  Longer timelines and lower success rates of drug candidates limit the
productivity of clinical trials in the pharmaceutical industry. Promising de
novo drug design techniques help solve this by exploring a broader chemical
space, efficiently generating new molecules, and providing improved therapies.
However, optimizing for molecular characteristics found in approved oral drugs
remains a challenge, limiting de novo usage. In this work, we propose NovoMol,
a novel de novo method using recurrent neural networks to mass-generate drug
molecules with high oral bioavailability, increasing clinical trial time
efficiency. Molecules were optimized for desirable traits and ranked using the
quantitative estimate of drug-likeness (QED). Generated molecules meeting QED's
oral bioavailability threshold were used to retrain the neural network, and,
after five training cycles, 76% of generated molecules passed this strict
threshold and 96% passed the traditionally used Lipinski's Rule of Five. The
trained model was then used to generate specific drug candidates for the
cancer-related PDGFR{\alpha} receptor and 44% of generated candidates had
better binding affinity than the current state-of-the-art drug, Imatinib (with
a receptor binding affinity of -9.4 kcal/mol), and the best-generated candidate
at -12.9 kcal/mol. NovoMol provides a time/cost-efficient AI-based de novo
method offering promising drug candidates for clinical trials.
