Anatomy of fingerprint search calculations on structurally diverse sets of active compounds

Similarity searching using molecular fingerprints is a widely used approach for the identification of novel hits. A fingerprint search involves many pairwise comparisons of bit string representations of known active molecules with those precomputed for database compounds. Bit string overlap, as evaluated by various similarity metrics, is used as a measure of molecular similarity. Results of a number of studies focusing on fingerprints suggest that it is difficult, if not impossible, to develop generally applicable search parameters and strategies, irrespective of the compound classes under investigation. Rather, more or less, each individual search problem requires an adjustment of calculation conditions. Thus, there is a need for diagnostic tools to analyze fingerprint-based similarity searching. We report an analysis of fingerprint search calculations on different sets of structurally diverse active compounds. Calculations on five biological activity classes were carried out with two fingerprints in two compound source databases, and the results were analyzed in histograms. Tanimoto coefficient (Tc) value ranges where active compounds were detected were compared to the distribution of Tc values in the database. The analysis revealed that compound class-specific effects strongly influenced the outcome of these fingerprint calculations. Among the five diverse compound sets studied, very different search results were obtained. The analysis described here can be applied to determine Tc intervals where scaffold hopping occurs. It can also be used to benchmark fingerprint calculations or estimate their probability of success.     

Bounds and algorithms for fast exact searches of chemical fingerprints in linear and sublinear time

Chemical fingerprints are used to represent chemical molecules by recording the presence or absence, or by counting the number of occurrences, of particular features or substructures, such as labeled paths in the 2D graph of bonds, of the corresponding molecule. These fingerprint vectors are used to search large databases of small molecules, currently containing millions of entries, using various similarity measures, such as the Tanimoto or Tversky's measures and their variants. Here, we derive simple bounds on these similarity measures and show how these bounds can be used to considerably reduce the subset of molecules that need to be searched. We consider both the case of single-molecule and multiple-molecule queries, as well as queries based on fixed similarity thresholds or aimed at retrieving the top K hits. We study the speedup as a function of query size and distribution, fingerprint length, similarity threshold, and database size |D| and derive analytical formulas that are in excellent agreement with empirical values. The theoretical considerations and experiments show that this approach can provide linear speedups of one or more orders of magnitude in the case of searches with a fixed threshold, and achieve sublinear speedups in the range of O(|D|0.6) for the top K hits in current large databases. This pruning approach yields subsecond search times across the 5 million compounds in the ChemDB database, without any loss of accuracy.     

Collection and preparation of molecular databases for virtual screening

Drug discovery and development research is undergoing a paradigm shift from a linear and sequential nature of the various steps involved in the drug discovery process of the past to the more parallel approach of the present, due to a lack of sufficient correlation between activities estimated by in vitro and in vivo assays. This is attributed to the non-drug-likeness of the lead molecules, which has often been detected at advanced drug development stages. Thus a striking aspect of this paradigm shift has been early/parallel in silico prioritization of drug-like molecular databases (also database pre-processing), in addition to prioritizing compounds with high affinity and selectivity for a protein target. In view of this, a drug-like database useful for virtual screening has been created by prioritizing molecules from 36 catalog suppliers, using our recently derived binary QSAR based drug-likeness model as a filter. The performance of this model was assessed by a comparative evaluation with respect to commonly used filters implemented by the ZINC database. Since the model was derived considering all the limitations that have plagued the existing rules and models, it performs better than the existing filters and thus the molecules prioritized by this filter represent a better subset of drug-like compounds. The application of this model on exhaustive subsets of 4,972,123 molecules, many of which have passed the ZINC database filters for drug-likeness, led to a further prioritization of 2,920,551 drug-like molecules. This database may have a great potential for in silico virtual screening for discovering molecules, which may survive the later stages of the drug development research.     

