Molecular Interaction Fields (MIF) is an archetypal computational chemistry technique that can be used to capture a singular fingerprint of an ensemble of atoms on a protein and encode its physicochemical environment. Thus, MIFs have particular relevance in the context of binding hot spots and binding site analysis.
Molecular interaction fields (MIFs) were introduced in computational and medicinal chemistry in the late 1970s to describe the three-dimensional interactions between a molecule and its environment. Molecules which elicit similar interactions generate similar MIFs, even though their underlying chemical structures may differ quite substantially. This model enables a number of core tasks in medicinal chemistry, ranging from bioisosteric replacement and molecular similarity assessment to virtual screening, quantitative structure activity relationships, and prediction of metabolic liability.
Root-Mean-Square Deviation (RMSD) values are calculated in MIFs. RMSD values between template and model structures are calculated on residue by residue basis, confirming that the mutation was the only structural change. Then, the MIF similarities are determined, that shows that this technique effectively captured subtle changes on molecules.
Computational techniques are effective tools for helping the drug design process. Computational chemistry can be used to predict physicochemical properties, energies, binding modes, interactions and a wide amount of helpful data in lead discovery and optimization. The interactions formed between a ligand and a molecular target structure can be represented by molecular interaction fields (MIF). The MIF identifies regions of a molecule where specific chemical groups can interact favorably, suggesting interaction sites with other molecules.
Drug discovery is a highly complex and costly process, and in recent years, the pharmaceutical industry has shifted from traditional to genomics‐ and proteomics‐based drug research strategies. The identification of druggable target sites, promising hits, and high quality leads are crucial steps in the early stages of drug discovery projects. Pharmacokinetic (PK) and drug metabolism profiling to optimize bioavailability, clearance, and toxicity are increasingly important areas to prevent costly failures in preclinical and clinical studies. The integration of a wide variety of technologies and expertise in multidisciplinary research teams combining synergistic effects between experimental and computational approaches on the selection and optimization of bioactive compounds to pass these hurdles is now commonplace, although there remain challenging areas. Molecular interaction fields (MIFs) are widely used in a range of applications to support the discovery teams, characterizing molecules according to their favorable interaction sites and therefore enabling predictions to be made about how molecules might interact. The utility of MIF‐based in silico approaches in drug design is extremely broad, including approaches to support experimental design in hit‐finding, lead‐optimization, physicochemical property prediction and PK modeling, drug metabolism prediction, and toxicity.
A set of MIFs means the spatial variation of the interaction energy between a molecular target and a chosen probe. The target may be a molecule or a macromolecule, or even a molecular complex. On the other hand, the probe may be a molecule or a molecular fragment in order to simulate the interaction of any chemical group.
Originally, GRID-derived MIFs were developed to determine energetically favorable binding sites on macromolecules in order to predict where ligands bind to biological macromolecules. Thereby, MIFs can guide structure-based ligand design whenever the target is a protein, a therapeutic agent, or other biologically important macromolecule, gaining a better insight to the factors affecting the binding helps in the design of improved ligands.
However, GRID MIFs are also frequently applied to low-molecular-weight compounds to derive 3D quantitative structure-activity relationships (QSARs) in comparative molecular field analysis (CoMFA)-like and GRID/GOLPE.