Abstract: An example method for training a graph convolutional neural network (GCNN) configured for virtual screening of molecules for drug discovery is described herein. The method can include receiving a first data set including a plurality of molecules, and training the GCNN to initialize one or more parameters of the GCNN using the first data set. The method can also include receiving a second data set including a plurality of molecules and respective inhibition rates for a disease, and training the GCNN to refine the one or more parameters of the GCNN using the second data set. The molecules in the first and second data sets can be expressed in a computer-readable format. An example method for virtually screening molecules on Plasmodium falciparum (P. falciparum) is also described herein.

Abstract: An example method for training a graph convolutional neural network (GCNN) configured for virtual screening of molecules for drug discovery is described herein. The method can include receiving a first data set including a plurality of molecules, and training the GCNN to initialize one or more parameters of the GCNN using the first data set. The method can also include receiving a second data set including a plurality of molecules and respective inhibition rates for a disease, and training the GCNN to refine the one or more parameters of the GCNN using the second data set. The molecules in the first and second data sets can be expressed in a computer-readable format. An example method for virtually screening molecules on Plasmodium falciparum (P. falciparum) is also described herein.