QSAR using 2D descriptors and TRIPOS' SIMCA

Rational ligand-based virtual screening and structure-activity relationship studies in the ligand-binding domain of the glucocorticoid receptor-α

The interest in developing synthetic glucocorticoids (GCs) arises from the utility of endogenous steroids as potent anti-inflammatory and immunosuppressant agents. The first GCs to be discovered, such as cortisol or dexamethasone, still represent the main treatment for conditions of the inflammatory process, despite the fact that they carry a significant risk of side effects. Hence, there is a continuing need to find drugs that preserve the immune effects of GCs without the side effects, such as those on metabolism (diabetes), bone tissue (osteoporosis), muscles (myopathy), eyes and skin. In this review, we focus on the recent use of ligand-based computational approaches in glucocorticoid receptor (GR) drug-design efforts for the determination of novel GR ligands. We examine a number of ligand-based (similarity searches, pharmacophore screens and quantitative structure-activity relationships) approaches that have been implemented in recent years. A recent virtual high-throughput screening similarity search was successful in developing a novel series of nonsteroidal GR antagonists. Additionally, there has been considerable success in ligand-based structure-analysis relationship generation and lead optimization studies for the GR. Future trends toward integrated GR ligand design incorporating ligand- and structure-based methodologies are inevitable.

Biomolecular Structure and Modeling: Historical Perspective

physics, chemistry, and biology have been connected by a web of causal explanation organized by induction-based theories that telescope into one another. … Thus, quantum theory underlies atomic physics, which is the foundation of reagent chemistry and its specialized offshoot biochemistry, which interlock with molecular biology — essentially, the chemistry of organic macromolecules — and hence, through successively higher levels of organization, cellular, organismic, and evolutionary biology. … Such is the unifying and highly productive understanding of the world that has evolved in the natural sciences.

Using multidimensional patterns of amino acid attributes for QSAR analysis of peptides

On the basis of exploratory factor analysis, six multidimensional patterns of 516 amino acid attributes, namely, factor analysis scales of generalized amino acid information (FASGAI) involving hydrophobicity, alpha and turn propensities, bulky properties, compositional characteristics, local flexibility and electronic properties, are proposed to represent structures of 48 bitter-tasting dipeptides and 58 angiotensin-converting enzyme inhibitors. Characteristic parameters related to bioactivities of the peptides studied are selected by genetic algorithm, and quantitative structure-activity relationship (QSAR) models are constructed by partial least square (PLS). Our results by a leave-one-out cross validation are compared with the previously known structure representation method and are shown to give slightly superior or comparative performance. Further, two data sets are divided into training sets and test sets to validate the characterization repertoire of FASGAI. Performance of the PLS models developed by training samples by a leave-one-out cross validation and external validation for test samples are satisfying. These results demonstrate that FASGAI is an effective representation technique of peptide structures, and that FASGAI vectors have many preponderant characteristics such as straightforward physicochemical information, high characterization competence and easy manipulation. They can be further applied to investigate the relationship between structures and functions of various peptides, even proteins.

Spline-fitting with a genetic algorithm: a method for developing classification structure-activity relationships

Classification methods allow for the development of structure-activity relationship models when the target property is categorical rather than continuous. We describe a classification method which fits descriptor splines to activities, with descriptors selected using a genetic algorithm. This method, which we identify as SFGA, is compared to the well-established techniques of recursive partitioning (RP) and soft independent modeling by class analogy (SIMCA) using five series of compounds: cyclooxygenase-2 (COX-2) inhibitors, benzodiazepine receptor (BZR) ligands, estrogen receptor (ER) ligands, dihydrofolate reductase (DHFR) inhibitors, and monoamine oxidase (MAO) inhibitors. Only 1-D and 2-D descriptors were used. Approximately 40% of compounds in each series were assigned to a test set, "cherry-picked" from the complete set such that they lie outside the training set as much as possible. SFGA produced models that were more predictive for all but the DHFR set, for which SIMCA was most predictive. RP gave the least predictive models for all but the MAO set. A similar trend was observed when using training and test sets to which compounds were randomly assigned and when gradually eliminating compounds from the (designed) training set. The stability of models was examined for the random and reduced sets, where stability means that classification statistics and the selected descriptors are similar for models derived from different sets. Here, SIMCA produced the most stable models, followed by SFGA and RP. We show that a consensus approach that combines all three methods outperforms the single best model for all data sets.