Uncovering the pharmacological mechanism of motherwort (Leonurus japonicus Houtt.) for treating menstrual disorders: A systems pharmacology approach

Leonurus japonicus (motherwort) is a traditional Chinese medicine that is widely used to treat menstrual disorders (MDs). However, the pharmacological mechanisms that underlie its clinical application remain unclear. In this study, a network pharmacology-based approach was used that integrated drug-likeness evaluation, oral bioavailability prediction, target exploration, network construction, bioinformatic annotation and molecular docking to investigate the mechanisms that underlie motherwort treatment for MDs. In total, 29 bioactive compounds were collected from 51 compounds in motherwort, which shared 17 common MDs-related targets. Network analysis indicated that motherwort played a therapeutic role in MDs treatment through multiple components that acted on multiple targets. Pathway enrichment analysis showed that the putative targets of motherwort were primarily involved in various pathways associated with the endocrine system, cancers, vascular system, and anti-inflammation process. Notably, five targets (i.e., AKT1, PTGS2, ESR1, AR and PPARG) were screened as hub genes based on a degree algorithm. Moreover, most of the bioactive components in motherwort had good binding ability with these genes, implying that motherwort could regulate their biological function. Collectively, this study elucidated the molecular mechanisms that underlay the efficiency of motherwort against MDs and demonstrated the potential of network pharmacology as an approach to uncover the action mechanism of herbal medicines.     

Collection and preparation of molecular databases for virtual screening

Drug discovery and development research is undergoing a paradigm shift from a linear and sequential nature of the various steps involved in the drug discovery process of the past to the more parallel approach of the present, due to a lack of sufficient correlation between activities estimated by in vitro and in vivo assays. This is attributed to the non-drug-likeness of the lead molecules, which has often been detected at advanced drug development stages. Thus a striking aspect of this paradigm shift has been early/parallel in silico prioritization of drug-like molecular databases (also database pre-processing), in addition to prioritizing compounds with high affinity and selectivity for a protein target. In view of this, a drug-like database useful for virtual screening has been created by prioritizing molecules from 36 catalog suppliers, using our recently derived binary QSAR based drug-likeness model as a filter. The performance of this model was assessed by a comparative evaluation with respect to commonly used filters implemented by the ZINC database. Since the model was derived considering all the limitations that have plagued the existing rules and models, it performs better than the existing filters and thus the molecules prioritized by this filter represent a better subset of drug-like compounds. The application of this model on exhaustive subsets of 4,972,123 molecules, many of which have passed the ZINC database filters for drug-likeness, led to a further prioritization of 2,920,551 drug-like molecules. This database may have a great potential for in silico virtual screening for discovering molecules, which may survive the later stages of the drug development research.     

METAPRINT: a metabolic fingerprint. Application to cassette design for high-throughput ADME screening

METAPRINT, a metabolic fingerprint, has been developed by predicting metabolic pathways and corresponding potential metabolites. Calculated drug-likeness parameters (log P and MW) have been incorporated into METAPRINT to allow the encoding of metabolic diversity within a chemical library. The application of METAPRINT in the design of cassette dosing experiments is demonstrated using a library of alpha-1a antagonists synthesized at Glaxo Wellcome. Results obtained by Ward's clustering algorithm suggest that METAPRINTs are able to discriminate between low- and high-clearance compounds. Cassette design was performed by maximizing the intracassette Euclidean distances between compounds in METAPRINT space, using simulated annealing. Calculated distances in METAPRINT space were in accordance with experimental data.     

Phytochemical databases of Chinese herbal constituents and bioactive plant compounds with known target specificities

Two databases have been constructed to facilitate applications of cheminformatics and molecular modeling to medicinal plants. The first contains data on known chemical constituents of 240 commonly used Chinese herbs, the other contains information on target specificities of bioactive plant compounds. Structures are available for all compounds. In the case of the Chinese herbal constituents database, further details include trivial and systematic names, compound class and skeletal type, botanical and Chinese (pinyin) names of associated herb(s), CAS registry number, chirality, pharmacological and toxicological information, and chemical references. For the bioactive plant compounds database, details of molecular target(s), IC50 and related measures, and associated botanical species are given. For Chinese herbs, approximately 7000 unique compounds are listed, though some are found in more than one herb, the total number for all herbs being 8264. For bioactive plant compounds, 2597 compounds active against 78 molecular targets are covered. Statistical relationships within and between the two databases are explored.     

