Many drugs operate by chemically binding to specific molecular receptors. Molecular receptors typically are specific proteins (a term that includes glycoproteins and lipoproteins) in an animal such as a human being, and drug design and selection can be facilitated by accurately estimating the binding affinity of a drug to a protein, or, more generally, estimating the binding affinity of a ligand to a receptor, the term receptor being used to mean any moiety that specifically binds the ligand.
One way to determine receptor-ligand binding affinity uses the molecular structure of the receptor-ligand complex that results when the ligand binds to the receptor. Such structures may be studied by x-ray crystallography. The publicly accessible protein data bank (PDB) now contains more than 10,000 x-ray crystal structures, and pharmaceutical and biotechnology companies have an order of magnitude more proprietary structures. Many of these structures have been co-crystallized with small molecules bound to them. The examination of such structures, and deployment of the knowledge thereby gained to design new, more potent, and more specific inhibitors, is referred to as structure-based drug design.
Computational modeling facilitates structure-based drug design. One aspect of modeling detailed below involves scoring functions that use simulation techniques, such as molecular dynamics, Monte Carlo, or continuum electrostatics calculations. These scoring functions can be problematic, as one is required to calculate a very small difference (the binding affinity) between two very large numbers (the free energies of the complex and of the separated protein and ligand). An alternative approach is to develop an empirical scoring function, based on the geometry of the complex, which directly evaluates the desired quantity. Such an approach has the advantage of being extremely fast as well as being amenable to fitting to experimental data, large amounts of which are now publicly available. Our own application, U.S. patent application Ser. No. 11/373,684 (US 2007/061118) entitled “Predictive Scoring Function for Estimating Binding Affinity,” which is hereby incorporated by reference in its entirety, discloses scoring functions.
It is desirable to increase the accuracy and robustness of scoring functions. It is particularly desirable to use scoring functions not only to distinguish active from inactive ligands, but also to rank ligands in a group of ligands, e.g., from most active to least active, to identify more promising candidates to be studied ahead of less promising candidates. Moreover, the ability to rank order a group of ligands having different chemotypes would allow one to search a broader range of candidates for effective drugs. Finally, rank ordering ligands without a large amount of preliminary work (e.g., using a training set) would further increase the convenience and speed of the process.