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Text S1 - Prediction of Antibacterial Activity from Physicochemical Properties of Antimicrobial Peptides
Extended discussions on the 1) Analysis of published data under the proposed model, 2) Influence of the anionic charge of the membrane models on the conclusions of this work, 3) Approximations in the
NIAID Data Ecosystem40
Additional file 4 of Optimal selection of molecular descriptors for antimicrobial peptides classification: an evolutionary feature weighting approach
Predictions of antibacterial activity for DAMPD_ANTIBACTERIAL. Evaluation of different models with DAMPD_ANTIBACTERIAL after applying the best compromise solutions and four machine learning algorithms
Figshare2018-09-24 更新30
First Multitarget Chemo-Bioinformatic Model To Enable the Discovery of Antibacterial Peptides against Multiple Gram-Positive Pathogens
Antimicrobial peptides (AMPs) have emerged as promising therapeutic alternatives to fight against the diverse infections caused by different pathogenic microorganisms. In this context, theoretical app
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CalcAMP: A new machine learning model for the accurate pre-diction of antimicrobial activity of peptides
Datasets used for the publication: CalcAMP: A new machine learning model for the accurate prediction of antimicrobial activity of peptides
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CpACpP: In Silico Cell-Penetrating Anticancer Peptide Prediction Using a Novel Bioinformatics Framework
Cell-penetrating anticancer peptides (Cp-ACPs) are considered promising candidates in solid tumor and hematologic cancer therapies. Current approaches for the design and discovery of Cp-ACPs trust the
Figshare2021-07-25 更新30
