Water molecules play a crucial role in mediating the interaction between a ligand and a macromolecular receptor. An understanding of the nature and role of each water molecule in the active site of a protein could greatly increase the efficiency of rational drug design approaches: if the propensity of a water molecule for displacement can be determined, then synthetic effort may be most profitably applied to the design of specific ligands with the displacement of this water molecule in mind. In this paper, a thermodynamic analysis of water molecules in the binding sites of six proteins, each complexed with a number of inhibitors, is presented. Two classes of water molecules were identified: those conserved and not displaced by any of the ligands, and those that are displaced by some ligands. The absolute binding free energies of 54 water molecules were calculated using the double decoupling method, with replica exchange thermodynamic integration in Monte Carlo simulations. It was found that conserved water molecules are on average more tightly bound than displaced water molecules. In addition, Bayesian statistics is used to calculate the probability that a particular water molecule may be displaced by an appropriately designed ligand, given the calculated binding free energy of the water molecule. This approach therefore allows the numerical assessment of whether or not a given water molecule should be targeted for displacement as part of a rational drug design strategy.

To reach their biological target, drugs have to cross cell membranes, and understanding passive membrane permeation is therefore crucial for rational drug design. Molecular dynamics simulations offer a powerful way of studying permeation at the single molecule level, yielding detailed dynamic and thermodynamic data. Biological membranes have a very inhomogeneous character and a highly anisotropic behavior. Starting from a computer model proven to reproduce the physical properties of such a complex system, the permeation of small organic molecules across a lipid bilayer model has been studied. Free energy profiles and diffusion coefficients along the bilayer normal have been calculated for small organic molecules by means of all-atom molecular dynamics (MD) simulations constraining the compounds at chosen depths inside the membrane. These data also allow for the calculation of permeability coefficients, the results for which have been compared with experimental data. The calculated permeability coefficients are generally 1 order of magnitude larger than the equivalent experimental data, but the molecules' relative permeability coefficients are reproduced.

Many limitations of current computer-aided drug design arise from the difficulty of reliably predicting the binding affinity of a small molecule to a biological target. There is thus a strong interest in novel computational methodologies that claim predictions of greater accuracy than current scoring functions, and at a throughput compatible with the rapid pace of drug discovery in the pharmaceutical industry. Notably, computational methodologies firmly rooted in statistical thermodynamics have received particular attention in recent years. Yet free energy calculations can be daunting to learn for a novice user because of numerous technical issues and various approaches advocated by experts in the field. The purpose of this article is to provide an overview of the current capabilities of free energy calculations and to discuss the applicability of this technology to drug discovery.

The calculation of relative free energies that involve large reorganizations of the environment is one of the great challenges of condensed-phase simulation. Such calculations are of particular importance in protein−ligand free-energy calculations. To meet this challenge, we have developed new free-energy techniques that combine the advantages of the replica-exchange method with free-energy perturbation (FEP) and finite-difference thermodynamic integration (FDTI). These new techniques are tested and compared with FEP, FDTI, and the adaptive umbrella weighted histogram analysis method (AdUmWHAM) on the challenging calculation of the relative hydration free energy of methane and water. This calculation involves a large solvent configurational change. Through the use of replica-exchange moves along the λ-coordinate, the configurations sampled along λ are allowed to mix, which leads to dramatic improvements in solvent configurational sampling, an efficient reduction of random sampling error, and a reduction of general simulation error. This is achieved at effectively no extra computational cost, relative to standard FEP or FDTI.