Predicting drug pharmacokinetic properties using molecular interaction fields and SIMCA

We have developed a method that combines molecular interaction fields with soft independent modeling of class analogy (SIMCA) to predict pharmacokinetic drug properties. Several additional considerations to those made in traditional QSAR are required in order to develop a successful QSPR strategy that is capable of accommodating the many complex factors that contribute to key pharmacokinetic properties such as ADME (absorption, distribution, metabolism, and excretion) and toxicology. An accurate prediction of oral bioavailability, for example, requires that absorption and first-pass hepatic elimination both be taken into consideration. To accomplish this, general properties of molecules must be related to their solubility and ability to penetrate biological membranes, and specific features must be related to their particular metabolic and toxicological profiles. Here we describe a method, which is applicable to structurally diverse data sets while utilizing as much detailed structural information as possible. We address the issue of the molecular alignment of a structurally diverse set of compounds using idiotropic field orientation (IFO), a generalization of inertial field orientation. We have developed a second flavor of this method, which directly incorporates electrostatics into the molecular alignment. Both variations of IFO produce a characteristic orientation for each structure and the corresponding molecular fields can then be analyzed using SIMCA. Models are presented for human intestinal absorption, blood-brain barrier penetration and bioavailability to demonstrate ways in which this tool can be used early in the drug development process to identify leads likely to exhibit poor pharmacokinetic behavior in pre-clinical studies, and we have explored the influence of conformation and molecular field type on the statistical properties of the models obtained.

General topological patterns of known drugs

Discriminating "drug-like" from "non-drug-like" compounds is a relatively emerging topic within the drug research. The basic assumption is that it is possible to obtain relevant information from structural features common to the known drugs, in order to discard a huge number of candidate chemical structures with low probability of becoming drugs. A graph-theoretical contribution to this subject is reported in this paper, by making exclusive use of linear relationships. The results suggest that it is possible to achieve a pattern of general pharmacological activity based on molecular topology. Conclusions are tentative pending verification of the results with larger compound libraries.

Computation of the physio-chemical properties and data mining of large molecular collections

Very large data sets of molecules screened against a broad range of targets have become available due to the advent of combinatorial chemistry. This information has led to the realization that ADME (absorption, distribution, metabolism, and excretion) and toxicity issues are important to consider prior to library synthesis. Furthermore, these large data sets provide a unique and important source of information regarding what types of molecular shapes may interact with specific receptor or target classes. Thus, the requirement for rapid and accurate data mining tools became paramount. To address these issues Pharmacopeia, Inc. formed a computational research group, The Center for Informatics and Drug Discovery (CIDD).* In this review we cover the work done by this group to address both in silico ADME modeling and data mining issues faced by Pharmacopeia because of the availability of a large and diverse collection (over 6 million discrete compounds) of drug-like molecules. In particular, in the data mining arena we discuss rapid docking tools and how we employ them, and we describe a novel data mining tool based on a ID representation of a molecule followed by a molecular sequence alignment step. For the ADME area we discuss the development and application of absorption, blood-brain barrier (BBB) and solubility models. Finally, we summarize the impact the tools and approaches might have on the drug discovery process.

CoMFA-SIMCA Model for antichagasic nitrofurazone derivatives

The physico-chemical properties of some 5-nitro-2-furaldehyde semicarbazones (nitrofurazones) and thiophene analogues were compared with their in vitro and in vivo trypanocidal activity against Trypanosoma cruzi (Tulahuen strain). 3D-QSAR models were obtained by applying the SIMCA methodology to the electrostatic and steric fields (CoMFA fields) of the molecules. Nitrofurazones bearing N4 substituents. which cover a range of 14-17 A from the nitro group with a thickness of about 6 A when considering the extended conformer. produced complete survival in infected mice. The in vitro model allows larger N4 substituents than the SURVival model, but they must not bear positive centres in the region 15-16 A from the nitro group. Moreover, the in vitro model is in agreement with the active site of trypanothione reductase (TR). Both models can be of use in the design of novel drugs bearing an amide-like group at a distance of 7-9 A from an easily reducible group.