Chem-X and CAMBRIDGE. Comparison of computer generated chemical structures with X-ray crystallographic data

Chemical structure representations and applications in computational toxicity

Efficient storage and retrieval of chemical structures is one of the most important prerequisite for solving any computational-based problem in life sciences. Several resources including research publications, text books, and articles are available on chemical structure representation. Chemical substances that have same molecular formula but several structural formulae, conformations, and skeleton framework/scaffold/functional groups of the molecule convey various characteristics of the molecule. Today with the aid of sophisticated mathematical models and informatics tools, it is possible to design a molecule of interest with specified characteristics based on their applications in pharmaceuticals, agrochemicals, biotechnology, nanomaterials, petrochemicals, and polymers. This chapter discusses both traditional and current state of art representation of chemical structures and their applications in chemical information management, bioactivity- and toxicity-based predictive studies.

High resolution NMR conformational studies of new bivalent NOP receptor antagonists in model membrane systems

The interaction of new bivalent NOP receptor antagonists with dodecyl phosphatidylcholine micelles and DMPC/cholesterol liposomes was investigated in solution by high resolution NMR. The ligands are structurally related to the NOP antagonist JTC-801 plus a propanediamine or heptanediamine spacer between the pharmacophoric units. Ligand internuclear distances were derived from 2D NOESY data and applied to molecular modelling calculations as conformational restraints. NMR experiments on micelles evidenced that the ligands closely approached the micelles but gave no hints on the preferential conformations of the interacting ligands. Results from NMR experiments in the presence of liposomes clearly indicated that both ligands strongly interacted with the bilayer assuming a preferential folded conformation with the quinoline arms superimposing on each other. The finding suggested that these strongly lipophilic pharmacophores could localize in the native receptorial membrane in the form of a depot, gaining access to the recognition site via the lipid bilayer.

The pKa Distribution of Drugs: Application to Drug Discovery

The acid-base dissociation constant (p Ka) of a drug is a key physicochemical parameter influencing many biopharmaceutical characteristics. While this has been well established, the overall proportion of non-ionizable and ionizable compounds for drug-like substances is not well known. Even less well known is the overall distribution of acid and base p Ka values. The current study has reviewed the literature with regard to both the proportion of ionizable substances and p Ka distributions. Further to this a set of 582 drugs with associated p Ka data was thoroughly examined to provide a representative set of observations. This was further enhanced by delineating the compounds into CNS and non-CNS drugs to investigate where differences exist. Interestingly, the distribution of p Ka values for single acids differed remarkably between CNS and non-CNS substances with only one CNS compound having an acid p Ka below 6.1. The distribution of basic substances in the CNS set also showed a marked cut off with no compounds having a p Ka above 10.5.

The p Ka distributions of drugs are influenced by two main drivers. The first is related to the nature and frequency of occurrence of the functional groups that are commonly observed in pharmaceuticals and the typical range of p Ka values they span. The other factor concerns the biological targets these compounds are designed to hit. For example, many CNS targets are based on seven transmembrane G protein-coupled receptors (7TM GPCR) which have a key aspartic acid residue known to interact with most ligands. As a consequence, amines are mostly present in the ligands that target 7TM GPCR's and this influences the p Ka profile of drugs containing basic groups. For larger screening collections of compounds, synthetic chemistry and the working practices of the chemists themselves can influence the proportion of ionizable compounds and consequent p Ka distributions. The findings from this study expand on current wisdom in p Ka research and have implications for discovery research with regard to the composition of corporate databases and collections of screening compounds. Rough guidelines have been suggested for the profile of compound collections and will evolve as this research area is expanded.

Computer automated prediction of potential therapeutic and toxicity protein targets of bioactive compounds from Chinese medicinal plants

Understanding the molecular mechanism and pharmacology of bioactive compounds from Chinese medicinal plants (CMP) is important in facilitating scientific evaluation of novel therapeutic approaches in traditional Chinese medicine. It is also of significance in new drug development based on the mechanism of Chinese medicine. A key step towards this task is the determination of the therapeutic and toxicity protein targets of CMP compounds. In this work, newly developed computer software INVDOCK is used for automated identification of potential therapeutic and toxicity targets of several bioactive compounds isolated from Chinese medicinal plants. This software searches a protein database to find proteins to which a CMP compound can bind or weakly bind. INVDOCK results on three CMP compounds (allicin, catechin and camptotecin) show that 60% of computer-identified potential therapeutic protein targets and 27% of computer-identified potential toxicity targets have been implicated or confirmed by experiments. This software may potentially be used as a relatively fast-speed and low-cost tool for facilitating the study of molecular mechanism and pharmacology of bioactive compounds from Chinese medicinal plants and natural products from other sources.

Prediction of potential toxicity and side effect protein targets of a small molecule by a ligand-protein inverse docking approach

Determination of potential drug toxicity and side effect in early stages of drug development is important in reducing the cost and time of drug discovery. In this work, we explore a computer method for predicting potential toxicity and side effect protein targets of a small molecule. A ligand-protein inverse docking approach is used for computer-automated search of a protein cavity database to identify protein targets. This database is developed from protein 3D structures in the protein data bank (PDB). Docking is conducted by a procedure involving multiple conformer shape-matching alignment of a molecule to a cavity followed by molecular-mechanics torsion optimization and energy minimization on both the molecule and the protein residues at the binding region. Potential protein targets are selected by evaluation of molecular mechanics energy and, while applicable, further analysis of its binding competitiveness against other ligands that bind to the same receptor site in at least one PDB entry. Our results on several drugs show that 83% of the experimentally known toxicity and side effect targets for these drugs are predicted. The computer search successfully predicted 38 and missed five experimentally confirmed or implicated protein targets with available structure and in which binding involves no covalent bond. There are additional 30 predicted targets yet to be validated experimentally. Application of this computer approach can potentially facilitate the prediction of toxicity and side effect of a drug or drug lead.

