Automated Ligand Design in Simulated Molecular Docking

Abstract

The drug discovery process broadly follows the sequence of high-throughput screening, optimisation, synthesis, testing, and finally, clinical trials. We investigate methods for accelerating this process with machine learning algorithms that can automatically design novel ligands for biological targets. Recent work has demonstrated the viability of deep reinforcement learning, generative adversarial networks and auto-encoders. Here, we extend state-of-the-art deep reinforcement learning molecular modification algorithms and, through the integration of molecular docking simulations, apply them to automatically design novel antagonists for the adenosine triphosphate binding site of Plasmodium falciparum phosphatidylinositol 4-kinase, an enzyme essential to the malaria parasite’s development within an infected host. We demonstrated that such an algorithm was capable of designing novel molecular graphs with better DSs than the best DSs in a set of reference molecules. There reference set here was a set of 1,011 structural analogues of napthyridine, imidazopyridazine, and aminopyradine