Contemporary Accounts in Drug Discovery and Development. Группа авторов

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Robertson, M.J., van Zundert, G.C.P., Borrelli, K., and Skiniotis, G. (2020). GemSpot: a pipeline for robust modeling of ligands into Cryo‐EM maps. Structure 28: 707–716.e3.

      21 21 van Zundert, G.C.P., Moriarty, N.W., Sobolev, O.V. et al. (2020). Macromolecular refinement of X‐ray and cryo‐electron microscopy structures with Phenix / OPLS3e for improved structure and ligand quality. bioRχiv. https://doi.org/10.1101/2020.07.10.198093.

      22 22 Beuming, T., Che, Y., Abel, R. et al. (2012). Thermodynamic analysis of water molecules at the surface of proteins and applications to binding site prediction and characterization. Proteins 80: 871–883.

      23 23 Young, T., Abel, R., Kim, B. et al. (2007). Motifs for molecular recognition exploiting hydrophobic enclosure in protein–ligand binding. Proc. Natl. Acad. Sci.: 808–813. https://doi.org/10.1073/pnas.0610202104.

      24 24 Abel, R., Young, T., Farid, R. et al. (2008). Role of the active‐site solvent in the thermodynamics of factor Xa ligand binding. J. Am. Chem. Soc. 130: 2817–2831.

      25 25 Bayden, A.S., Moustakas, D.T., Joseph‐McCarthy, D., and Lamb, M.L. (2015). Evaluating free energies of binding and conservation of crystallographic waters using SZMAP. J. Chem. Inf. Model. 55: 1552–1565.

      26 26 Nittinger, E., Gibbons, P., Eigenbrot, C. et al. (2019). Water molecules in protein‐ligand interfaces. Evaluation of software tools and SAR comparison. J. Comput. Aided Mol. Des. 33: 307–330.

      27 27 Ghanakota, P., DasGupta, D., and Carlson, H.A. (2019). Free energies and entropies of binding sites identified by MixMD Cosolvent simulations. J. Chem. Inf. Model. 59: 2035–2045.

      28 28 Ghanakota, P., van Vlijmen, H., Sherman, W., and Beuming, T. (2018). Large‐scale validation of mixed‐solvent simulations to assess hotspots at protein–protein interaction interfaces. J. Chem. Inf. Model.: 784–793. https://doi.org/10.1021/acs.jcim.7b00487.

      29 29 Ghanakota, P. and Carlson, H.A. (2016). Driving structure‐based drug discovery through Cosolvent molecular dynamics. J. Med. Chem. 59: 10383–10399.

      30 30 Ghanakota, P. and Carlson, H.A. (2016). Moving beyond active‐site detection: MixMD applied to allosteric systems. J. Phys. Chem. B 120: 8685–8695.

      31 31 Bian, Y. and Xie, X.‐Q.S. (2018). Computational fragment‐based drug design: current trends, strategies, and applications. AAPS J. 20: 59.

      32 32 Blay, V., Tolani, B., Ho, S.P., and Arkin, M.R. (2020). High‐throughput screening: today's biochemical and cell‐based approaches. Drug Discov. Today https://doi.org/10.1016/j.drudis.2020.07.024.

      33 33 Price, A.J., Howard, S., and Cons, B.D. (2017). Fragment‐based drug discovery and its application to challenging drug targets. Essays Biochem. 61: 475–484.

      34 34 Favalli, N., Bassi, G., Scheuermann, J., and Neri, D. (2018). DNA‐encoded chemical libraries – achievements and remaining challenges. FEBS Lett. 592: 2168–2180.

      35 35 Yuen, L.H. and Franzini, R.M. (2017). Achievements, challenges, and opportunities in DNA‐encoded library research: an academic point of view. Chembiochem. 18: 829–836.

      36 36 Gimeno, A., Ojeda‐Montes, M.J., Tomás‐Hernández, S. et al. (2019). The light and dark sides of virtual screening: what is there to know. Int. J. Mol. Sci.: 20. https://doi.org/10.3390/ijms20061375.

