Abstract: A method for executing multiple chemical programs in parallel in an array of chemical reactors using a single array of substance containers may be provided. The method includes receiving a plurality of chemical programs, building a plurality of records comprising each a chemical program. Thereby, each record includes a key and a data field, wherein the key is indicative of the reactants required for the respective chemical reaction, and wherein the data field includes the chemical program. The method further includes creating an ordered data structure of the data records based on the keys, selecting a next record from the ordered data structure, assigning the selected next record to selected ones of the array of chemical reactors, repeating the steps of selecting and assigning until, as a maximum, each chemical reactor has a defined record assigned to it, and executing the chemical programs according to their defined records in parallel.

Abstract: A method for executing multiple chemical experiments in parallel may be provided. The method comprises receiving a list of actions to be performed for synthesizing a chemical product. Thereby, the actions correspond to at least two chemical partial reactions and the list comprises a delimiter symbol separating two chemical partial reactions, determining identical chemical partial reactions, and building a reaction commonality tree (RCT) of the chemical reactions. Furthermore, the method comprises executing a plurality of the identical chemical partial reactions independent of a sequence of chemical partial reactions of the reaction commonality tree only once. Each of the identical chemical partial reactions is executed in a different chemical reactor and each resulting intermediate product has a quantity of the sum of the related identical chemical partial reactions.

Abstract: Disclosed is a method for predicting at least one aspect of an enzymatically catalyzed chemical reaction. The method comprises providing a trained machine learning model, and inputting one or two input strings into the training model. Each input string is selected from a group of strings consisting of: a string representation of at least one educts of the chemical reaction, a string representation of at least one product of the chemical reaction, and/or a string representation of amino acids of an enzyme which is supposed to transform the educts into the products in the reaction. The trained machine learning model predicts at least the one or more strings which were not provided as input and the prediction is performed as a function of the one or two strings provided as input. The method outputting the prediction result for predicting or optimizing the chemical reaction.

Abstract: Disclosed are compounds having the formula: wherein R1, R2, and R3 are as defined herein, and methods of making and using the same.

Abstract: The present application pertains to methods of using indole-oxadiazole compounds of Formula I to modulate cannabinoid receptor activity: I In particular diseases, disorders or conditions that benefit from modulating cannabinoid receptor activity, such as non-alcoholic steatohepatitis (NASH), non-alcoholic fatty liver disease (NAFLD), schizophrenia, bipolar disorder, psychosis, metabolic syndrome, type-2 diabetes, dyslipidaemia, obesity, eating disorders, cardiovascular diseases and disorders, and other conditions as described herein, may be treated. Also included in the present application are certain novel compounds of Formula Ia and pharmaceutical compositions comprising these compounds.

Abstract: A method for executing multiple chemical programs in parallel in an array of chemical reactors using a single array of substance containers may be provided. The method includes receiving a plurality of chemical programs, building a plurality of records comprising each a chemical program. Thereby, each record includes a key and a data field, wherein the key is indicative of the reactants required for the respective chemical reaction, and wherein the data field includes the chemical program. The method further includes creating an ordered data structure of the data records based on the keys, selecting a next record from the ordered data structure, assigning the selected next record to selected ones of the array of chemical reactors, repeating the steps of selecting and assigning until, as a maximum, each chemical reactor has a defined record assigned to it, and executing the chemical programs according to their defined records in parallel.

Abstract: A method for executing multiple chemical experiments in parallel may be provided. The method comprises receiving a list of actions to be performed for synthesizing a chemical product. Thereby, the actions correspond to at least two chemical partial reactions and the list comprises a delimiter symbol separating two chemical partial reactions, determining identical chemical partial reactions, and building a reaction commonality tree (RCT) of the chemical reactions. Furthermore, the method comprises executing a plurality of the identical chemical partial reactions independent of a sequence of chemical partial reactions of the reaction commonality tree only once. Each of the identical chemical partial reactions is executed in a different chemical reactor and each resulting intermediate product has a quantity of the sum of the related identical chemical partial reactions.

Abstract: A computer-implemented method for generating an organic synthesis procedure from a simplified molecular-input line-entry system (SMILES) string may be provided. The method includes receiving a plurality of SMILES strings describing a desired chemical product and required reactants, and predicting procedure steps for an organic synthesis procedure for producing the desired chemical product by a deep machine-learning model system trained with sets of SMILES strings describing respective desired chemical products, reactants and related procedure steps as training data. The sets can be extracted from a corpus of associated chemical documents, and the predicted procedure steps are human readable. The method includes further receiving a modification signal for a modification to the predicting procedure steps, storing the plurality of received SMILES strings, the predicted procedure steps and the modification of the predicting procedure steps.

Abstract: Disclosed are compounds having the formula: wherein R1, R2, and R3 are as defined herein, and methods of making and using the same.

Abstract: Method(s), apparatus and system(s) are provided for generating and using an ensemble model. The ensemble may be generated by training a plurality of models based on a plurality of datasets associated with compounds; calculating model performance statistics for each of the plurality of trained models; selecting and storing a set of optimal trained model(s) from the trained models based on the calculated model performance statistics; and forming one or more ensemble models, each ensemble model comprising multiple models from the set of optimal trained model(s). The ensemble model may be used by retrieving the ensemble model and inputting, to the ensemble model, data representative of one or more labelled dataset(s) used to generate and/or train the model(s) of the ensemble model; and receiving, from the ensemble model, output data associated with labels of the one or more labelled dataset(s).

Abstract: Disclosed are compounds having the formula: wherein R1, R2, and R3 are as defined herein, and methods of making and using the same.

Abstract: Disclosed are compounds having the formula: wherein R1, R2, and R3 are as defined herein, and methods of making and using the same.

Abstract: Disclosed are compounds having the formula (I): wherein R1, R2, and R3 are as defined herein, and methods of making and using the same.

Abstract: Disclosed are compounds having the formula: wherein R1 and R2 are as defined herein, and methods of making and using the same.

Abstract: The invention is directed to substituted pyrrolidinone and imidazolidinone derivatives. Specifically, the invention is directed to compounds according to Formula I: wherein R1, R2, R3, R4, R5, R6, R7, X and Y are as defined herein.

Abstract: Disclosed are compounds having the formula (I): wherein R1, R2, and R3 are as defined herein, and methods of making and using the same.

Abstract: The present invention relates to compounds of the formula (I), salts thereof, to pharmaceutical compositions containing them and their use in medicine. In particular, the invention relates to compounds as activators of AMPK.

Abstract: The present invention relates to compounds of the formula (I), salts thereof, to pharmaceutical compositions containing them and their use in medicine. In particular, the invention relates to compounds as activators of AMPK.

Abstract: The present invention relates to compounds of the formula (I), salts thereof, to pharmaceutical compositions containing them and their use in medicine. In particular, the invention relates to compounds as activators of AMPK.

Abstract: The present invention relates to pyrimidinedione compounds of formula (I), salts thereof, to pharmaceutical compositions containing them and their use in medicine. In particular, the invention relates to compounds of formula (I) or salts thereof as activators of AMPK.