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The authors discuss the computational method used in lead discovery. The topics of interest include the Monte Carlo combinatorial growth algorithm generating low-free-energy ligands in the protein binding site and application of this method in the picomolar inhibitors designed for the human carbonic anhydrase.

A small molecular growth program methodology was designed.Results have shown that through this design, individual optimized ligands can be generated ubiquitously. Various candidate leads were generated and screened according to the ease of synthesis, solubility, hydrogen bond formation, chemical affinity, and empirical measures of enthalpy by using Quanta/CHARMM.

The derivation of a knowledge-based potential for intermolecular interactions for the statistical information stored in the Cambridge Structural Database is described. A statistical mechanical method that relates the occurrences of intermolecular contacts in the database of their energies is developed.

It is well-known that the structure-based design approach has had a measurable impact on the drug discovery process in identifying novel and efficacious therapeutic agents for a variety of disease targets. The de novo design approach has inherent potential to generate novel molecules that best fit into a protein binding site when compared to all of the computational methods applied to structure-based design. In its initial attempts, this approach did not achieve much success due to technical hurdles. More recently, the algorithmic advancements in the methodologies and clever strategies developed to design drug-like molecules have improved the success rate. We describe a state-of-the-art structure-based design technology called Contour and provide details of the algorithmic enhancements we have implemented. Contour was designed to create novel drug-like molecules by assembling synthetically viable fragments in the protein binding site using a high-resolution crystal structure of the protein. The technology consists of a sophisticated growth algorithm and a novel scoring function based on a directional model. The growth algorithm generates molecules by dynamically selecting only those fragments from the fragment library that are complementary to the binding site, and assembling them by sampling the conformational space for each attached fragment. The scoring function embodying the essential elements of the binding interactions aids in the rank ordering of grown molecules and helps identify those that have high probability of exhibiting activity against the protein target of interest. The application of Contour to identify inhibitors against human renin enzyme eventually leading to the clinical candidate VTP-27,999 will be discussed here.

Structure guided optimization of a series of nonpeptidic alkyl amine renin inhibitors allowed the rational incorporation of additional polar functionality. Replacement of the cyclohexylmethyl group occupying the S1 pocket with a (R)-(tetrahydropyran-3-yl)methyl group and utilization of a different attachment point led to the identification of clinical candidate 9. This compound demonstrated excellent selectivity over related and unrelated off-targets, >15% oral bioavailability in three species, oral efficacy in a double transgenic rat model of hypertension, and good exposure in humans.

Computational lead design procedures require fast and accurate scoring functions to rank millions of generated virtual ligands for protein targets. In this article, we present an improved version of the SMoG scoring function, called SMoG2001. This function is based on a knowledge-based approach-that is, the free energy parameters are derived from the observed frequencies of atom-atom contacts in the database of three-dimensional structures of protein-ligand complexes via a procedure based on statistical mechanics. We obtained the statistics from the set of 725 complexes. SMoG2001 reproduces the experimental binding constants of the majority of 119 complexes of the testing set with good accuracy. On similar testing sets, SMoG2001 performs better than two other widely used scoring functions, PMF and SCORE1(LUDI), and comparably to DrugScore. SMoG2001 poorly predicts the affinities of ligands interacting via quantum mechanical forces with metal ions and ligands that are large and flexible. We attribute significant improvement in accuracy over previous versions of the SMoG scoring function to a better description of the reference state-that is, the state of no interactions.