This disclosure relates to an analysis method for analyzing a chemical reaction from one or more starting materials, comprising a preparation step S1 of preparing a reaction network diagram indicating the one or more starting materials, at least part of one or more intermediate products and one or more final products generated from the chemical reaction, and reaction pathways of the chemical reaction, and a prediction step S2 of predicting the reaction rate in each reaction pathway using an artificial intelligence algorithm.

A system, device, and method for predicting an active set of compounds that bind to a biomolecular target is disclosed. The system and device contain modules allowing for the prediction of an active set of compounds. A core identification module can identify the core of an initial lead compound. A core hopping module is used to identify potential lead compounds having different cores compared to the core of an initial lead compound. A scoring module can use computational techniques to calculate the relative binding free energy of each identified potential lead compound. An activity prediction module can use the relative binding free energy calculations to predict an active set of compounds that bind to the biomolecular target. Empirical analysis can be used to inform the accuracy and completeness of the predicted active set of compounds.

A system, device, and method for predicting an active set of compounds that bind to a biomolecular target is disclosed. The system and device contain modules allowing for the prediction of an active set of compounds. A core identification module can identify the core of an initial lead compound. A core hopping module is used to identify potential lead compounds having different cores compared to the core of an initial lead compound. A scoring module can use computational techniques to calculate the relative binding free energy of each identified potential lead compound. An activity prediction module can use the relative binding free energy calculations to predict an active set of compounds that bind to the biomolecular target. Empirical analysis can be used to inform the accuracy and completeness of the predicted active set of compounds.

Disclosed is a method for classifying monitoring results from an analytical sensor system (20), by allowing (100) a first set of analyte sample solutions to interact with a ligand (3) and acquiring (101) a set of response data, extracting (102) at least one interaction parameter from the response data, and for each analyte sample solution providing (103) a trained machine learning algorithm with the interaction parameter(s). The trained machine learning algorithm classifies (104) each analyte sample solution based on the interaction parameter(s) into at least one quality classification group indicative of the interaction of the analyte sample solution with the ligand (3). The machine learning algorithm is trained (200) using a set of interaction parameters extracted from response data obtained from interactions between a second set of analyte sample solutions with at least one ligand (3), and at least one quality classification group indicative of the interaction of the analyte sample solution with the ligand (3).

A method and system for calculating the free energy difference between a target state and a reference state. The method includes determining one or more intermediate states using a coupling parameter, performing molecular simulations to obtain ensembles of micro-states for each of the system states, and calculating the free energy difference by an analysis of the ensembles of micro-states of the system states. The method can be particularly suited for calculating physical or non-physical transformation of molecular systems such as ring-opening, ring-closing, and other transformations involving bond breaking and/or formation. A soft bond potential dependent on a bond stretching component of the coupling parameter and different from the conventional harmonic potential is used in the molecular simulations of the system states for the bond being broken or formed during the transformation.

A storage medium storing a program that causes a computer to execute a process that includes acquiring a first model that is trained based on training data which indicates a first combination of constituent values of a target object and an environmental value in an experiment on the target object with associating with a characteristic value and that specifies a mean value and a deviation value of the characteristic value; acquiring a second model that is trained based on training data which indicates a second combination of the constituent values and an allowable condition for the experiment, and that specifies the allowable condition; and generating a solution set for the first combination by performing multi-objective optimization by a penalty term based on the allowable condition, a first objective function, and a second objective function.

The present invention provides a method for forming a history of natural gas accumulation by using carbon isotopes by a pyrolysis experiment. The method includes: obtaining activation energy distribution and a frequency factor of light carbon methane; carrying out carbon isotope kinetics simulation of natural gas in a study area by using a spreadsheet function of Excel to obtain activation energy, a mass fraction and a frequency factor of heavy carbon methane; establishing a burial history and a thermal history of the study area based on geological data; and combining the activation energy distribution and frequency factor of the heavy carbon methane with the burial history and thermal history of the study area, and establishing an instantaneous curve, a cumulative curve and a stage cumulative curve of natural gas under geological conditions on a geologic time scale.

A method of recycling aluminum alloy wheels, the method may comprise (a) providing a feed of aluminum alloy wheels; (b) fragmenting the aluminum alloy wheels into a plurality of fragments; (c) shot blasting the plurality of fragments to at least partly remove at least one contaminant material; (d) storing at least one recognition criterion and at least one carbon factor; (e) for each fragment of a representative sample of fragments, determining, for each contaminant material, a contaminant material concentration estimate for that fragment; (f) operating a data processor to either approve or reject the plurality of fragments, based on an aggregate carbon emission calculation being based on the contaminant material concentration estimate and the carbon factor. When the plurality of fragments is approved, they may be provided to a downstream recycling process. When the plurality of fragments is rejected, they may be further shot blasted.

A method of recycling aluminum alloy wheels, the method may comprise (a) providing a feed of aluminum alloy wheels; (b) fragmenting the aluminum alloy wheels into a plurality of fragments; (c) shot blasting the plurality of fragments to at least partly remove at least one contaminant material; (d) storing at least one recognition criterion and at least one carbon factor; (e) for each fragment of a representative sample of fragments, determining, for each contaminant material, a contaminant material concentration estimate for that fragment; (f) operating a data processor to either approve or reject the plurality of fragments, based on an aggregate carbon emission calculation being based on the contaminant material concentration estimate and the carbon factor. When the plurality of fragments is approved, they may be provided to a downstream recycling process. When the plurality of fragments is rejected, they may be further shot blasted.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing retrosynthesis using a neural network. One of the methods includes generating a prediction of a set of a plurality of predicted reactants that are combinable to generate a target compound, the generating comprising processing, for each of a plurality of candidate sets of reactants, a network input characterizing the candidate set using a neural network, determining, for each candidate set of the plurality of candidate sets, a score using the generated probabilities; and selecting a particular candidate set of one or more reactants using the determined scores.