A computer implemented method of generating a machine learning model for interpreting host phage response data comprising receiving datasets and labels for a host phage response, training a machine learning model and using this model to estimate the efficacy of a test phage in inhibiting growth of a test bacteria.

An artificial intelligence-based (AI-based) drug molecule processing method and apparatus, an electronic device, a computer-readable storage medium, and a computer program product are provided. The method includes: determining a plurality of candidate drug molecules for a target protein; performing activity prediction based on the plurality of candidate drug molecules and the target protein, to obtain activity information of each candidate drug molecule; performing homology modeling on the target protein, to obtain a reference protein having a structure homologous with that of the target protein; performing molecular docking based on the reference protein and the plurality of candidate drug molecules, to obtain molecular docking information of each candidate drug molecule; and screening the plurality of candidate drug molecules based on the activity information of each candidate drug molecule and the molecular docking information of each candidate drug molecule, to obtain target drug molecules for the target protein.

The present invention is a molecular graph representation learning method based on contrastive learning, the method comprising: obtaining a molecular fingerprint representation of each molecule, and calculating a similarity between each two molecular fingerprints; collecting a full amount of chemical functional group information, and matching a corresponding functional group for each atom in the molecule; using a heterogeneous graph to model a molecular graph; using a RGCN in the structure-aware molecular encoder to encode the representation of each atom in the molecule and the representation of the functional group to which the atom belongs, and mapping the molecule to a feature space through an aggregation function to obtain a structure-aware feature representation; according to the fingerprint similarity between molecules, selecting positive and negative samples, and carrying out a comparative learning in the feature space; obtaining the structure-aware molecular encoder by using the contrastive learning method for training on a large-sample molecular dataset, and applying the structure-aware molecular encoder to a prediction task of downstream molecular attributes. The present invention helps to capture more abundant molecular structure information and solve the problem on molecular property prediction.

A system and method for generating libraries of chemical compounds having desired and specific properties by formulating a reaction-based mechanism that may be powered by several algorithms including but not limited to genetic algorithm, expert iteration algorithms, planning methods, reinforcement learning and machine learning algorithms. The system and method may also provide the process steps by which these optimized products S′ may be synthesized from the reactants R1,R2 and further enables a rapid and efficient search of the synthetically accessible chemical space.

A system and method for generating libraries of chemical compounds having desired and specific properties by formulating a reaction-based mechanism that may be powered by several algorithms including but not limited to genetic algorithm, expert iteration algorithms, planning methods, reinforcement learning and machine learning algorithms. The system and method may also provide the process steps by which these optimized products S′ may be synthesized from the reactants R1,R2 and further enables a rapid and efficient search of the synthetically accessible chemical space.

An information processing method is performed by a computer for evaluating flammability of a mixed refrigerant material containing a plurality of components. The method includes: calculating, for each of the plurality of components, a second value obtained by multiplying a mixture ratio thereof in the mixed refrigerant material by a first value obtained based on numbers of hydrogen atoms, halogen atoms, and double bonds included in a molecular structure thereof; calculating a total sum of the second value calculated for each of the plurality of components; and classifying the mixed refrigerant material into a predetermined flammability class based on the total sum.

An information processing method is performed by a computer for evaluating flammability of a mixed refrigerant material containing a plurality of components. The method includes: calculating, for each of the plurality of components, a second value obtained by multiplying a mixture ratio thereof in the mixed refrigerant material by a first value obtained based on numbers of hydrogen atoms, halogen atoms, and double bonds included in a molecular structure thereof; calculating a total sum of the second value calculated for each of the plurality of components; and classifying the mixed refrigerant material into a predetermined flammability class based on the total sum.

A method of generating a chemical structure performed by a neural network device includes receiving a target property value and a target structure characteristic value; selecting first generation descriptors; generating second generation descriptors; determining, using a first neural network of the neural network device, property values of the second generation descriptors; determining, using a second neural network of the neural network device, structure characteristic values of the second generation descriptors; selecting, from the second generation descriptors, candidate descriptors that satisfy the target property value and the target structure characteristic value; and generating, using the second neural network of the neural network device, chemical structures for the selected candidate descriptors.

A method of generating a chemical structure performed by a neural network device includes receiving a target property value and a target structure characteristic value; selecting first generation descriptors; generating second generation descriptors; determining, using a first neural network of the neural network device, property values of the second generation descriptors; determining, using a second neural network of the neural network device, structure characteristic values of the second generation descriptors; selecting, from the second generation descriptors, candidate descriptors that satisfy the target property value and the target structure characteristic value; and generating, using the second neural network of the neural network device, chemical structures for the selected candidate descriptors.

One aspect of the disclosure relates to systems and methods for determining probabilities of successful synthesis of materials in the real world at one or more points in time. The probabilities of successful synthesis of materials in the real world at one or more points in time can be determined by representing the materials and their pre-defined relationships respectively as nodes and edges in a network form, and computation of the parameters of the nodes in the network as input to a classification model for successful synthesis. The classification model being configured to determine probabilities of successful synthesis of materials in the real world at one or more points in time.