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To aid this latter approach and overcome the absence of knowledge of the target or its structure, computational models may be developed using artificial intelligence (AI) and machine learning (ML). Nevertheless, 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). As a result, good hypotheses or key insights may be overlooked, which can lengthen the time taken to identify a lead candidate and increase costs associated with synthesizing complex molecules that are later revealed to be inactive. There are a number of obvious limitations to this approach, including the personal bias/imagination of the scientist or the availability/cost of resources. The lead-optimization phase in this type of drug discovery is less streamlined than that in the former method as it is conducted without guidance from target binding interactions and often relies upon the intuition of the medicinal chemist to design and synthesize compounds to explore the SAR. (2) This process involves the initial identification of potent compounds that give rise to the desired effect (e.g., inhibition of cell growth), with target determination performed thereafter. Since all data and participant interactions remain in the public domain, this research project “lives” and may be improved by others.Īlternatively, if the biological target is not known, phenotypic drug discovery may be undertaken.
Sandra model set 102 series#
Half possessed biological activity, with one featuring a motif that the human chemists familiar with this series would have dismissed as “ill-advised”. 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. Competition participants could see all entries as they were submitted. 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. The structure of PfATP4 has not been determined. 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.
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