The present invention generally relates to computer-aided molecular design

Medicilon's medicinal chemistry involves the application of a number of specialized disciplinary approaches all focused on the ultimate goal of drug discovery. Drug target identification and validation, rational (target-based) drug design, structural biology, computational-based drug design, methods development (chemical, biochemical and computational), and “Hit-to-lead” development are all aspects of medicinal chemistry. The techniques and approaches of chemical biology, synthetic organic chemistry, combinatorial (bio)chemistry, mechanistic enzymology, computational chemistry, chemical genomics and high-throughput screening are all used and applied by medicinal chemists towards drug discovery. Email:marketing@medicilon.com.cn web:www.medicilon.com

The invention develops a computer-aided drug design method and system to optimize a lead through structure-based drug design with synthetic accessibility. In this invention, two systems of the structure-based lead optimization are developed and implemented: 1) LeadOp (“short for lead optimization”)—an algorithm that performs lead optimization through structure-based fragment hopping method; and 2) LeadOp+R (short for “lead optimization with synthetic accessibility based on chemical reaction route”)—an algorithm that performs lead optimization with synthetic accessibility. LeadOp algorithm provides users to optimize a lead compound with various combinations of fragments with stronger binding based on group efficiency, generating lead with stronger potency. Furthermore, LeadOp+R provides an advantage in the selection of the new fragment to be assembled, which was identified based on the group efficiency calculated in the active site and reaction rule.

The present invention generally relates to computer-aided molecular design, and more specifically computer-aided lead optimization and computational modeling of lead optimization.

Discovering a new drug to treat or cure some biological condition, is a lengthy and expensive process, typically taking on average 12 years and $800 million per drug, and taking possibly up to 15 years or more and $1 billion to complete in some cases. Numerous software packages have been developed to assist in the development of new drugs. These methods involve a wide range of computational techniques, including use of a) rigid-body pattern-matching algorithms, either based on surface correlations, use of geometric hashing, pose clustering, or graph pattern-matching; b) fragmental-based methods, including incremental construction or ‘place and join’ operators; c) stochastic optimization methods including use of Monte Carlo, simulated annealing, or genetic (or memetic) algorithms; d) molecular dynamics simulations or e) hybrids strategies derived thereof.

Lead optimization typically involves substituent replacement paired with a QSAR (quantitative structure—activity relationship) model to refine and evaluate new compounds related to a specific biological end point or druglike properties. The use of QSAR optimization relies on the availability of confirmed chemical and biological data for a series of molecules to build the QSAR model that is able to predict the bioactivity (or end point) for new compounds in the hope of designing either better compounds or finding a novel series of compounds. Scaffold hopping aims to substitute the existing chemical core structure with a novel chemical structure while maintaining—or improving—the biological activity of the original molecule and uses one of two approaches: (i) virtual screening of the entire molecule, not a specific scaffold, to find novel chemical structures in molecular databases of available or virtual compounds or (ii) replacing the core structure with a different chemical motif that preserves similar ligand-receptor interactions via crucial ligand terminal groups.

The QSAR approach in the search for new scaffolds depends mostly on the molecular similarity of the initial compound of interest and the compounds in the database. The molecular similarity search techniques include shape, pharmacophore, and fingerprint-based methods or a combination of these strategies to identify similar molecules based on molecular features and potential similar bioactivities. The type of structural features and the molecular similarity cutoff value affects which molecules are selected. To overcome the molecular similarity bias that is commonly seen in ligand-based methods, fragment-based approaches have become widely used. Fragment libraries of possible molecular replacements (substituent) can be constructed by searching for bioisosteres, locating similar ring systems, replacing a central atom of the scaffold, using simple chemical rules (SMART matches, an extension of SMILES strings used to locate molecular substructures to condense the current compound databases), or defining fragmentation schemes of known ligands.