Jeffrey W. Godden

Combinatorial preferences affect molecular similarity/diversity calculations using binary fingerprints and Tanimoto coefficients

A combinatorial method was developed to calculate complete distributions of the Tanimoto coefficient (Tc) for binary fingerprint (FP) representations of specified length, regardless of the chemical parameters they reflect. Theoretical Tc distributions were calculated for FPs consisting of up to 67 bit positions which revealed significant statistical preferences of certain Tc values. Calculation of Tc distributions in a large compound database using different FPs mirrored the effects identified by our general analysis. On the basis of these findings, an average Tc is biased by statistically preferred values.

Variability of molecular descriptors in compound databases revealed by Shannon entropy calculations

A method is introduced to calculate and compare the variability of molecular descriptors in compound databases. Descriptor variability analysis is based on histograms recording the distribution of molecular descriptors and calculation of Shannon entropy (SE), a metric originally applied in digital communication. SE values reflect the variability of descriptor settings. We have calculated a total of 92 molecular descriptors in the ACD and NCI databases and ranked them according to their variability. Significant differences in entropy are observed for a number of descriptors. However, the most variable descriptors are similar in the ACD and NCI databases. Such high-entropy descriptors are preferred tools to discriminate between compounds or account for the diversity of chemical libraries.

Profile Scaling Increases the Similarity Search Performance of Molecular Fingerprints Containing Numerical Descriptors and Structural Keys

The concept of compound class-specific profiling and scaling of molecular fingerprints for similarity searching is discussed and applied to newly designed fingerprint representations. The approach is based on the analysis of characteristic patterns of bits in keyed fingerprints that are set on in compounds having equivalent biological activity. Once a fingerprint profile is generated for a particular activity class, scaling factors that are weighted according to observed bit frequencies are applied to signature bit positions when searching for similar compounds. In systematic similarity search calculations over 23 diverse activity classes, profile scaling consistently increased the performance of fingerprints containing property descriptors and/or structural keys. A significant improvement of approximately 15% was observed for a new fingerprint consisting of binary encoded molecular property descriptors and structural keys. Under scaling conditions, this fingerprint, termed MP-MFP, correctly recognized on average close to 60% of all active test compounds, with only a few false positives. MP-MFP outperformed MACCS keys and other reference fingerprints. In general, optimum performance in scaling calculations was achieved at higher threshold values of the Tanimoto coefficient than in nonscaled calculations, thereby increasing the search selectivity. In general, putting relatively high weight on signature bit positions that were always, or almost always, set on was found to be the most effective scaling procedure. Analysis of class-specific search performance revealed that profile scaling of MP-MFP improved the similarity search results for each of the 23 activity classes.

IDENTIFICATION OF A PREFERRED SET OF MOLECULAR DESCRIPTORS FOR COMPOUND CLASSIFICATION BASED ON PRINCIPAL COMPONENT ANALYSIS

An algorithm based on principal component analysis was investigated to classify molecules in a database consisting of 455 compounds with activities against seven different biological targets. Diversity profiles of these compound sets were calculated and compared. To effectively classify compounds with similar biological activity, all possible combinations of 17 molecular descriptors were tested by complete factorial analysis, and preferred descriptor combinations were identified. High efficiency was achieved for a combination of a limited set of structural keys and two or three additional 2D descriptors. The performance of the approach was compared to Jarvis−Patrick clustering.

Evaluation of Descriptors and Mini-Fingerprints for the Identification of Molecules with Similar Activity

Combinations of 65 preferred 1D/2D molecular descriptors and 143 single structural keys were evaluated for their performance in compound classification focused on biological activity. The analysis was based on principal component analysis of descriptor combinations and facilitated by use of a genetic algorithm and different scoring functions. In these calculations, several descriptor combinations with greater than 95% prediction accuracy were identified. A set of 40 preferred structural keys was incorporated into a small binary fingerprint designed to search databases for compounds with biological activity similar to query molecules. The performance of mini-fingerprints was tested by systematic similarity search calculations in a database consisting of compounds belonging to seven biological activity classes, which had not been used to select effective descriptors. In these blind test calculations, mini-fingerprints correctly identified approximately 54% of compounds sharing similar biological activity and with 1% false positives. Thus, although the design of mini-fingerprints is conceptually simple, they perform well in activity-oriented similarity searching.

Molecular scaffold-based design and comparison of combinatorial libraries focused on the ATP-binding site of protein kinases.

