Abstract: A method for predicting a property of a sample molecule involves, for each of a multitude of reference molecules, obtaining a multitude of fingerprints and at least one property, and obtaining the multitude of fingerprints of the sample molecule. The method further involves for each of the multitude of reference molecules, using each of the multitude of fingerprints, calculating distances to the sample molecule, and for each of the multitude of reference molecules, determining a relative predictive dominance, based on the distances to the sample molecule. The method also involves, for each of the multitude of reference molecules, determining a fitness value based on the relative predictive dominance, and predicting the at least one property of the sample molecule based on the at least one property of the multitude of reference molecules and the fitness values obtained for the reference molecules.

Abstract: The invention involves a method for identifying a target protein. The invention involves receiving a request to identify a target protein based on a ligand; identifying, using the ligand, a first protein, where the ligand binds with the first protein to form a ligand-protein complex; generating, a first binding site profile for the first protein, where the first binding site profile describes molecular properties of the first protein; obtaining, from a controlled server, structure data describing molecular properties of surfaces for a multitude of proteins, where the multitude of proteins comprises the target protein; identifying, using the first binding site profile and the structure data, the target protein; and presenting the target protein to a user.

Abstract: A method for predicting drug-target binding using synthetically-augmented data involves generating a multitude of ghost ligands for a multitude of proteins in a protein structure database, generating a multitude of drug-target interaction (DTI) features for proteins and ligands in a DTI database, using the multitude of ghost ligands, generating a machine learning model using the multitude of DTI features, and predicting a likelihood of interaction for a combination of a query protein and a query ligand using the machine learning model.

Abstract: A method for predicting a property of a sample molecule involves, for each of a multitude of reference molecules, obtaining a multitude of fingerprints and at least one property, and obtaining the multitude of fingerprints of the sample molecule. The method further involves for each of the multitude of reference molecules, using each of the multitude of fingerprints, calculating distances to the sample molecule, and for each of the multitude of reference molecules, determining a relative predictive dominance, based on the distances to the sample molecule. The method also involves, for each of the multitude of reference molecules, determining a fitness value based on the relative predictive dominance, and predicting the at least one property of the sample molecule based on the at least one property of the multitude of reference molecules and the fitness values obtained for the reference molecules.

Abstract: The invention involves a method for identifying a target protein. The invention involves receiving a request to identify a target protein based on a ligand; identifying, using the ligand, a first protein, where the ligand binds with the first protein to form a ligand-protein complex; generating, a first binding site profile for the first protein, where the first binding site profile describes molecular properties of the first protein; obtaining, from a controlled server, structure data describing molecular properties of surfaces for a multitude of proteins, where the multitude of proteins comprises the target protein; identifying, using the first binding site profile and the structure data, the target protein; and presenting the target protein to a user.