Design of potent antimalarials with generative chemistry

Recent advances in generative modelling allow designing novel compounds through deep neural networks. One such neural network model, JT-VAE (the Junction Tree Variational Auto-Encoder), excels at proposing chemically valid structures. Here, on the basis of JT-VAE, we built a generative modelling approach, JAEGER, for finding novel chemical matter with desired bioactivity. Using JAEGER, we designed compounds to inhibit malaria. To prioritize the compounds for synthesis, we used the in-house pQSAR (Profile-QSAR) program, a massively multitask bioactivity model based on 12,000 Novartis assays. On the basis of pQSAR activity predictions, we selected, synthesized and experimentally profiled two compounds. Both compounds exhibited low nanomolar activity in a malaria proliferation assay as well as a biochemical assay measuring activity against PI(4)K, which is an essential kinase that regulates intracellular development in malaria. The compounds also showed low activity in a cytotoxicity assay. Our findings show that JAEGER is a viable approach for finding novel active compounds for drug discovery. Tropical diseases, such as malaria, can develop resistance to specific drugs. Godinez and colleagues present here a generative design approach to find new anti-malarial drugs to circumvent this resistance.