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.

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.

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.

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.

A system and method that given one or more input molecules, produces a contextualized summary of characteristics of related target molecules, e.g., proteins. Using a knowledge graph which is populated with all known molecules, input molecules are analyzed according to various similarity indexes which relate the input molecules to target proteins or other biological entities. The knowledge graph may also comprise scientific literature, governmental data (FDA clinical phase data), private research endeavors (general assays, etc.), and other related biological data. The summary produced may comprise target proteins that satisfy certain biological properties, general assay results (ADMET characteristics), related diseases, off-target molecule interactions (non-targeted molecules involved in a specific pathway or cascade), market opportunities, patents, experiments, and new hypothesis.

A composition-of-matter comprising a crystallized form of a large ribosomal (50S) subunit of a pathogenic bacterium, and the atomic coordinates of the three-dimensional structure thereof are provided herein, as well as methods for crystallizing the same, and using the atomic coordinates of the same to design de novo ligands with high specificity thereto.

A system and method that given one or more input molecules, produces a contextualized summary of characteristics of related target molecules, e.g., proteins. Using a knowledge graph which is populated with all known molecules, input molecules are analyzed according to various similarity indexes which relate the input molecules to target proteins or other biological entities. The knowledge graph may also comprise scientific literature, governmental data (FDA clinical phase data), private research endeavors (general assays, etc.), and other related biological data. The summary produced may comprise target proteins that satisfy certain biological properties, general assay results (ADMET characteristics), related diseases, off-target molecule interactions (non-targeted molecules involved in a specific pathway or cascade), market opportunities, patents, experiments, and new hypothesis.

Aspects of the present disclosure include methods for optimizing pharmacological compound development and methods for optimizing one or more modifications of a compound. Aspects of the present disclosure further include methods for designing treatments for a disease, and methods for designing optimized candidate compounds to treat a disease that causes one or more disease effects. Aspects of the present disclosure further include computer-implemented methods for training a model for pharmacological compound design, and computer-implemented methods for optimizing chemical modification of pharmacological compounds.