MAIP: a web service for predicting blood‐stage malaria inhibitors

Bosc, Nicolas; Félix, Eloy; Arcila, Ricardo; Méndez, David; Saunders, Martin; Green, Darren V. S.; Ochoada, Jason; Shelat, Anang A.; Martin, Éric; Iyer, Preeti; Engkvist, Ola; Verras, Andreas; Duffy, James; Burrows, Jeremy N.; Gardner, Mark; Leach, Andrew R.

doi:10.1186/s13321-021-00487-2

Cited by 32 publications

(42 citation statements)

References 29 publications

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“…ChEMBL Database was used to predict the Antimalarial inhibition profile of the selected compounds on three different datasets as MMV, PubChem, and St. Jude sets [27]. The Enrichment factor is based on early detection of actives within the list of compounds [28].…”

Section: Maip Analysismentioning

confidence: 99%

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In Silico Prediction of Plasmodium falciparum Cytoadherence Inhibitors That Disrupt Interaction between gC1qR-DBLβ12 Complex

et al. 2022

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Malaria causes about half a million deaths per year, mainly in children below 5 years of age. Cytoadherence of Plasmodium falciparum infected erythrocytes in brain and placenta has been linked to severe malaria and malarial related deaths. Cytoadherence is mediated by binding of human receptor gC1qR to the DBLβ12 domain of a P. falciparum erythrocyte membrane protein family 1 (PfEMP1) protein. In the present work, molecular dynamic simulation was extensively studied for the gC1qR-DBLβ12 complex. The stabilized protein complex was used to study the protein–protein interface interactions and mapping of interactive amino acid residues as hotspot were performed. Prediction of inhibitors were performed by using virtual protein–protein inhibitor database Timbal screening of about 15,000 compounds. In silico mutagenesis studies, binding profile and protein ligand interaction fingerprinting were used to strengthen the screening of the potential inhibitors of gC1qR-DBLβ12 interface. Six compounds were selected and were further subjected to the MAIP analysis and ADMET studies. From these six compounds, the compounds 3, 5, and 6 were found to outperform on all screening criteria from the rest selected compounds. These compounds may provide novel drugs to treat and manage severe falciparum malaria. Additionally. the identified hotspots can be used in future for designing novel interventions for disruption of interface interactions, such as through peptides or vaccines. Futher in vitro and in vivo studies are required for the confirmation of these compounds as potential inhibitors of gC1qR-DBLβ12 interaction.

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Section: Maip Analysismentioning

confidence: 99%

“…The antimalarial tool MAIP [27] based on consensus in silico model implemented in ChEMBL webserver was used for the large-scale prediction of antimalarial activities of the compounds. The MAIP is based on open-source tools and is freely available for the prediction of compounds which is integrated with the ChEMBL website.…”

Section: Maip Analysismentioning

confidence: 99%

In Silico Prediction of Plasmodium falciparum Cytoadherence Inhibitors That Disrupt Interaction between gC1qR-DBLβ12 Complex

et al. 2022

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“…To overcome this, new avenues are pursued. One particularly promising approach is collaborative efforts between otherwise competing companies, e.g., Martin and Zhu [1], leveraging artificial intelligence (AI) methods [2,3]. Here, we describe a part of the MELLODDY project, a collaborative effort of different pharma companies (referred to as "partner" throughout this article) in the field of multi-task learning [4].…”

Section: Introductionmentioning

confidence: 99%

Don’t Overweight Weights: Evaluation of Weighting Strategies for Multi-Task Bioactivity Classification Models

et al. 2021

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Machine learning models predicting the bioactivity of chemical compounds belong nowadays to the standard tools of cheminformaticians and computational medicinal chemists. Multi-task and federated learning are promising machine learning approaches that allow privacy-preserving usage of large amounts of data from diverse sources, which is crucial for achieving good generalization and high-performance results. Using large, real world data sets from six pharmaceutical companies, here we investigate different strategies for averaging weighted task loss functions to train multi-task bioactivity classification models. The weighting strategies shall be suitable for federated learning and ensure that learning efforts are well distributed even if data are diverse. Comparing several approaches using weights that depend on the number of sub-tasks per assay, task size, and class balance, respectively, we find that a simple sub-task weighting approach leads to robust model performance for all investigated data sets and is especially suited for federated learning.

show abstract

“…To overcome this, new avenues are pursued. One particularly promising approach are collaborative efforts of otherwise competing companies, e.g., Martin and Zhu [1], leveraging artificial intelligence (AI) methods [2,3]. Here we describe a part of the MELLODDY project, a collaborative effort of different pharma companies (referred to as "partner" throughout this article) in the field of multi-task learning [4].…”

Section: Introductionmentioning

confidence: 99%

Don’t overweight weights: Evaluation of weighting strategies for multi-task bioactivity classification models

Humbeck

Morawietz

Sturm

et al. 2021

Preprint

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Machine learning models predicting the bioactivity of chemical compounds belong nowadays to the standard tools of cheminformaticians and computational medicinal chemists. Multi-task and federated learning are promising machine learning approaches that allow priva-cy-preserving usage of large amount of data from diverse sources, which is crucial for achieving good generalization and high-performance results. Using large, real world data sets from six pharmaceutical companies, here we investigate different strategies for averaging weighted task loss functions to train multi-task bioactivity classification models. The weighting strategies shall be suitable for federated learning and ensure that learning efforts are well distributed even if data are diverse. Comparing several approaches using weights that depend on the number of sub-tasks per assay, task size, and class balance, respectively, we find that a simple sub-task weighting approach leads to robust model performance for all investigated data sets and is especially suited for federated learning.

show abstract

MAIP: a web service for predicting blood‐stage malaria inhibitors

Cited by 32 publications

References 29 publications

In Silico Prediction of Plasmodium falciparum Cytoadherence Inhibitors That Disrupt Interaction between gC1qR-DBLβ12 Complex

In Silico Prediction of Plasmodium falciparum Cytoadherence Inhibitors That Disrupt Interaction between gC1qR-DBLβ12 Complex

Don’t Overweight Weights: Evaluation of Weighting Strategies for Multi-Task Bioactivity Classification Models

Don’t overweight weights: Evaluation of weighting strategies for multi-task bioactivity classification models

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