inhibitors and methods of use thereof https://uspto.report/patent/app/20200062760 (accessed 29 August 2020).
13 13 Murcko, M. (2018).Workshop on free energy, kinetics, and Markov state models. https://www.youtube.com/watch?v=T4zEx‐l10BQ (accessed 29 August 2020).
14 14 Relay Therapeutics (2020).Our focus on protein motion is creating new possibilities in drug discovery. https://relaytx.com/pipeline (accessed 30 August 2020).
15 15 Business WIre (2020).Schrödinger and Bayer collaborate to co‐develop de novo design technology to accelerate drug discovery. https://www.businesswire.com/news/home/20200108005059/en/Schr%C3%B6dinger‐Bayer‐Collaborate‐Co‐Develop‐de‐novo‐Design (accessed 29 August 2020).
16 16 Business WIre (2019).Schrödinger announces collaboration with AstraZeneca to deploy advanced computing technology for drug discovery. https://www.businesswire.com/news/home/20190904005166/en/Schr%C3%B6dinger‐Announces‐Collaboration‐AstraZeneca‐Deploy‐Advanced‐Computing (accessed 29 August 2020).
17 17 Business WIre (2020).Schrödinger announces expanded collaboration with AstraZeneca to extend computational modeling solutions to biologics. https://www.businesswire.com/news/home/20200323005050/en/Schr%C3%B6dinger‐Announces‐Expanded‐Collaboration‐AstraZeneca‐Extend‐Computational (accessed 29 August 2020).
18 18 Rees, V. (2020).Discovering and designing drugs with artificial intelligence. https://www.drugtargetreview.com/article/56366/discovering‐and‐designing‐drugs‐with‐artificial‐intelligence (accessed 30 August 2020).
19 19 Bell, J.A., Cao, Y., Gunn, J.R. et al. (2012). PrimeX and the Schrödinger computational chemistry suite of programs. In: International. Tables for Crystallography, 534–538. Wiley https://doi.org/10.1107/97809553602060000864.
20 20 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