      37 37 Fradera, X. and Babaoglu, K. (2017). Overview of methods and strategies for conducting virtual small molecule screening. Curr. Protoc. Chem. Biol. 9: 196–212.

      38 38 Pagadala, N.S., Syed, K., and Tuszynski, J. (2017). Software for molecular docking: a review. Biophys. Rev.: 91–102. https://doi.org/10.1007/s12551‐016‐0247‐1.

      39 39 Murphy, R.B., Repasky, M.P., Greenwood, J.R. et al. (2016). WScore: a flexible and accurate treatment of explicit water molecules in ligand‐receptor docking. J. Med. Chem. 59: 4364–4384.

      40 40 Friesner, R.A., Murphy, R.B., Repasky, M.P. et al. (2006). Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein‐ligand complexes. J. Med. Chem. 49: 6177–6196.

      41 41 Friesner, R.A., Banks, J.L., Murphy, R.B. et al. (2004). Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J. Med. Chem.: 1739–1749. https://doi.org/10.1021/jm0306430.

      42 42 McGaughey, G.B., Sheridan, R.P., Bayly, C.I. et al. (2007). Comparison of topological, shape, and docking methods in virtual screening. J. Chem. Inf. Model. 47: 1504–1519.

      43 43 Sastry, G.M., Inakollu, V.S.S., and Sherman, W. (2013). Boosting virtual screening enrichments with data fusion: coalescing hits from two‐dimensional fingerprints, shape, and docking. J. Chem. Inf. Model. 53: 1531–1542.

      44 44 Hawkins, P.C.D., Skillman, A.G., and Nicholls, A. (2007). Comparison of shape‐matching and docking as virtual screening tools. J. Med. Chem. 50: 74–82.

      45 45 Grant, J.A., Gallardo, M.A., and Pickup, B.T. (1996). A fast method of molecular shape comparison: a simple application of a Gaussian description of molecular shape. J. Comput. Chem.: 1653–1666. https://doi.org/10.1002/(SICI)1096‐987X(19961115)17:14<1653::AID‐JCC7>3.0.CO;2‐K.

      46 46 Bahi, M. and Batouche, M. (2018). Deep learning for ligand‐based virtual screening in drug discovery. In: 2018 3rd International Conference on Pattern Analysis and Intelligent Systems (PAIS).Tebessa, Algeria (24–25 October 2018). IEEE https://doi.org/10.1109/pais.2018.8598488.

      47 47 Pérez‐Sianes, J., Pérez‐Sánchez, H., and Díaz, F. (2019). Virtual screening meets deep learning. Curr. Comput. Aided Drug Des. 15: 6–28.

      48 48 Gentile, F., Agrawal, V., Hsing, M. et al. (2019). Deep docking – a deep learning approach for virtual screening of big chemical datasets. doi:10.1101/2019.12.15.877316.

      49 49 McCloskey, K., Sigel, E.A., Kearnes, S. et al. (2020). Machine learning on DNA‐encoded libraries: a new paradigm for hit finding. J. Med. Chem. 63: 8857–8866.

      50 50 Yu, H.S., Modugula, K., Ichihara, O. et al. (2020). General theory of fragment linking in molecular design: why fragment linking rarely succeeds and how to improve outcomes. J. Chem. Theory Comput. https://doi.org/10.1021/acs.jctc.0c01004.

      51 51 Zhao, H. (2007). Scaffold selection and scaffold hopping in lead generation: a medicinal chemistry perspective. Drug Discov. Today 12: 149–155.

      52 52 Wang, L., Deng, Y., Wu, Y. et al. (2017). Accurate modeling of scaffold hopping transformations in drug discovery. J. Chem. Theory Comput. 13: 42–54.

      53 53 Paulsen, J.L., Yu, H.S., Sindhikara, D. et al. (2020). Evaluation of free energy calculations for the prioritization of macrocycle synthesis. J. Chem. Inf. Model. 60: 3489–3498.

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