True prediction of lowest observed adverse effect levels.

A database of structurally heterogeneous chemical structures with their experimental values of Lowest Observed Adverse Effect Levels (LOAELs) was modeled using graph theoretical descriptors. Variable selection for multiple linear regression (MLR) and linear discriminant analysis (LDA) was accomplished by the Internal Test Set (ITS) method in order to achieve true predicted LOAEL values. The results obtained can be considered good if we take in count the structural diversity of the training set.

Predictability and prediction of lowest observed adverse effect levels in a structurally heterogeneous set of chemicals.

A database of chronic lowest observed adverse effect levels (LOAELs) for 234 compounds, previously compiled from different sources (Toxicology Letters79, 131-143 (1995)), was modelled using graph theoretical descriptors. This study reveals that data are not homogeneous. Only those data originating from the U.S. Environmental Protection Agency (EPA) reports could be well modelled by multilinear regression (MLR) and linear discriminant analysis (LDA). In contrast, data available from the specific procedures of the National Toxicology Program (NTP) database introduced noise and did not render good models either alone, or in combination with the EPA data.

Structural invariants for the prediction of relative toxicities of polychloro dibenzo-p-dioxins and dibenzofurans.

Multivariate models are reported that can predict the relative toxicity of compounds with severe environmental impact, namely polychloro dibenzo-p-dioxins (PCDDs) and dibenzofurans (PCDFs). Multiple linear regression analysis (MLR) and partial least square projections of latent variables (PLS) show the usefulness of graph-theoretical descriptors, mainly topological charge indices (TCIs), in these series. The general trends of the group are correctly reproduced and better results are presented than have previously been published. In general, the more toxic compounds exhibit more symmetric molecular structures.

Use of molecular topology for the prediction of physico-chemical, pharmacokinetic and toxicological properties of a group of antihistaminic drugs.

We used molecular connectivity to search mathematical models for predicting physico-chemical (e.g. the partition coefficient, P), pharmacokinetic (e.g. the time of maximum plasma level, and toxicological properties (lethal dose, LD) for a group of antihistaminic drugs. The results obtained clearly reveal the high efficiency of molecular topology for the prediction of these properties. Randomization and cross-validation by use of leave-one-out tests were also performed in order to assess the stability and the prediction ability of the connectivity functions selected.

Search of a topological pattern to evaluate toxicity of heterogeneous compounds.

Molecular connectivity has been applied to the search of mathematical models able to predict the carcinogenic and teratogenic activity of a wide group of structurally heterogeneous compounds. Through the linear discriminant analysis and the diagrams of distribution of pharmacological activity, the classification criteria that minimizes the percentage of error are established. The easiness and speed of the calculation of the descriptors used in this work make the models developed useful in data bases containing a huge number of compounds.

Virtual darwinian drug design: QSAR inverse problem, virtual combinatorial chemistry, and computational screening.

The generation of diversity and its further selection by an external system is a common mechanism for the evolution of the living species and for the current drug design methods. This assumption allows us to label the methods based on generation and selection of molecular diversity as "Darwinian" ones, and to distinguish them from the structure-based, structure-modulation approaches. An example of a Darwinian method is the inverse QSAR. It consists of the computational generation of candidate chemical structures and their selection according to a previously established QSAR model. New trends in the field of combinatorial chemical syntheses comprise the concepts of virtual combinatorial synthesis and virtual or computational screening. Virtual combinatorial synthesis, closely related to inverse QSAR, can be defined as the computational simulation of the generation of new chemical structures by using a combinatorial strategy to generate a virtual library. Virtual screening is the selection of chemical structures having potential desirable properties from a database or virtual library in order to be synthesized and assayed. This review is mainly focused on graph theoretical drug design approaches, but a survey with key references is provided that covers other simulation methods.

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.

Virtual combinatorial syntheses and computational screening of new potential anti-herpes compounds.

