BACKGROUND

Field of the Art

The disclosure relates to the field of medical research, and more particularly to the field of prediction of the bioactivity interactions between molecules and proteins using 3D-based representation and analysis.

Discussion of the State of the Art

Pharmaceutical research is hindered by the complexity of protein-ligand interactions. Proteins are macromolecules that are involved in a large array of biological functions. Proteins are macromolecules, comprising long chains of amino acids, each of which is itself an organic molecule. The shape of proteins determines their bioactivity, and the shape of a protein is determined by the way the protein folds based on its molecular structure. The complexity of proteins and their folding patterns makes their final shapes computationally intractable.

Ligands further complicate the issue as each ligand further changes the shape of the protein, which changes its bioactivity. Inferring the interaction between a protein and a ligand alone is a computationally difficult task because proteins are two or three orders of magnitude larger than a typical ligand and the number of possible interaction sites is very large. Further, each ligand further changes the shape of the protein, which changes its bioactivity, and the possible spatial conformations of a protein is several orders of magnitude larger than a ligand.

What is needed is a system and method for computationally tractable prediction of protein-ligand interactions and their bioactivity using three-dimensional pocket-based convolutional neural network models.

SUMMARY

Accordingly, the inventor has conceived and reduced to practice, a system and method that predicts whether a given protein-ligand pair is active or inactive and outputs a pose score classifying the propriety of the pose. A 3D bioactivity platform comprising a 3D bioactivity module and data platform scrapes empirical lab-based data that a docking simulator uses to generate a dataset from which a 3D-CNN model is trained. The model then may receive new protein-ligand pairs and determine a classification for the bioactivity and pose propriety of that protein-ligand pair. Furthermore, gradients relating to the binding affinity in the 3D model of the molecule may be used to generate profiles from which new protein targets may be determined.

According to a first preferred embodiment, a system for prediction of protein-ligand bioactivity and pose propriety is disclosed, comprising: a computing device comprising a memory and a processor; a 3D bioactivity module comprising a first plurality of programming instructions stored in the memory and operating on the processor, wherein the first plurality of programming instructions causes the computing device to: receive empirical data related to a protein-ligand pair; generate training data from the empirical data; train a machine learning model using the generated training data; receive data related to a target protein-ligand pair; use the machine learning model to predict the bioactivity of the target protein-ligand pair; use the machine learning model to predict the propriety of the pose of the target protein-ligand pair; and output the predictions related to the target protein-ligand pair.

According to a second preferred embodiment, a method for prediction of protein-ligand bioactivity and pose propriety is disclosed, comprising the steps of: receiving empirical data related to a protein-ligand pair; generating training data from the empirical data; training a machine learning model using the generated training data; receiving data related to a target protein-ligand pair; using the machine learning model to predict the bioactivity of the target protein-ligand pair; using the machine learning model to predict the propriety of the pose of the target protein-ligand pair; and output the predictions related to the target protein-ligand pair.

According to various aspects of the invention; the system and method further comprising a docking simulator, wherein the docking simulator generates the training data; wherein the training data comprises one or more poses of the protein-ligand pair; the empirical data comprises ground-truths about a protein-ligand pair; 5; the 3D bioactivity module outputs a three-dimensional model of the target protein-ligand pair; the three-dimensional model includes an importance reference system that represents the binding affinity per each atom in the molecule; the importance reference system information is used in backpropagation to further enhance the machine learning model.

