Jordi Mestres

MIMIC: A molecular‐field matching program. Exploiting applicability of molecular similarity approaches

This contribution presents the development and applicability of MIMIC, a molecular‐field matching program for quantitatively evaluating the similarity between molecules, in a computationally feasible way and assesses the relative orientation that maximizes their similarity. In the present version one can deal with two types of molecular‐field similarities, namely steric volume and electrostatic, as well as a combined similarity that takes into account a given contribution from each of them. Besides optimization of the relative spatial orientation by maximizing these similarities, MIMIC can perform exhaustive searches to locate a global similarity maximum candidate and the set of local similarity maxima closest to it. The high accuracy of the approach permits evaluation of the similarity space coverage of each similarity maximum. In addition, the study of the relationships among similarity spaces is proposed as a strategy to better understand the linkage between the different molecular overlay solutions obtained from the use of different molecular‐field representations. Finally, calculation of the atomic contributions to the total molecular similarity provides a means for locating maximum similarity loci and for constructing pharmacophore patterns. The methodological bases and a detailed description of how they were implemented in MIMIC to handle molecular matchings between large systems is presented. All current features of the program were applied to a relative ligand‐binding problem between two nonnucleoside HIV‐1 reverse transcriptase inhibitors.

A Ligand-Based Approach to Mining the Chemogenomic Space of Drugs

The practical implementation and validation of a ligand-based approach to mining the chemogenomic space of drugs is presented and applied to the in silico target profiling of 767 drugs against 684 targets of therapeutic relevance. The results reveal that drugs targeting aminergic G protein-coupled receptors (GPCRs) show the most promiscuous pharmacological profiles. The detection of cross-pharmacologies between aminergic GPCRs and the opioid, sigma, NMDA, and 5-HT3 receptors aggravate the potential promiscuity of those drugs, predominantly including analgesics, antidepressants, and antipsychotics.

Ligand-based approach to in silico pharmacology: nuclear receptor profiling.

Bioactive ligands are a valuable and increasingly accessible source of information about protein targets. On the basis of this statement, a list of 25 nuclear receptors was described by a series of bioactive ligands extracted directly from bibliographical sources, stored properly in an annotated chemical library, and mathematically represented using the recently reported SHED molecular descriptors. Analysis of this ligand information allowed for derivation of a threshold of nuclear receptor concern. If the similarity of one molecule to any of the molecules annotated to one particular nuclear receptor is below that threshold, the molecule receives an alert on the probability of having affinity below 10 microM for that nuclear receptor. On this basis, a linkage map was constructed that reveals the interaction network of nuclear receptors from the perspective of their active ligands. This ligand-based approach to nuclear receptor profiling was subsequently applied to four external chemical libraries of 10,000 molecules targeted to proteases, kinases, ion channels, and G protein-coupled receptors. The percentage of each library that returned an alert on at least one nuclear receptor was reasonably low and varied between 4.4 and 9.7%. In addition, ligand-based nuclear receptor profiling of a set of 2944 drugs provided an alert for 153 drugs. For some of them, namely, acitretin, telmisartan, phenyltoloxamine, tazarotene, and flumazenil, bibliographical evidence could be found indicating that those drugs may indeed have some potential off-target residual affinity for the nuclear receptors annotated. Overall, the present findings suggest that ligand-based approaches to protein family profiling appear as a promising means toward the establishment of novel tools for in silico pharmacology.

Foundations and recent developments on molecular quantum similarity

A general definition of the Quantum Molecular Similarity Measure is reported. Particular cases of this definition are discussed, drawing special attention to the new definition of Gravitational-like Quantum Molecular Similarity Measures. Applications to the study of fluoromethanes and chloromethanes, the Carbonic Anhydrase enzyme, and the Hammond postulate are presented. Our calculations fully support the use of Quantum Molecular Similarity Measures as an efficient molecular engineering tool in order to predict physical properties, biological and pharmacological activities, as well as to interpret complex chemical problems.

Similarity-driven flexible ligand docking.

