Proteomic Barcoding Platform for Macromolecular Screening and Delivery.

Autor: Wang N; Molecular Pharmacology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States., Mcneer NA; Molecular Pharmacology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States., Eton E; Molecular Pharmacology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States., Fass J; Tri-I PhD Program in Computational Biology and Medicine, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States., Kentsis A; Molecular Pharmacology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States.; Tow Center for Developmental Oncology, Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States.; Departments of Pediatrics, Pharmacology, and Physiology & Biophysics, Weill Cornell Medical College, Cornell University, New York, New York 10065, United States.

Publikováno v: Journal of proteome research [J Proteome Res] 2024 Jun 07; Vol. 23 (6), pp. 2067-2077. Date of Electronic Publication: 2024 May 22.

Multicenter phase II trial to investigate safety and efficacy of gemcitabine combined with cetuximab as adjuvant therapy in pancreatic cancer (ATIP)

Autor: Fensterer, H., Schade-Brittinger, C., Müller, H.-H., Tebbe, S., Fass, J., Lindig, U., Settmacher, U., Schmidt, W.E., Märten, A., Ebert, M.P., Kornmann, M., Hofheinz, R., Endlicher, E., Brendel, C., Barth, P.J., Bartsch, D.K., Michl, P., Gress, T.M. ^*

Publikováno v: In Annals of Oncology October 2013 24(10):2576-2581

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes via Nonequilibrium Candidate Monte Carlo

Autor: Gill, SC, Lim, NM, Grinaway, PB, Rustenburg, AS, Fass, J, Ross, GA, Chodera, JD, Mobley, DL

Publikováno v: The journal of physical chemistry. B, vol 122, iss 21
Gill, SC; Lim, NM; Grinaway, PB; Rustenburg, AS; Fass, J; Ross, GA; et al.(2018). Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes via Nonequilibrium Candidate Monte Carlo. JOURNAL OF PHYSICAL CHEMISTRY B, 122(21), 5579-5598. doi: 10.1021/acs.jpcb.7b11820. UC Irvine: Retrieved from: http://www.escholarship.org/uc/item/9xr5z58r

Accurately predicting protein-ligand binding affinities and binding modes is a major goal in computational chemistry, but even the prediction of ligand binding modes in proteins poses major challenges. Here, we focus on solving the binding mode predi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::e4df728667c3877f1f0bd9fd30538b44
https://escholarship.org/uc/item/9xr5z58r

End-to-end differentiable construction of molecular mechanics force fields.

Autor: Wang Y; Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center New York 10065 NY USA yuanqing.wang@choderalab.org john.chodera@choderalab.org.; Physiology, Biophysics and System Biology PhD Program, Weill Cornell Medical College, Cornell University New York 10065 NY USA.; MFA Program in Creative Writing, Division of Humanities and Arts, City College of New York, City University of New York New York 10031 NY USA., Fass J; Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center New York 10065 NY USA yuanqing.wang@choderalab.org john.chodera@choderalab.org.; Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medical College, Cornell University New York 10065 NY USA., Kaminow B; Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center New York 10065 NY USA yuanqing.wang@choderalab.org john.chodera@choderalab.org.; Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medical College, Cornell University New York 10065 NY USA., Herr JE; Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center New York 10065 NY USA yuanqing.wang@choderalab.org john.chodera@choderalab.org., Rufa D; Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center New York 10065 NY USA yuanqing.wang@choderalab.org john.chodera@choderalab.org.; Tri-Institutional PhD Program in Chemical Biology, Weill Cornell Medical College, Cornell University New York 10065 NY USA., Zhang I; Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center New York 10065 NY USA yuanqing.wang@choderalab.org john.chodera@choderalab.org.; Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medical College, Cornell University New York 10065 NY USA., Pulido I; Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center New York 10065 NY USA yuanqing.wang@choderalab.org john.chodera@choderalab.org., Henry M; Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center New York 10065 NY USA yuanqing.wang@choderalab.org john.chodera@choderalab.org., Bruce Macdonald HE; Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center New York 10065 NY USA yuanqing.wang@choderalab.org john.chodera@choderalab.org., Takaba K; Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center New York 10065 NY USA yuanqing.wang@choderalab.org john.chodera@choderalab.org.; Pharmaceutical Research Center, Advanced Drug Discovery, Asahi Kasei Pharma Corporation Shizuoka 410-2321 Japan., Chodera JD; Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center New York 10065 NY USA yuanqing.wang@choderalab.org john.chodera@choderalab.org.

Publikováno v: Chemical science [Chem Sci] 2022 Sep 08; Vol. 13 (41), pp. 12016-12033. Date of Electronic Publication: 2022 Sep 08 (Print Publication: 2022).

Bayesian-Inference-Driven Model Parametrization and Model Selection for 2CLJQ Fluid Models.

Autor: Madin OC; Department of Chemical & Biological Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States., Boothroyd S; Boothroyd Scientific Consulting Ltd., 71-75 Shelton Street, London, Greater London, United Kingdom, WC2H 9JQ., Messerly RA; Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States., Fass J; Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Graduate School of Medical Sciences, New York, New York 10065, United States., Chodera JD; Computational & Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States., Shirts MR; Department of Chemical & Biological Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States.

Publikováno v: Journal of chemical information and modeling [J Chem Inf Model] 2022 Feb 28; Vol. 62 (4), pp. 874-889. Date of Electronic Publication: 2022 Feb 07.