References
- Ladbury JE, Klebe G, Freire E. Adding calorimetric data to decision making in lead discovery: a hot tip. Nat Rev Drug Discov. 2010;9:23–27.
- Geschwindner S, Ulander J, Johansson P. Ligand binding thermodynamics in drug discovery: still a hot tip? J Med Chem. 2015;58:6321–6335.
- Klebe G. Broad-scale analysis of thermodynamic signatures in medicinal chemistry: are enthalpy-favored binders the better development option? Drug Discov Today. 2019;24:943–948.
- Giordanetto F, Jin C, Willmore L, et al. Fragment hits: what do they look like and how do they bind? J Med Chem. 2019;62:3381–3394.
- Irwin BWJ, Huggins DJ. Estimating atomic contributions to hydration and binding using free energy perturbation. J Chem Theory Comput. 2018;14:3218–3227.
- Krimmer SG, Cramer J, Betz M, et al. Rational design of thermodynamic and kinetic binding profiles by optimizing surface water networks coating protein-bound ligands. J Med Chem. 2016;59:10530–10548.
- Schiebel J, Gaspari R, Wulsdorf T, et al. Intriguing role of water in protein-ligand binding studied by neutron crystallography on trypsin complexes. Nat Commun. 2018;9:e3559.
- Spyrakis F, Ahmed MH, Bayden AS, et al. The roles of water in the protein matrix: a largely untapped resource for drug discovery. J Med Chem. 2017;60:6781–6827.
- Leeson PD, Springthorpe B. The influence of drug-like concepts on decision-making in medicinal chemistry. Nat Rev Drug Discov. 2007;6:881–890.
- Shultz MD. The thermodynamic basis for the use of lipophilic efficiency (LipE) in enthalpic optimizations. Bioorg Med Chem Lett. 2013;23:5992–6000.
- Wienen-Schmidt B, Jonker HRA, Wulsdorf T, et al. Paradoxically, most flexible ligand binds most entropy-favored: intriguing impact of ligand flexibility and solvation on drug–kinase binding. J Med Chem. 2018;61:5922–5933.
- Verteramo ML, Stenström O, Ignjatović MM, et al. Interplay between conformational entropy and solvation entropy in protein–ligand binding. J Am Chem Soc. 2019;141:2012–2026.
- Magarkar A, Schnapp G, Apel A-K, et al. Enhancing drug residence time by shielding of intra-protein hydrogen bonds: a case study on CCR2 Antagonists. ACS Med Chem Lett. 2019;10:324–328.
- Betz M, Wulsdorf T, Krimmer SG, et al. Impact of surface water layers on protein-ligand binding: how well are experimental data reproduced by molecular dynamics simulations in a thermolysin test case? J Chem Inf Model. 2016;56:223–233.
- Li A, Gilson MK. Protein-ligand binding enthalpies from near-millisecond simulations: analysis of a preorganization paradox. J Chem Phys. 2018;149:072311.
- Haider K, Cruz A, Ramsey S, et al. Solvation structure and thermodynamic mapping (SSTMap): an open-source, flexible package for the analysis of water in molecular dynamics trajectories. J Chem Theory Comput. 2018;14:418–425.
- Bucher D, Stouten P, Triballeau N. Shedding light on important waters for drug design: simulations versus grid-based methods. J Chem Inf Model. 2018;58:692–699.
- Anandakrishnan R, Izadi S, Onufriev AV. Why computed protein folding landscapes are sensitive to the water model. J Chem Theory Comput. 2019;15:625–636.
- Wahl J, Smieško M. Assessing the predictive power of relative binding free energy calculations for test cases involving displacement of binding site water molecules. J Chem Inf Model. 2019;59(2):754–765.
- Chan H, Cherukara MJ, Narayanan B, et al. Machine learning coarse grained models for water. Nat Commun. 2019;10:e379.
- Silverstein KAT, Haymet ADJ, Dill KA. A simple model of water and the hydrophobic effect. J Am Chem Soc. 1998;120(13):3166–3175.
- Onufriev AV, Izadi S. Water models for biomolecular simulations. Wiley Interdiscip Rev Comput Mol Sci. 2018;8:e1347.
- Bannan CC, Burley KH, Chiu M, et al. Blind prediction of cyclohexane–water distribution coefficients from the SAMPL5 challenge. J Comput Aid Mol Des. 2016;30(11):927–944.
- Rizzi A, Murkli S, McNeill JN, et al. Overview of the SAMPL6 host–guest binding affinity prediction challenge. J Comput Aid Mol Des. 2018;32(10):937–963.