Abstract
The Open Source Malaria (OSM) consortium is developing compounds that kill the human malaria parasite, Plasmodium falciparum, by targeting PfATP4, an essential ion pump on the parasite surface. The structure of PfATP4 has not been determined. Here, we describe a public competition created to develop a predictive model for the identification of PfATP4 inhibitors, thereby reducing project costs associated with the synthesis of inactive compounds. Competition participants could see all entries as they were submitted. In the final round, featuring private sector entrants specializing in machine learning methods, the best-performing models were used to predict novel inhibitors, of which several were synthesized and evaluated against the parasite. Half possessed biological activity, with one featuring a motif that the human chemists familiar with this series would have dismissed as "ill-advised". Since all data and participant interactions remain in the public domain, this research project "lives" and may be improved by others.
Highlights
Efficiency in the early stages of the drug discovery pipeline, from hit identification to lead optimization, is key to the development of new drugs
An initial attempt by a single Open Source Malaria (OSM) contributor to develop a pharmacophore model was based around the known PfATP4 active compounds from the Medicines for Malaria Venture (MMV) Malaria Box.[31,32]
With hit identification and lead optimization being key steps in the development of any new drug, the continued advancements in machine learning and artificial intelligence approaches possess significant promise to streamline this process, which would result in more efficient medicinal chemistry campaigns
Summary
Efficiency in the early stages of the drug discovery pipeline, from hit identification to lead optimization, is key to the development of new drugs. In target-based drug discovery, the molecular target of interest is known.[1] With this knowledge, libraries containing many compounds are screened (experimentally or computationally) against the known target to identify promising candidates or chemical scaffolds for further development. Through testing these chemicals, the key binding interactions may be identified and more directed structure−activity relationship (SAR). The advantage of phenotypic drug discovery, which underpins its popularity, is that hit or lead compounds are already known to be effective in their overall role (e.g., the killing of a pathogen)
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