Abstract: The present disclosure relates to novel compounds that inhibit glucose-induced degradation-deficient (GID) E3 ligase, pharmaceutical compositions containing such compounds, and their use in prevention and treatment of cancer and related diseases and conditions.

Abstract: Disclosed are heteroaryl compounds and pharmaceutical compositions thereof, which are useful for inhibiting EGFR, as well as methods for using such compounds to treat cancer associated with an EGFR or HER2 exon 20 insertion mutation.

Abstract: The present disclosure provides methods and apparatuses for molecular modeling with machine-learned universal potential functions. An exemplary method includes: determining a data structure representing chemical identities of atoms in a molecule; training an energy potential model using a training set comprising the data structure, true conformations of the molecule, and false conformations of the molecule; determining, using the trained energy potential model, a potential function associated with the molecule; or determining a conformation of the molecule based on potential function.

Abstract: A computer-implemented method for predicting molecule properties is disclosed. According to some embodiments, the method may include receiving an input file of a compound. The method may also include implementing a neural network to determine molecular configurations of the compound based on the input file and a plurality of molecular descriptors associated with the compound. The method may also generating, using the neural network, one or more three-dimensional (3D) models of the compound based on the determined molecular configurations of the compound. The method may also include determining, using the neural network, energy scores of the one or more 3D models when the compound is docked into a protein. The method may further include determining a property of the docked compound based on the energy scores.

Abstract: A computer-implemented method for predicting molecule properties is disclosed. According to some embodiments, the method may include receiving an input file of a compound. The method may also include implementing a neural network to determine molecular configurations of the compound based on the input file and a plurality of molecular descriptors associated with the compound. The method may also generating, using the neural network, one or more three-dimensional (3D) models of the compound based on the determined molecular configurations of the compound. The method may also include determining, using the neural network, energy scores of the one or more 3D models when the compound is docked into a protein. The method may further include determining a property of the docked compound based on the energy scores.