression (SVR) mode. The method includes the steps of training set preparation: preparing a large amount of data containing the crystal structure and the affinity of a protein-ligand complex; construction and calculation of the molecule descriptors: constructing 50 kinds of molecule descriptors which belong to nine categories, and calculating concrete values of all the complex descriptors in the training set; regression model construction: fitting the relation between the descriptors and the affinity through the SVR mode, and introducing a conjugate gradient method to optimize a penalty factor C and a kernel function parameter; and novel scoring function building which is used for predicting the affinity of the complex. The method has the advantages of being high in prediction capacity, small in target dependence, high in homolog sensitivity and the like.

The invention discloses a theoretical prediction method for organic chemical n-octyl alcohol/ air distribution coefficient (KOA) and belongs to the field of ecological risk assessment testing strategy. The method comprises the following steps of: establishing a quantitative structure-activity relationship (QSAR) based on a molecular Dragon descriptor of a compound and calculating free melting energy based on a thermodynamic principle by adopting an open source solvent model, and transforming to obtain the KOA according to a thermodynamic principle formula of logKOA=-deltaGOA/2.303RT. A general strategy of predicting the KOA of the compound is provided based on the method, namely whether the molecule is in the application range is judged according to the Dragon descriptor, if so, a QSAR model is preferentially adopted (QSAR-T is adopted at different temperatures), otherwise, the compound is predicted by adopting an SM8AD solvent model. The method and strategy are adopted and accorded, the KOA of different compounds at different temperatures can be rapidly and effectively predicted, lots of manpower, material resources and financial resources are saved, and important essential data is provided for large-scale ecological risk assessment and environment supervision of chemicals.

The invention discloses a method for adopting a quantitative structure-activity relationship model to predicting soil or sediment adsorption coefficients of an organic compound. On the basis that a molecular structure of the organic compound is known, the soil or sediment adsorption coefficients of the organic compound can be quickly and efficiently predicted only by calculating a molecular descriptor with the molecular structure and applying a built QSAR (quantitative structure-activity relationship) model. The method is simple, quick, low in cost and capable of saving manpower, material resources and financial resources needed for experiment testing. Modeling is performed according to guidelines on building and using the QSAR model of the Organization for Economic Cooperation and Development, and a simple and transparent multiple linear regression analysis method is applied, so that easiness in understanding and applying is realized; the method has clear application domain, good fitting capacity, robustness and predicting capability; by the method, the soil or sediment adsorption coefficients of the organic compound in the application domain can be effectively predicted, and necessary basic data are provided for ecological risk evaluation and management of the compound; the method has important significance.

The invention provides a medicament module pharmacokinetic property and toxicity predicting method based on a capsule network. After a comprehensive module fingerprint and a module descriptor are constructed and early-period preparing operation for establishing model is performed, a low-grade characteristic content of a molecule is extracted from an upper-layer low-grade characterized through convolutional or restricted Boltzmann machine operation; then a capsule network method is used for abstracting the high-grade characteristic of the molecule in a lower-layer high-grade characteristic; a relation between the high-grade characteristic and an active label is fit through a dynamic routing algorithm, thereby predicting the pharmacokinetic property and the toxicity class of an unknown smallmolecule. The method does not require collection of large scale datasets for training, optimization is performed on input through end-to-end and furthermore automatic dimension reduction is realized.A coupling coefficient is updated through iterating a dynamic routing process. The dynamic routing conveys all characteristics of an upper-layer capsule to a random lower-layer capsule, thereby greatly reserving a hierarchical position relation. The method realizes a better predicting effect than that of a traditional machine learning method.

The invention discloses a computer aided design system for predicting energetic molecules based on machine learning performance, and belongs to the technical field of computer aided design systems. The system comprises: a molecule rapid generation module, which is used for generating all possible molecular structural formulas according to permutation and combination of a molecular mother ring anda substituent group input by a user, and removing a repeated structure; a molecular data set module used for recording reported energetic molecular performance data, and the energetic molecular performance data being mainly used as a training set of the machine learning model training module; a molecular descriptor generation module used for calculating molecular descriptors of molecules generatedby the molecular rapid generation module or molecules in the molecular data set module or molecules input by a user; a machine learning model training module used for training and storing a model byadopting a machine learning algorithm according to the data of the molecular descriptor set; and a performance prediction module used for reading the model and carrying out performance prediction on the molecules generated by the molecule rapid generation module or the molecules input by the user.