The activity of new anti-HSV-1 chemical structures, designed by virtual combinatorial chemical synthesis and selected by a computational screening, is determined by an in vitro assay. A virtual library of phenol esters and anilides was formed from two databases of building blocks: one with carbonyl fragments and the other containing both substituted phenoxy and phenylamino fragments. The library of virtually assembled compounds was computationally screened, and those compounds which were selected by our mathematical model as active ones were finally synthesized and tested. Our antiviral activity model is a "tandem" of four linear functions of topological graph-theoretical descriptors. A given chemical structure was selected as active if it satisfies every discriminant equation in that model. The final result was that five new structures were selected, synthesized, and tested: all of them demonstrated activity, and three showed appreciable anti-HSV-1 activity, with IC(50) values of 0.9 microM. The same model, applied to a database of known compounds, has identified the anti-herpes activity of the following compounds: 3,5-dimethyl-4-nitroisoxazole, nitrofurantoin, 1-(pyrrolidinocarbonylmethyl)piperazine, nebularine, cordycepin, adipic acid, thymidine, alpha-thymidine, inosine, 2, 4-diamino-6-(hydroxymethyl)pteridine, 7-(carboxymethoxy)-4-methylcoumarin, 5-methylcytidine, and others that showed less activity.

Prediction of properties of chiral compounds by molecular topology.

A common assumption in chemistry is that chiral behavior is associated with 3-D geometry. However, chiral information is related to symmetry, which allows the topological handling of chiral atoms by weighted graphs and the calculation of new descriptors that give a weight to the corresponding entry in the main diagonal of the topological matrix. In this study, it is demonstrated that, operating in this way, chiral topological indices are obtained that can differentiate the pharmacological activity between pairs of enantiomers. The 50% inhibitory concentration (IC50) values of the D2 dopamine receptor and the sigma receptor for a group of 3-hydroxy phenyl piperidines are specifically predicted. Moreover, the sedative character of a group of chiral barbiturates can be identified.

Antimicrobial activity characterization in a heterogeneous group of compounds.

In this work we carry out a study of pattern recognition to detect the microbiological activity in a group of heterogeneous compounds. The structural descriptors utilized are the topological connectivity indexes. The methods followed are stepwise linear discriminant analysis (linear analysis) and artificial neural network (nonlinear analysis). Although both methods are appropriate to differentiate between active and inactive compounds, the artificial neural network is, in this case, more adequate, since it shows in a test set a prediction success of 98%, versus 92% obtained with linear discriminant analysis.

Pharmacological studies of 1-(p-chlorophenyl)propanol and 2-(1-hydroxy-3-butenyl)phenol: two new non-narcotic analgesics designed by molecular connectivity.

Molecular topology has been applied to the design of new analgesic drugs. Linear discriminant analysis and connectivity functions were used to design two potentially suitable drugs which were synthesized and tested for analgesic properties by the acetic acid-induced abdominal constriction test in mice and the tail-flick test in rats. In mice, the compound 1-(p-chlorophenyl)propanol showed higher analgesic activity, both intraperitoneally and orally, than acetylsalicylic acid. 2-(1-Hydroxy-3-butenyl)phenol exhibited a lesser protective effect (70% of that shown by acetylsalicylic acid). In rats, acetylsalicylic acid gave the greatest protection against pain when administered intraperitoneally, while 1-(p-chlorophenyl)propanol was the most active orally. The 2-(1-hydroxy-3-butenyl)phenol, both intraperitoneally and orally, showed the least protective effect. These results demonstrated the peripheral analgesic properties of the selected compounds, thus confirming the validity of the molecular design method.

Pharmacological distribution diagrams: a tool for de novo drug design.

Discriminant analysis applied to SAR studies using topological descriptors allows us to plot frequency distribution diagrams: a function of the number of drugs within an interval of values of discriminant function vs. these values. We make use of these representations, pharmacological distribution diagrams (PDDs), in structurally heterogeneous groups where generally they adopt skewed Gaussian shapes or present several maxima. The maxima afford intervals of discriminant function in which exists a good expectancy to find new active drugs. A set of beta-blockers with contrasted activity has been selected to test the ability of PDDs as a visualizing technique, for the identification of new beta-blocker active compounds.

Topological approach to drug design.

In this paper we demonstrated that by an adequate combination of different topological indices it is possible to select and design new active compounds in different therapeutical scopes, with a very high efficiency level. Particularly successful in the search of new "lead drugs", the results show the surprising ability of the topological methods to describe molecular structures.