An analysis method of crystal growth conditions includes a step of calculating an evaluation function on the basis of results obtained by measuring crystals grown under varied crystal growth conditions, a step of performing machine learning of the evaluation function, and a step of obtaining optimum crystal growth conditions from a result of the machine learning, wherein the evaluation function is based on a difference between crystal quality data of an ideal crystal and crystal quality data of the crystal having been grown.

Methods and systems are disclosed in which an automated or semi-automated laboratory may be combined with a machine learning methodology to enable predicting outcomes of chemical reactions or to predict reaction conditions. The model may be trained on reactions including data from the laboratory, purposefully selected to satisfy a desired goal by a user. The user can interact with the process and the model via dedicated user interfaces designed to enable efficient user-machine interaction. The method can be used in the context of multiple challenging problems in chemistry such as steering an automated chemistry laboratory, synthesizing a large collection of compounds such as DNA encoded library, or recommending high yielding reaction conditions for reactions involving drug-like compounds.

Methods and systems are disclosed in which an automated or semi-automated laboratory may be combined with a machine learning methodology to enable predicting outcomes of chemical reactions or to predict reaction conditions. The model may be trained on reactions including data from the laboratory, purposefully selected to satisfy a desired goal by a user. The user can interact with the process and the model via dedicated user interfaces designed to enable efficient user-machine interaction. The method can be used in the context of multiple challenging problems in chemistry such as steering an automated chemistry laboratory, synthesizing a large collection of compounds such as DNA encoded library, or recommending high yielding reaction conditions for reactions involving drug-like compounds.

Disclosed are high-pressure polymerization methods and systems using optimized operation sequence logic established at least partly from an analysis of a database containing data of previous operations. The optimized operation sequence logic and collected current process and system data are used to automate the operation of a high pressure ethylene polymerization process and unit.

Disclosed are high-pressure polymerization methods and systems using optimized operation sequence logic established at least partly from an analysis of a database containing data of previous operations. The optimized operation sequence logic and collected current process and system data are used to automate the operation of a high pressure ethylene polymerization process and unit.

A method of interactively navigating a user through a path of menu choices on a user interface may include displaying a current menu of choices on a first portion of a user interface display. The user interface allows for selecting of a menu item from the current menu of choices and to drill down through levels of menu choices based on selecting a menu item from a prior level of menu choices. A second portion of the user interface display presents past selected and past unselected menu items of the drilled-down levels. The past unselected menu items are displayed as selectable options. The user interface allows for jumping to a different path of menu choices by selecting a past unselected menu item from a previously navigated menu level displayed on the second portion of the user interface display.

A method of interactively navigating a user through a path of menu choices on a user interface may include displaying a current menu of choices on a first portion of a user interface display. The user interface allows for selecting of a menu item from the current menu of choices and to drill down through levels of menu choices based on selecting a menu item from a prior level of menu choices. A second portion of the user interface display presents past selected and past unselected menu items of the drilled-down levels. The past unselected menu items are displayed as selectable options. The user interface allows for jumping to a different path of menu choices by selecting a past unselected menu item from a previously navigated menu level displayed on the second portion of the user interface display.

The present disclosure relates to polypeptide degradation, and in particular to techniques for predicting the likelihood that a peptide bond for a given polypeptide molecule is susceptible to a cleavage reaction. Particularly, aspects of the present disclosure are directed to generating a representation of a polypeptide, performing a molecular-dynamics simulation using the representation to obtain a set of polypeptide conformations, determining, for each polypeptide conformation, a spatial characteristic of an amino acid, estimating a nucleophilic attack distance of each polypeptide conformation based on the spatial characteristic, identifying a reactive conformation that is susceptible to a cleavage reaction based on the nucleophilic attack distance of each polypeptide conformation, determining a free energy of the spatial characteristic of the amino acid in the reactive conformation; and predicting a probability of the side chain of the amino acid being trapped in the reactive conformation based on the free energy.

The method includes: providing a computer system in which are stored phenomenological models of evolution of a quantitative attribute value, each model having between three and nine parameters, including an initial quantitative attribute value parameter, said computer system including an equation resolution tool for computing an experimental stability data set for finding for each said model best fitting values for its parameters, an estimator production tool for finding for each model estimators including a physico-chemical parameter likelihood estimator and a fit distance, respectively based on a comparison between initial quantitative attribute values in the experimental data set and the best fitting value found for said model for the initial quantitative attribute value parameter, and on a comparison between the values in the experimental data set and the corresponding values given by said model; selecting as law of evolution of the quantitative attribute value a best model amongst said models based on the found estimators; determining said preservation state using the law of evolution.

The method includes: providing a computer system in which are stored phenomenological models of evolution of a quantitative attribute value, each model having between three and nine parameters, including an initial quantitative attribute value parameter, said computer system including an equation resolution tool for computing an experimental stability data set for finding for each said model best fitting values for its parameters, an estimator production tool for finding for each model estimators including a physico-chemical parameter likelihood estimator and a fit distance, respectively based on a comparison between initial quantitative attribute values in the experimental data set and the best fitting value found for said model for the initial quantitative attribute value parameter, and on a comparison between the values in the experimental data set and the corresponding values given by said model; selecting as law of evolution of the quantitative attribute value a best model amongst said models based on the found estimators; determining said preservation state using the law of evolution.