Compound libraries were designed to target specifically the ATP cofactor-binding site in protein kinases by combining knowledge- and diversity-based design elements. A key aspect of the approach is the identification of molecular building blocks or scaffolds that are compatible with the binding site and therefore capture some aspects of target specificity. Scaffolds were selected on the basis of docking calculations and analysis of known inhibitors. We have generated 75 molecular scaffolds and applied different strategies to compute diverse compounds from scaffolds or, alternatively, to screen compound databases for molecules containing these scaffolds. The resulting libraries had a similar degree of molecular diversity, with at most 12% of the compounds being identical. However, their scaffold distributions differed significantly and a small number of scaffolds dominated the majority of compounds in each library.

Differential Shannon Entropy as a sensitive measure of differences in database variability of molecular descriptors.

A method termed Differential Shannon Entropy (DSE) is introduced to compare differences in information content and variance of molecular descriptors between compound databases. The analysis is based on histograms recording the individual and grouped distributions of molecular descriptors and calculation of Shannon entropy (SE), a formalism originally applied to digital communication. We have recently shown that SE values reflect the nonparametric variability of descriptor settings. Now the analysis has been advanced to assess differences in information content of 143 molecular descriptors in databases containing synthetic compounds, natural products, or drug-like molecules. The DSE metric captures the degree to which descriptor distributions complement or duplicate information contained in molecular databases. In our analysis, we observe significant differences for a number of descriptors and rank them according to their associated DSE values. Using DSE calculations, relative information content of different types of descriptors can be quantified, even if differences are subtle.

Mini-fingerprints detect similar activity of receptor ligands previously recognized only by three-dimensional pharmacophore-based methods.

Mini-fingerprints (MFPs) are short binary bit string representations of molecular structure and properties, composed of few selected two-dimensional (2D) descriptors and a number of structural keys. MFPs were specifically designed to recognize compounds with similar activity. Here we report that MFPs are capable of detecting similar activities of some druglike molecules, including endothelin A antagonists and alpha(1)-adrenergic receptor ligands, the recognition of which was previously thought to depend on the use of multiple point three-dimensional (3D) pharmacophore methods. Thus, in these cases, MFPs and pharmacophore fingerprints produce similar results, although they define, in terms of their complexity, opposite ends of the spectrum of methods currently used to study molecular similarity or diversity. For each of the studied compound classes, comparison of MFP bit settings identified a consensus or signature pattern. Scaling factors can be applied to these bits in order to increase the probability of finding compounds with similar activity by virtual screening.

Differential Shannon entropy analysis identifies molecular property descriptors that predict aqueous solubility of synthetic compounds with high accuracy in binary QSAR calculations

Prediction of aqueous solubility of organic molecules by binary QSAR was used as a test case for a recently introduced entropy-based descriptor selection method. Property descriptors suitable for solubility predictions were exclusively selected on the basis of Shannon entropy calculations in molecular learning sets, not taking any other information into account. Sets of only five or 10 2D descriptors with largest entropy differences between molecules above or below a defined solubility threshold yielded consistently high prediction accuracy between 80% and 90% in binary QSAR calculations, regardless of the threshold values applied. The top five descriptors with largest differential Shannon entropy (DSE) values achieved an average prediction accuracy of 88%. These findings suggest that differences in entropy and relative information content of descriptors in compared compound data sets correlate with significant differences in physical properties and support the practical relevance of entropy-based descriptor selection routines. The study also demonstrates that binary QSAR methodology can be effectively used to classify small molecules according to aqueous solubility.

Oxadiazols: a New Class of Rationally Designed Anti-Human Immunodeficiency Virus Compounds Targeting the Nuclear Localization Signal of the Viral Matrix Protein

ABSTRACT Despite recent progress in anti-human immunodeficiency virus (HIV) therapy, drug toxicity and emergence of drug-resistant isolates during long-term treatment of HIV-infected patients necessitate the search for new targets that can be used to develop novel antiviral agents. One such target is the process of nuclear translocation of the HIV preintegration complex. Previously we described a class of arylene bis(methylketone) compounds that inhibit HIV-1 nuclear import by targeting the nuclear localization signal (NLS) in the matrix protein (MA). Here we report a different class of MA NLS-targeting compounds that was selected using computer-assisted drug design. The leading compound from this group, ITI-367, showed potent anti-HIV activity in cultures of T lymphocytes and macrophages and also inhibited HIV-1 replication in ex vivo cultured lymphoid tissue. The virus carrying inactivating mutations in MA NLS was resistant to ITI-367. Analysis by real-time PCR demonstrated that the compound specifically inhibited nuclear import of viral DNA, measured by two-long terminal repeat circle formation. Evidence of the existence of this mechanism was provided by immunofluorescent microscopy, using fluorescently labeled HIV-1, which demonstrated retention of the viral DNA in the cytoplasm of drug-treated macrophages. Compounds inhibiting HIV-1 nuclear import may be attractive candidates for further development.