Methods for treating cancer using compounds that inhibit human DNA ligases. Methods for using compounds that inhibit human DNA ligases to provide insights into the reaction mechanisms of human DNA ligases, for example to identify the human DNA ligase involved in different DNA repair pathways. Screening methods for compounds that inhibit human DNA ligases.

The disclosure provides methods and systems for identifying a subset of compounds in a plurality of compounds. The identifying includes obtaining, for each compound, a vector including a set of elements, where each element includes a measurement of a different feature of an instance of a cell context upon exposure to the compound. The identifying includes performing the obtaining for a plurality of cell contexts, to obtain a plurality of vectors for each compound across different cell contexts. The identifying includes combining the vectors for each compound to form a combined vector for each compound, thereby forming a plurality of combined vectors representing different compounds. The identifying includes pruning the plurality of compounds to the subset of compounds based on a similarity between respective combined vectors in the plurality of combined vectors corresponding to compounds in the plurality of compounds.

A method for training molecular binding models includes: using a to-be-trained molecular binding model to determine, based on protein feature information and molecular feature information, binding activity feature information, embedding feature information and eutectic feature information between sample protein molecules and sample alternative molecules; determining a training loss of the to-be-trained molecular binding model based on the binding activity feature information, the embedding feature information and the eutectic feature information; and outputting the molecular binding model as a trained molecular binding model when the training loss meets a training target; the trained molecular binding model being configured to determine binding activity feature information between a target protein molecule and a target alternative molecule to predict the binding activity of a compound after virtual binding of the target protein molecule and the target alternative molecule.

Methods, apparatus, and storage medium for determining a combination of coupling partners for a reaction according to input data. The method includes obtaining test input data for a test coupling partner of a test chemical type; obtaining selected input data for a selected coupling partner of a selected chemical type; determining, based on a reaction condition library, a candidate reaction condition set according to the test input data and selected input data, the candidate reaction condition set comprising a previous reaction condition; determining a candidate reaction vector representative of the candidate reaction condition set; inputting the candidate reaction vector into an input layer of a neural network set; and receiving an output at an output layer of the neural network set, the output indicative of a predicted yield from reacting the test coupling partner and the selected coupling partner under the candidate reaction condition set.

Methods, apparatus, and storage medium for determining a combination of coupling partners for a reaction according to input data. The method includes obtaining test input data for a test coupling partner of a test chemical type; obtaining selected input data for a selected coupling partner of a selected chemical type; determining, based on a reaction condition library, a candidate reaction condition set according to the test input data and selected input data, the candidate reaction condition set comprising a previous reaction condition; determining a candidate reaction vector representative of the candidate reaction condition set; inputting the candidate reaction vector into an input layer of a neural network set; and receiving an output at an output layer of the neural network set, the output indicative of a predicted yield from reacting the test coupling partner and the selected coupling partner under the candidate reaction condition set.

Disclosed herein are systems and uses of systems operating between fully quantum coherent and fully classical states. Such systems operate in what is termed the “Poised realm” and exhibit unique behaviors that can be applied to a number of useful applications. Non-limiting examples include drug discovery, computers, and artificial intelligence

Disclosed herein are systems and uses of systems operating between fully quantum coherent and fully classical states. Such systems operate in what is termed the “Poised realm” and exhibit unique behaviors that can be applied to a number of useful applications. Non-limiting examples include drug discovery, computers, and artificial intelligence

The present invention provides a drug virtual screening system for crystal complexes, and method of using the same, comprising a visualization subsystem, an evaluation tool box subsystem, an AI model management subsystem, a large-scale sampling subsystem, a virtual screening subsystem, and a data log storage subsystem. Starting with the known crystal complexes, a batch of candidate compounds that meet the requirements are recommended after going through the visualization subsystem, evaluation tool box subsystem, AI model management subsystem, large-scale sampling subsystem, and virtual screening system in turn. Based on this system, the generation of the compound library is organically combined with the subsequent virtual screening. Users only need to describe the action mode of the drug on the protein and the requirements for the drug to generate a batch of compounds that meet the expectations. The automated system reduces user intervention and improves the efficiency of research and development.

A system, device, and method for predicting a docked position of a target ligand in a binding site of a biomolecule is disclosed. The prediction makes use of a template ligand-biomolecule complex structure in order to predict a target ligand-biomolecule complex structure. The system and device contain modules allowing for the prediction of a target-ligand biomolecule complex structure. A preparation module can receive information identifying a target ligand and a template ligand-biomolecule structure. A pharmacophore matcher module can identify common pharmacophores between the template ligand and the target ligand. A docking module can predict a docked ligand position of the target ligand by overlapping the pharmacophore models of the target ligand and template ligand while the template ligand is in the binding site of the biomolecule. A biomolecule modification module can modify the biomolecule to reduce clashes between the docked target ligand and the biomolecule.

A system, device, and method for predicting a docked position of a target ligand in a binding site of a biomolecule is disclosed. The prediction makes use of a template ligand-biomolecule complex structure in order to predict a target ligand-biomolecule complex structure. The system and device contain modules allowing for the prediction of a target-ligand biomolecule complex structure. A preparation module can receive information identifying a target ligand and a template ligand-biomolecule structure. A pharmacophore matcher module can identify common pharmacophores between the template ligand and the target ligand. A docking module can predict a docked ligand position of the target ligand by overlapping the pharmacophore models of the target ligand and template ligand while the template ligand is in the binding site of the biomolecule. A biomolecule modification module can modify the biomolecule to reduce clashes between the docked target ligand and the biomolecule.