Machine Learning Prediction of Allosteric Drug Activity from Molecular Dynamics

Allosteric drugs have been attracting increasing interest over the past few years. In this context, it is common practice to use high-throughput screening for the discovery of non-natural allosteric drugs. While the discovery stage is supported by a growing amount of biological information and increasing computing power, major challenges still remain in selecting allosteric ligands and predicting their effect on the target protein's function. Indeed, allosteric compounds can act both as inhibitors and activators of biological responses. Computational approaches to the problem have focused on variations on the theme of molecular docking coupled to molecular dynamics with the aim of recovering information on the (long-range) modulation typical of allosteric proteins.

Automated summary

Allosteric drugs have gained increasing interest, with high-throughput screening being a common practice for their discovery. Despite growing biological information and computational power, challenges remain in selecting allosteric ligands and predicting their impact on target protein function. Computational approaches typically involve molecular docking and molecular dynamics to recover information on the long-range regulation typical of allosteric proteins.