Antimalarial drugs are becoming less effective due to the emergence of drug resistance. Resistance has been reported for all available malaria drugs, including artemisinin, thus creating a perpetual need for alternative drug candidates. The traditional drug discovery approach of high throughput screening (HTS) of large compound libraries for identification of new drug leads is time-consuming and resource intensive. While virtual in silico screening is a solution to this problem, however, the generalization of the models is not ideal. Artificial intelligence (AI), utilizing either structure-based or ligand-based approaches, has demonstrated highly accurate performances in the field of chemical property prediction. Leveraging the existing data, AI would be a suitable alternative to blind-search HTS or fingerprint-based virtual screening. The AI model would learn patterns within the data and help to search for hit compounds efficiently. In this work, we introduce DeepMalaria, a deep-learning based process capable of predicting the anti-Plasmodium falciparum inhibitory properties of compounds using their SMILES. A graph-based model is trained on 13,446 publicly available antiplasmodial hit compounds from GlaxoSmithKline (GSK) dataset that are currently being used to find novel drug candidates for malaria. We validated this model by predicting hit compounds from a macrocyclic compound library and already approved drugs that are used for repurposing. We have chosen macrocyclic compounds as these ligand-binding structures are underexplored in malaria drug discovery. The in silico pipeline for this process also consists of additional validation of an in-house independent dataset consisting mostly of natural product compounds. Transfer learning from a large dataset was leveraged to improve the performance of the deep learning model. To validate the DeepMalaria generated hits, we used a commonly used SYBR Green I fluorescence assay based phenotypic screening. DeepMalaria was able to detect all the compounds with nanomolar activity and 87.5% of the compounds with greater than 50% inhibition. Further experiments to reveal the compounds’ mechanism of action have shown that not only does one of the hit compounds, DC-9237, inhibits all asexual stages of Plasmodium falciparum, but is a fast-acting compound which makes it a strong candidate for further optimization.

Drugs against malaria are losing their effectiveness because of emerging drug resistance. This underscores the need for novel therapeutic options for malaria with mechanism of actions distinct from current antimalarials. To identify novel pharmacophores against malaria we have screened compounds containing structural features of natural products that are pharmacologically relevant. This screening has identified a 4-nitro styrylquinoline (SQ) compound with submicromolar antiplasmodial activity and excellent selectivity. SQ exhibits a cellular action distinct from current antimalarials, acting early on malaria parasite's intraerythrocytic life cycle including merozoite invasion. The compound is a fast-acting parasitocidal agent and also exhibits curative property in the rodent malaria model when administered orally. In this report, we describe the synthesis, preliminary structure-function analysis, and the parasite developmental stage specific action of the SQ scaffold.

A deep-water sponge of the genus Spongosorites has yielded a bis-indole alkaloid which we have named dragmacidin G. Dragmacidin G was first reported by us in the patent literature and has recently been reported by Hitora et al. from a sponge of the genus Lipastrotheya. Dragmacidin G is the first in this series of compounds to have a pyrazine ring linking the two indole rings. It also has a rare N-(2-mercaptoethyl)-guanidine side chain. Dragmacidin G shows a broad spectrum of biological activity including inhibition of methicillin-resistant Staphylococcus aureus, Mycobacterium tuberculosis, Plasmodium falciparum, and a panel of pancreatic cancer cell lines.