The invention discloses a method for predicting acute toxicity of an organophosphorous flame retardant on a zebra fish based on QSAR/QEcoSAR method combination and belongs to the technical field of quantitative structure-activity relationship (QSAR) for chemical environment supervision. The method comprises the steps that based on the chemical reaction principle and the minimum energy principle, QSAR and QEcoSAR are combined, and a multivariate linear regression method is adopted to construct a QSAR model of LC50 and training set compound molecule descriptors (electronegativity, chemical hardness, chemical force, eletrophilicity and other characteristics). The method is low in cost and convenient and fast to use, and a large amount of manpower, cost and time needed in experimental testingcan be saved; and by use of the prediction result, important data support can be provided for ecological risk evaluation and management of organophosphorous chemicals, and therefore the method has important realistic significance and theoretical value.

Described herein are nanoparticles which are largely made of (e.g., 90 wt. %) hydrophobic drugs and are stabilized by water soluble dyes. The nanoparticles can range in size from 30 nm to 150 nm and have highly negative surface charge (e.g., −55 mV). These nanoparticles are highly soluble in water, stable for days in PBS buffer and can be easily lyophilzed and reconstituted in water. Using quantitative self-assembly prediction calculations, topochemical molecular descriptors were identified and validated as highly predictive indicators of nano-assembly, nanoparticle size, and drug loading. The resulting nanoparticles selectively targeted kinase inhibitors to caveolin-1-expressing human colon cancer and autochthonous liver cancer models to yield striking therapeutic effects while avoiding pERK inhibition in healthy skin. The nanoparticles exhibited remarkable anti-tumor efficacy in vitro and in vivo in models of hepatocellular carcinoma.

Permeability models and methods for creating the models are disclosed. The models include receiving as an input in vitro permeability and structure data for a particular compound. Then the data is mapped to at least one permeability. In some models the data is mapped to a plurality of permeabilities, each associated with a specific region in a mammalian GI tract. Some models may take into consideration solubility, permeability and at least one molecular descriptor associated with the compound of interest.

The invention discloses a method for predicting inhibition concentration of a cytochrome P450 enzyme CYP1A2 inhibitor by utilizing simplified partial least squares. The method comprises the steps of: 1) collecting, processing and optimizing sample sets; 2) constructing molecular descriptors of the inhibitor; 3) preliminarily screening molecular descriptors of the inhibitor; 4) scaling data sets of molecular descriptors of the inhibitor again; 5), dividing data sets of molecular descriptors of the inhibitor; 6), setting up a QSAR model; and 7) predicting inhibition concentration of the cytochrome P450 enzyme CYP1A2 inhibitor.The method utilizes the method of partial least squares (PLS) to further estimate and simplify constituents of partial least squares (PLS) in the presence of serious multiple correlations of independent variables on the basis of regression modeling and uses the simplified method of partial least squares (SIMPLS) to precisely predict inhibition concentration of the cytochrome P450 inhibitor.

Method for predicting and/or correlating additive structure to engine performance includes developing molecular descriptors for one or more additives, developing a kinetic model for the engine performance using kinetic parameters, determining the relation between the molecular descriptors and the kinetic parameters by testing compositions comprising one or more additives, developing a QSPR library describing the relation between the molecular descriptors of the additives and the kinetic parameters and by predicting the engine performance of other additives using the QSPR library.

The invention belongs to the technical field of compound performance evaluation, and discloses a machine learning estimation method for sensitivity and mechanical properties of energetic substances and a relationship between the sensitivity and the mechanical properties of the energetic substances. The estimation method comprises the following steps: constructing a quantitative structure-activityrelationship model of impact sensitivity and bulk modulus of seven nitro energetic substances based on an artificial neural network and a method for determining independent screening and sparse operators by taking a molecular descriptor and molecular structure information calculated by E-Dragon as characteristics; and determining the relationship between the impact sensitivity and the mechanical property of the nitro energetic substance and the quantitative relationship between the impact sensitivity and the mechanical property of the nitro energetic substances respectively with the molecularstructure by utilizing the constructed quantitative structure-activity relationship model of the impact sensitivity and the bulk modulus of the nitro energetic substances. According to the method, seven QSPR models of the impact sensitivity and the bulk modulus of the nitro nitro energetic substances are established on the basis of molecular descriptors calculated by EDragon and several common molecular structure information, so that the process of experimental research on energetic materials is shortened, and design and comprehensive evaluation of novel energetic compounds are facilitated.

