Future Medicinal Chemistry

Journal: Future Medicinal Chemistry

Abbreviation

Future Med Chem

Publisher

Future Science

ISSN

1756-8927
1756-8919

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Publications1 - 10 of 15

Computational Chemistry
(2014)
Future Medicinal Chemistry
Antidiabetic sulfonylureas modulate farnesoid X receptor activation and target gene transcription
Steri, Ramona; Kara, Mahmut; Proschak, Ewgenij; et al. (2010)
Future Medicinal Chemistry
Polyamine-oligonucleotide conjugates: A promising direction for nucleic acid tools and therapeutics
Menzi, Mirjam; Lightfoot, Helen L.; Hall, Jonathan (2015)
Future Medicinal Chemistry
Tofacitinib and analogs as inhibitors of the histone kinase PRK1 (PKN1)
Ostrovskyi, Dmytro; Rumpf, Tobias; Eib, Julia; et al. (2016)
Future Medicinal Chemistry
Interview with Gisbert Schneider
Bruce, Isaac; Schneider, Gisbert (2012)
Future Medicinal Chemistry
Breaking the data barrier in computational medicinal chemistry
Schneider, Gisbert (2014)
Future Medicinal Chemistry
Computational medicinal chemistry
Schneider, Gisbert (2011)
Future Medicinal Chemistry
Editorial. Toward the elucidation of the mechanism for passive membrane permeability of cyclic peptides
Riniker, Sereina (2019)
Future Medicinal Chemistry
Combinatorial chemistry by ant colony optimization
Hiss, Jan A.; Reutlinger, Michael; Koch, Christian P.; et al. (2014)
Future Medicinal Chemistry
Active learning for computational chemogenomics
Reker, Daniel; Schneider, Petra; Schneider, Gisbert; et al. (2017)
Future Medicinal Chemistry
Aim: Computational chemogenomics models the compound–protein interaction space, typically for drug discovery, where existing methods predominantly either incorporate increasing numbers of bioactivity samples or focus on specific subfamilies of proteins and ligands. As an alternative to modeling entire large datasets at once, active learning adaptively incorporates a minimum of informative examples for modeling, yielding compact but high quality models. Results/methodology: We assessed active learning for protein/target family-wide chemogenomic modeling by replicate experiment. Results demonstrate that small yet highly predictive models can be extracted from only 10–25% of large bioactivity datasets, irrespective of molecule descriptors used. Conclusion: Chemogenomic active learning identifies small subsets of ligand–target interactions in a large screening database that lead to knowledge discovery and highly predictive models.

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Journal: Future Medicinal Chemistry

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