A similarity‐driven approach to flexible ligand docking is presented. Given a reference ligand or a pharmacophore positioned in the protein active site, the method allows inclusion of a similarity term during docking. Two different algorithms have been implemented, namely, a similarity‐penalized docking (SP‐DOCK) and a similarity‐guided docking (SG‐DOCK). The basic idea is to maximally exploit the structural information about the ligand binding mode present in cases where ligand‐bound protein structures are available, information that is usually ignored in standard docking procedures. SP‐DOCK and SG‐DOCK have been derived as modified versions of the program DOCK 4.0, where the similarity program MIMIC acts as a module for the calculation of similarity indices that correct docking energy scores at certain steps of the calculation. SP‐DOCK applies similarity corrections to the set of ligand orientations at the end of the ligand incremental construction process, penalizing the docking energy and, thus, having only an effect on the relative ordering of the final solutions. SG‐DOCK applies similarity corrections throughout the entire ligand incremental construction process, thus affecting not only the relative ordering of solutions but also actively guiding the ligand docking. The performance of SP‐DOCK and SG‐DOCK for binding mode assessment and molecular database screening is discussed. When applied to a set of 32 thrombin ligands for which crystal structures are available, SG‐DOCK improves the average RMSD by ca. 1 Å when compared with DOCK. When those 32 thrombin ligands are included into a set of 1,000 diverse molecules from the ACD, DIV, and WDI databases, SP‐DOCK significantly improves the retrieval of thrombin ligands within the first 10% of each of the three databases with respect to DOCK, with minimal additional computational cost. In all cases, comparison of SP‐DOCK and SG‐DOCK results with those obtained by DOCK and MIMIC is performed. Proteins 2000;40:623–636.

SHED: Shannon entropy descriptors from topological feature distributions.

A novel set of molecular descriptors called SHED (SHannon Entropy Descriptors) is presented. They are derived from distributions of atom-centered feature pairs extracted directly from the topology of molecules. The value of a SHED is then obtained by applying the information-theoretical concept of Shannon entropy to quantify the variability in a feature-pair distribution. The collection of SHED values reflecting the overall distribution of pharmacophoric features in a molecule constitutes its SHED profile. Similarity between pairs of molecules is then assessed by calculating the Euclidean distance of their SHED profiles. Under the assumption that molecules having similar pharmacological profiles should contain similar features distributed in a similar manner, examples are given to show the ability of SHED for scaffold hopping in virtual chemical screening and pharmacological profiling compared to that of substructural BCI fingerprints and three-dimensional GRIND descriptors.

A comparative analysis by means of quantum molecular similarity measures of density distributions derived from conventional ab initio and density functional methods

A procedure based on quantum molecular similarity measures (QMSM) has been used to compare electron densities obtained from conventional ab initio and density functional methodologies at their respective optimized geometries. This method has been applied to a series of small molecules which have experimentally known properties and molecular bonds of diverse degrees of ionicity and covalency. Results show that in most cases the electron densities obtained from density functional methodologies are of a similar quality than post‐Hartree–Fock generalized densities. For molecules where Hartree–Fock methodology yields erroneous results, the density functional methodology is shown to yield usually more accurate densities than those provided by the second order Mo/ller–Plesset perturbation theory.

On the calculation of ab initio quantum molecular similarities for large systems: fitting the electron density

A set of procedures for rapid calculation of quantum molecular similarities from ab initio wave functions is discussed. In all cases a density fitting is carried out to eliminate the need of calculating costly four‐centered integrals. It is proved that this methodology can be applied to large systems to reproduce exact quantum molecular similarity measures at an extremely low computational cost.

Chemical and Biological Profiling of an Annotated Compound Library Directed to the Nuclear Receptor Family

Nuclear receptors form a family of ligand-activated transcription factors that regulate a wide variety of biological processes and are thus generally considered relevant targets in drug discovery. We have constructed an annotated compound library directed to nuclear receptors (NRacl) as a means for integrating the chemical and biological data being generated within this family. Special care has been put in the appropriate storage of annotations by using hierarchical classification schemes for both molecules and nuclear receptors, which takes the ability to extract knowledge from annotated compound libraries to another level. Analysis of NRacl has ultimately led to the identification of scaffolds with highly promiscuous nuclear receptor profiles and to the classification of nuclear receptor groups with similar scaffold promiscuity patterns. This information can be exploited in the design of probing libraries for deorphanization activities as well as for devising screening batteries to address selectivity issues.

A molecular field-based similarity approach to pharmacophoric pattern recognition.

The use of molecular field-based similarity approaches for obtaining quality molecular alignments and for identifying field-based patterns in bioactive molecules is described. In addition to pairwise similarities, computation of multimolecule similarities affords a means for determining consensus multimolecule alignments. These multimolecule alignments constitute the basis for developing models for the relative binding of bioactive molecules to common protein-binding sites and for the graphical portrayal of molecular field similarity surface plots that identify, visually, molecular regions possessing similar molecular field characteristics. The latter information can then be exploited in the design of molecules that mimic appropriate characteristics of these highly similar steric and electrostatic domains. Regions with low steric and electrostatic similarity in suitably aligned sets of bioactive molecules represent tolerant domains where new structural motifs can be incorporated without significant reductions in activity. To illustrate the potential applicability of the actual molecular field-based similarity approaches to the design of bioactive molecules, a study on a set of HIV-1 protease inhibitors is presented.