Prediction of Ligand Selectivity and Efficacy Using Artificial Intelligence Algorithms

Purpose:

Identifying target-specific ligands is extremely challenging in drug discovery, especially in cases where receptors display high structural similarity. Such is the case for metabotropic glutamate receptor subtype 2 (mGlu2) and metabotropic glutamate receptor subtype 3 (mGlu3), which are prime targets for various neurological treatments. However, signal transduction through these two receptors often yields opposing physiological function and differentially affects pathologies. The purpose of this study is to develop artificial intelligence (AI) methods to predict ligand selectivity and efficacy on similar targets.

Methods:

Understanding the need to differentiate ligands based on their binding to mGlu2 and mGlu3, we employed a machine learning approach. Using patent-derived datasets, data was pre-processed into an eight-dimension vector space. Afterwards, the data was flattened, and a Multiple Input and Output (MIO) Model was designed to receive the incoming vectors. A classification arm was designated as an output, differentiating input structures as mGlu2 or mGlu3 ligands. In addition, this novel MIO Model with Functional application program interface (API) architecture also has the capacity to estimate efficacy of an input ligand by outputting Half-maximal inhibitory concentration (IC50) value.

Results:

The model yielded greater than 96% accuracy in the classification task to predict the binding selectivity of the ligands, while simultaneously delivering satisfactory performance when predicting efficacy. With regards to the regression arm, the model attained about 81% accuracy in correctly identifying high-affinity mGlu2 compounds, and 62% accuracy in correctly identifying high-affinity mGlu3 compounds. We then used docking studies, and the trained model to screen an available ZINC database, selecting the top 39 compounds out of 9270 ligands.

Conclusions:

This approach can pave the way for computer aided searches which screen for high efficacy ligands belonging to a certain class of interest. More specifically, this model can be used in combination with other established structure-based methodology like molecular docking, allowing for screening of even more drug candidates for further study and validation. With access to other high-quality datasets, this model has the potential to apply to other ligand classes of interest, posing great potential for drug repurposing studies.