Thermodynamics and Kinetics of Drug-Target Binding by Molecular Simulation

Abstract

Computational studies play an increasingly important role in chemistry and biophysics, mainly thanks to improvements in hardware and algorithms. In drug discovery and development, computational studies can reduce the costs and risks of bringing a new medicine to market. Computational simulations are mainly used to optimize promising new compounds by estimating their binding affinity to proteins. This is challenging due to the complexity of the simulated system. To assess the present and future value of simulation for drug discovery, we review key applications of advanced methods for sampling complex free-energy landscapes at near nonergodicity conditions and for estimating the rate coefficients of very slow processes of pharmacological interest. We outline the statistical mechanics and computational background behind this research, including methods such as steered molecular dynamics and metadynamics. We review recent applications to pharmacology and drug discovery and discuss possible guidelines for the practitioner. Recent trends in machine learning are also briefly discussed. Thanks to the rapid development of methods for characterizing and quantifying rare events, simulation’s role in drug discovery is likely to expand, making it a valuable complement to experimental and clinical approaches.

A Benchmark of Popular Free Energy Approaches Revealing the Inhibitors Binding to SARS-CoV2 Mpro

“…In recent reports, 22,72 the FEP simulations successfully determined the ligand-binding free energy as known as the most accurate free energy methods. 25,74 However, although the perturbation results correlate with the respective experiments, 22,72 the Pearson coefficient diffused in a large range from 0.54 to 0.94. In particular, FEP simulations determined the ligand-binding free energy of 11 inhibitors to SARS-CoV-2 Mpro with high accuracy, FEP = 0.94 ± 0.04.…”

“…The ligand-binding free energy calculation methods were thus carried out. 25,37 The performance of free energy calculations based on SMD/MD trajectories were thus assessed.…”

“…[23][24][25] Therefore, numerous approaches have been inspired to resolve the problem. 25 In order to screen a large number of candidates, which probably up to several million compounds, the computational probe is generally operated via two steps: initial screening thousands/millions of compounds via rapid protocols such as quantitative structure activity relationship, 26 molecular docking, 27 and machine learning; 28 the ∆ was then realized using MD simulations, in which the popular free energy estimation approaches can be listed as the linear interaction energy (LIE), 29 fast of pulling ligand (FPL), 30 molecular mechanism-Poisson-Boltzmann (generalized Born) surface area (MM-PB(GB)SA), 31,32 non-equilibrium molecular dynamics (NEMD), 33 thermodynamic integration, 34 and free energy perturbation (FEP) approaches, 35 etc. However, it should be noted that the precision and accuracy of the ligand-binding affinity approaches somehow depend on the enzyme targets.…”

Design, Synthesis, and In Silico Multitarget Pharmacological Simulations of Acid Bioisosteres with a Validated In Vivo Antihyperglycemic Effect

“…Molecular dynamics simulations are used to understand biomolecular structure and dynamics [14][15][16]. In particular, for protein-ligand interactions, they have shed light on multiple venues, ranging from the structure-activity relationships of multiple compounds [17] to the kinetics and thermodynamics of binding [18].…”

“…EIMS: m/z = 343.3 [M + ], 326 [M-17], 193.0 [M-151]. [4-(biphenyl-3-ylmethoxy)phenyl] acetic acid(3)The mixture of (4-hydroxyphenyl)acetic (10) acid, K 2 CO 3 , and 3-phenylbenzyl bromide(18) was stirred and heated at 80 • C for 7 h. Recrystallization from EtOH was performed to afford crystals of 3. Yield = 75%; mp = 141.9-143.8 • C. 1 H-NMR (600 MHz, DMSO-d 6 ) δ: 3.48 (s, 2H, CH 2 ), 5.16 (s, 2H, OCH 2 ), 6.98 (d, 2H, Jo = 8.58, H-3, H-5), 7.18 (d, 2H, Jo = 8.58, H-2, H-6), 7.37 (t, 1H, Jo = 7.56, H-5 ), 7.44 (d, 1H, Jo = 7.56, H-4 ), 7.49-7.46 (m, 3H, H-3", H-4", H-5"), 7.62 (d, 1H, Jo = 7.56, H-6 ), 7.67 (d, 2H, Jo = 7.62, H-2", H-6"), 7.72 (s, 1H, H-2 ); 13 C-NMR (150 MHz, DMSO-d 6 ) δ: 39.68 (CH 2 ), 68.74 (OCH 2 ), 110.59 (C-2"'), 114.24 (C-3 , C-5 ), 125.58 (C-2"), 125.76 (C-6"), 126.29 (C-4"'), 126.35 (C-2"', C-6"'), 126.87 (C-5"), 127.17 (C-4"), 128.58 (C-3"', C-5"'), 128.70 (C-1 ).…”