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
