Perspectives on Ligand/Protein Binding Kinetics Simulations: Force Fields, Machine Learning, Sampling, and User-Friendliness

Abstract

Computational techniques applied to drug discovery have gained considerable popularity for their ability to filter potentially active drugs from inactive ones, reducing the time scale and costs of preclinical investigations. The main focus of these studies has historically been the search for compounds endowed with high affinity for a specific molecular target to ensure the formation of stable and long-lasting complexes. Recent evidence has also correlated the in vivo drug efficacy with its binding kinetics, thus opening new fascinating scenarios for ligand/protein binding kinetic simulations in drug discovery. The present article examines the state of the art in the field, providing a brief summary of the most popular and advanced ligand/protein binding kinetics techniques and evaluating their current limitations and the potential solutions to reach more accurate kinetic models. Particular emphasis is put on the need for a paradigm change in the present methodologies toward ligand and protein parametrization, the force field problem, characterization of the transition states, the sampling issue, and algorithms' performance, user-friendliness, and data openness.

Figure 1

Artistic representation of the free-energy profile of LPB with bound (Bound), transition state (TS) and unbound state (Unbound). Target protein and ligand are shown as the surface and licorice, respectively.

Figure 2

Pipeline of LPB kinetics calculations for a sample system (i.e., adenosine GPCR A2A in complex with caffeine). The LPB can be parametrized using classic or ML-based FFs and then simulated employing one of the computational techniques described in the text. The output is the identification of the possible energy-minima LPB complexes (B and U), the transition state (TS), and rates between them (k_on and k_off).

Figure 3

Time scales of the physical and biological processes investigated by simulations. The bars related to the diffusion refer to diffusion coefficients measured in water. At the bottom time scales achievable by computational techniques.