Last Updated: 30/01/2025
Automated ligand design in simulated molecular docking – optimising ligand binding affinity through the application of deep Q-learning to docking simulations
Objectives
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.
The drug discovery process broadly follows the sequence of high-throughput screening, optimisation, synthesis, testing, and finally, clinical trials. Methods for accelerating this process with machine learning algorithms are being investigated 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, state-of-the-art deep reinforcement learning molecular modification algorithms is extended and, through the integration of molecular docking simulations, apply them to achieve the objective.
Mar 2023