Ligand-protein inverse docking and its potential use in the computer search of protein targets of a small molecule

Ligand-protein docking has been developed and used in facilitating new drug discoveries. In this approach, docking single or multiple small molecules to a receptor site is attempted to find putative ligands. A number of studies have shown that docking algorithms are capable of finding ligands and binding conformations at a receptor site close to experimentally determined structures. These algorithms are expected to be equally applicable to the identification of multiple proteins to which a small molecule can bind or weakly bind. We introduce a ligand-protein inverse-docking approach for finding potential protein targets of a small molecule by the computer-automated docking search of a protein cavity database. This database is developed from protein structures in the Protein Data Bank (PDB). Docking is conducted with a procedure involving multiple-conformer shape-matching alignment of a molecule to a cavity followed by molecular-mechanics torsion optimization and energy minimization on both the molecule and the protein residues at the binding region. Scoring is conducted by the evaluation of molecular-mechanics energy and, when applicable, by the further analysis of binding competitiveness against other ligands that bind to the same receptor site in at least one PDB entry. Testing results on two therapeutic agents, 4H-tamoxifen and vitamin E, showed that 50% of the computer-identified potential protein targets were implicated or confirmed by experiments. The application of this approach may facilitate the prediction of unknown and secondary therapeutic target proteins and those related to the side effects and toxicity of a drug or drug candidate. Proteins 2001;43:217-226.

Ligand-protein inverse docking and its potential use in the computer search of protein targets of a small molecule

Ligand-protein docking has been developed and used in facilitating new drug discoveries. In this approach, docking single or multiple small molecules to a receptor site is attempted to find putative ligands. A number of studies have shown that docking algorithms are capable of finding ligands and binding conformations at a receptor site close to experimentally determined structures. These algorithms are expected to be equally applicable to the identification of multiple proteins to which a small molecule can bind or weakly bind. We introduce a ligand-protein inverse-docking approach for finding potential protein targets of a small molecule by the computer-automated docking search of a protein cavity database. This database is developed from protein structures in the Protein Data Bank (PDB). Docking is conducted with a procedure involving multiple-conformer shape-matching alignment of a molecule to a cavity followed by molecular-mechanics torsion optimization and energy minimization on both the molecule and the protein residues at the binding region. Scoring is conducted by the evaluation of molecular-mechanics energy and, when applicable, by the further analysis of binding competitiveness against other ligands that bind to the same receptor site in at least one PDB entry. Testing results on two therapeutic agents, 4H-tamoxifen and vitamin E, showed that 50% of the computer-identified potential protein targets were implicated or confirmed by experiments. The application of this approach may facilitate the prediction of unknown and secondary therapeutic target proteins and those related to the side effects and toxicity of a drug or drug candidate. Proteins 2001;43:217-226.

Pharmacophores in drug design and discovery

Compounds containing a specific pharmacophore--the minimum structural features necessary for enzyme binding--can be retrieved from a database such as the National Cancer Institute repository by means of three-dimensional (3D) searching, which allows the retrieval of all compounds possessing a specified set of atoms with a given 3D geometry. The ways in which pharmacophores can be found and characterized and the details of the 3D searching methods are described. Elaboration of compounds found in such searches and their subsequent development as lead drugs is also discussed.

Active-site-directed 3D database searching: pharmacophore extraction and validation of hits

Two new computational tools, PRO_PHARMEX and PRO_SCOPE, for use in active-site-directed searching of 3D databases are described. PRO_PHARMEX is a flexible, graphics-based program facilitating the extraction of pharmacophores from the active site of a target macromolecule. These pharmacophores can then be used to search a variety of databases for novel lead compounds. Such searches can often generate many 'hits' of varying quality. To aid the user in setting priorities for purchase, synthesis or testing, PRO_SCOPE can be used to dock molecules rapidly back into the active site and to assign them a score using an empirical scoring function correlated to the free energy of binding. To illustrate how these tools can add value to existing 3D database software, their use in the design of novel thrombin inhibitors is described.

Molecular modeling in the discovery of drug leads

The National Cancer Institute of the U.S.A. maintains a repository of about 500,000 chemicals which it has tested at some time for anticancer activity. As new chemotherapeutic targets present themselves, methods have been developed by which this large database can be re-examined without resorting to expensive high-volume biological screening. Electronic screening, the method described in this paper, begins with the identification of a target enzyme. The pharmacophore used by the enzyme in binding to substrates is identified, and then all compounds in the database that contain the pharmacophore are found. These compounds are then further filtered, for example, by physical properties such as solubility, and the relatively small number of compounds that survive are submitted for biological testing. This use of a primary electronic screen in the search for ligands of protein kinase C is described. The screen is very fast, and the method is quite generally applicable to different enzymes.