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1.
J Med Chem ; 64(22): 16450-16463, 2021 11 25.
Artigo em Inglês | MEDLINE | ID: mdl-34748707

RESUMO

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.


Assuntos
Antimaláricos/química , Antimaláricos/farmacologia , ATPases Transportadoras de Cálcio/antagonistas & inibidores , Descoberta de Drogas , Inibidores Enzimáticos/química , Inibidores Enzimáticos/farmacologia , Modelos Biológicos , Humanos , Plasmodium falciparum/efeitos dos fármacos , Plasmodium falciparum/enzimologia , Relação Estrutura-Atividade
2.
IEEE Trans Inf Technol Biomed ; 9(2): 256-65, 2005 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-16138542

RESUMO

The ability to perform an exploratory search and retrieval of relevant documents from a large collection of domain-specific documents is an important requirement both in the field of medicine and other areas. In this paper, we present a unsupervised distributional clustering technique called SOPHIA. SOPHIA provides a semantically meaningful visual clustering of the document corpus in conjunction with an intuitive interactive search facility. We assess the effectiveness of SOPHIA's cluster-based information retrieval for the MEDLINE testset collection known as OHSUMED.


Assuntos
Armazenamento e Recuperação da Informação , Inteligência Artificial , Análise por Conglomerados , Vocabulário Controlado
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