A computer-implemented method for predicting a conformation of a ligand docked into a protein is disclosed. According to some embodiments, the method may include determining one or more poses of the ligand in the protein, the poses being representative conformations of the ligand. The method may also include determining, using a neural network, energy scores of the poses. The method may further include determining a proper conformation for the docked ligand based on the energy scores.

At least one binding site of a protein is probed by calculating a set of molecular dynamic trajectories of a protein-ligand complex family. At least one script is applied to the molecular dynamic trajectories to form a set of tensors, and at least one second script is applied to the set of tensors to integrate the set of tensors with experimental binding data corresponding to the protein-ligand complex family to form a primary image of the binding site, thereby probing the binding site of the protein.

There is provided a method for a machine learning based method of analysing drug-like molecules by representing the molecular quantum states of each drug-like molecule as a quantum graph, and then feeding that quantum graph as an input to a machine learning system.

There is provided a method for a machine learning based method of analysing drug-like molecules by representing the molecular quantum states of each drug-like molecule as a quantum graph, and then feeding that quantum graph as an input to a machine learning system.

The present invention provides a computer system for generating the molecular structure of a catalytic activator for a reaction in which input reactants, a.k.a. substrates, are converted into an output product, the computer system comprising: a trained machine learning model, preferably a variational autoencoder, configured to receive an operating feature set defining chemical features of the input reactants and chemical features of the output product of a reaction and to generate therefrom a set of catalyst features defining one or more catalytic activator, which is preferably an enzyme, for catalysing a reaction to convert the input reactants to the output product.

The present disclosure provides a method of generating compound information in a computing apparatus, the method including obtaining a learning model for information associated with partial structures, obtaining information associated with a source molecule that is a target of a partial structure modification, obtaining information associated with a partial structure set including a plurality of partial structures of the source molecule, selecting, from the partial structures included in the partial structure set, a target partial structure to be modified, obtaining, using the learning model, information associated with a modified partial structure corresponding to the target partial structure, and outputting result information in which the target partial structure is replaced by the modified partial structure in the source molecule.

The present disclosure provides a method of generating compound information in a computing apparatus, the method including obtaining a learning model for information associated with partial structures, obtaining information associated with a source molecule that is a target of a partial structure modification, obtaining information associated with a partial structure set including a plurality of partial structures of the source molecule, selecting, from the partial structures included in the partial structure set, a target partial structure to be modified, obtaining, using the learning model, information associated with a modified partial structure corresponding to the target partial structure, and outputting result information in which the target partial structure is replaced by the modified partial structure in the source molecule.

A sample analysis device includes an acquirer that acquires quantitative information of a test substance present in a sample, an estimator that reads a generalized reaction model obtained by generalization of a plurality of reaction models from a storage device and estimates a posterior distribution of a parameter of the generalized reaction model using Bayesian inference, and a calculator that calculates a confidence interval or a quantile of the quantitative information of a test substance in any period of time or calculates a confidence interval of a quantile in a period of time until the quantitative information of a test substance reaches a predetermined specification limit, based on the posterior distribution of a parameter estimated by the estimator.

A sample analysis device includes an acquirer that acquires quantitative information of a test substance present in a sample, an estimator that reads a generalized reaction model obtained by generalization of a plurality of reaction models from a storage device and estimates a posterior distribution of a parameter of the generalized reaction model using Bayesian inference, and a calculator that calculates a confidence interval or a quantile of the quantitative information of a test substance in any period of time or calculates a confidence interval of a quantile in a period of time until the quantitative information of a test substance reaches a predetermined specification limit, based on the posterior distribution of a parameter estimated by the estimator.

In one aspect, a method is disclosed for using dialects to generate candidate drug compounds. The dialects describe sequences of the candidate drug compounds and activities associated with the sequences. The method includes receiving a data set, training, using the data set, first layers of a machine learning model to determine relationships of components of a portion of a string described by a first dialect. The components pertain to amino acids associated with first activity level information of the sequences. The method includes training, using the data set and the portion of the string, a final layer to generate a remainder of the string. The remainder pertains to second activity level information of the sequences. The method includes generating, using the first and final layer, the string comprising the portion and the remainder. The string represents a candidate drug compound.