The distribution of chemical compounds in high-dimensional molecular descriptor space can be viewed in two dimensions by applying the projection method of this invention. This method has particular usefulness for viewing the relationships of a large number of compounds such as found in a large scale HTS or virtual combinatorial library. After selecting a representative subset of the larger data set of compounds, initially components from the high-dimensional descriptor space are determined by PCA. In order to relax an NLM projection using the PCA components as a start, the stress function is modified to reflect a local horizon beyound which the separation of the compounds is not meaningfully measureable. The resulting two dimensional projections provide a clear insight into the distribution of the chemical compounds in the higher dimensional space. The method is clearly generalizable to viewing descriptor space in three dimensions and to using high dimensional descriptors other than those used to describe molecular structure.

The invention discloses a method for predicting the water-phase reaction rate constant of organic matter and a sulfuric acid free radical in a water phase through quantitative structure activity relationship. Based on a compound structure, a molecular descriptor with a structural feature is calculated, and by the adoption of a multiple linear regression method, a QSAR model of an organic compound kso4- is constructed. Establishment of the model strictly confirms to the constructing and using guideline of the QSAR model from organization for economic cooperation and development, the constructed model has a definite application field, contains 197 kinds of organic compounds of different structures, and contains compounds containing carbon-carbon double bonds, carbon-carbon triple bonds, a hydroxyl group, phenolic hydroxyl, a carbonyl group, an aldehyde group, a carboxy group, an ester group, an amide group, nitro, an amino group, a cyano-group, an ether bond, a disulfide bond, fluorine, chlorine, bromine, iodine, arsenium and other radical groups. The mode has good fitting capacity, robustness and predicting capacity, can rapidly and accurately predict the kso4- value of the organic compound, and reference is provided for application of an advanced oxidation process based on a sulfuric acid free radical.

The invention discloses a method for constructing a quantitative structure-activity relationship model to predict a silicone oil-air partition coefficient of a hydrophobic compound. According to the method, the model is adopted for predicting the silicone oil-air partition coefficient, wherein logKSiO/A = 2.888 + 0.025 * alpha - 0.244 * (ELUMO-EHOMO), logKSiO/A represents the silicone oil-air partition coefficient, alpha represents an average molecular polarizability, and (ELUMO-EHOMO) represents an energy difference between a lowest unoccupied molecular orbit and a highest occupied molecular orbit. According to the method, the silicone oil-air partition coefficient of the hydrophobic compound can be quickly and efficiently predicted simply by calculating molecular descriptors with structural characteristics and applying the constructed QSAR model; the method is simple, quick and low in cost, and the labor, materials and financial resources needed for experimental tests can be saved.

The invention discloses a method for predicting toxicity of chemicals taking zebra fish embryos as receptors by establishing a QSAR model. On the basis of a known compound molecular structure, the half lethal concentration of the compound taking zebra fish embryos as receptors can be quickly and efficiently predicted only by calculating molecular descriptors with structural characteristics and applying the constructed QSAR model, and the method is simple, quick, low in cost and capable of saving manpower, material resources and financial resources required by experimental testing. According tothe method, modeling is carried out according to the construction of the QSAR model and the use guide rule of the economic cooperation and development organization, and a simple and transparent multiple linear regression analysis method is applied, so the method is easy to understand and apply; the method has a clear application domain and good fitting ability, robustness and prediction ability,can effectively predict the median lethal concentration of the compound in the application domain by taking zebra fish embryos as receptors, provides necessary basic data for ecological risk evaluation and management of the compound, and has important significance.

The use for biological screening purposes of a subset (library) of a large combinatorially accessible chemical universe increases the efficiency of the screening process only if the subset contains members representative of the total diversity of the universe. In order to insure inclusion in the subset of molecules representing the total diversity of the universe under consideration, valid molecular descriptors which quantitatively reflect the diversity of the molecules in the universe are required. A unique validation method is used to examine both a new three dimensional steric metric and some prior art metrics. With this method, the relative usefulness/validity of individual metrics can be ascertained from their application to randomly selected literature data sets. By the appropriate application of validated metrics, the method of this invention selects a subset of a combinatorial accessible chemical universe such that the molecules of the subset are representative of all the diversity present in the universe and yet do not contain multiple members which represent the same diversity (oversample). The use of the neighborhood definition of a validated metric may also be used to combine (without oversampling the same diversity) any number of combinatorial screening libraries.