Representation of the molecular topology of cyclical structures by means of cycle graphs. 2. Application to clustering of chemical databases

The great size of chemical databases and the high computational cost required in the atom-atom comparison of molecular structures for the calculation of the similarity between two chemical compounds necessitate the proposal of new clustering models with the aim of reducing the time of recovery of a set of molecules from a database that satisfies a range of similarities with regard to a given molecule pattern. In this paper we make use of the information corresponding to the cycles existing in the structure of molecules as an approach for the classification of chemical databases. The clustering method here proposed is based on the representation of the topological structure of molecules stored in chemical databases through its corresponding cycle graph. This method presents a more appropriate behavior for others described in the bibliography in which the information corresponding to the cyclicity of the molecules is also used.     

Using the gini coefficient to measure the chemical diversity of small‐molecule libraries

Modern databases of small organic molecules contain tens of millions of structures. The size of theoretically available chemistry is even larger. However, despite the large amount of chemical information, the “big data” moment for chemistry has not yet provided the corresponding payoff of cheaper computer‐predicted medicine or robust machine‐learning models for the determination of efficacy and toxicity. Here, we present a study of the diversity of chemical datasets using a measure that is commonly used in socioeconomic studies. We demonstrate the use of this diversity measure on several datasets that were constructed to contain various congeneric subsets of molecules as well as randomly selected molecules. We also apply our method to a number of well‐known databases that are frequently used for structure‐activity relationship modeling. Our results show the poor diversity of the common sources of potential lead compounds compared to actual known drugs. © 2016 Wiley Periodicals, Inc.     

Fingerprint scaling increases the probability of identifying molecules with similar activity in virtual screening calculations.

Results of systematic virtual screening calculations using a structural key-type fingerprint are reported for compounds belonging to 14 activity classes added to randomly selected synthetic molecules. For each class, a fingerprint profile was calculated to monitor the relative occupancy of fingerprint bit positions. Consensus bit patterns were determined consisting of all bits that were always set on in compounds belonging to a specific activity class. In virtual screening calculations, scale factors were applied to each consensus bit position in fingerprints of query molecules. This technique, called "fingerprint scaling", effectively increases the weight of consensus bit positions in fingerprint comparisons. Although overall prediction accuracy was satisfactory using unscaled calculations, scaling significantly increased the number of correct predictions but only slightly increased the rate of false positives. These observations suggest that fingerprint scaling is an attractive approach to increase the probability of identifying molecules with similar activity by virtual screening. It requires the availability of a series of related compounds and can be easily applied to any keyed fingerprint representation that associates bit positions with specific molecular features.     

Pharmacophore features of potential drugs

Drug discovery efforts rely increasingly on the identification of quality lead compounds through high-throughput synthesis and screening. However, large-scale random libraries have yielded only a low number of quality lead molecules. To address this shortcoming researchers have paid more attention to the concept of "drug-likeness" of molecules in combinatorial and screening libraries. Database profiling and analysis methods have been employed to identify the structural features of known drug molecules. Neural networks and machine learning methods help to distinguish between drugs and nondrugs. More recently, database-independent pharmacophore filters have been introduced that provide simple intuitive rules to classify potential drugs.     

Identification of critical chemical features for Aurora kinase-B inhibitors using Hip-Hop, virtual screening and molecular docking

This study was performed to find the selective chemical features for Aurora kinase-B inhibitors using the potent methods like Hip-Hop, virtual screening, homology modeling, molecular dynamics and docking. The best hypothesis, Hypo1 was validated toward a wide range of test set containing the selective inhibitors of Aurora kinase-B. Homology modeling and molecular dynamics studies were carried out to perform the molecular docking studies. The best hypothesis Hypo1 was used as a 3D query to screen the chemical databases. The screened molecules from the databases were sorted based on ADME and drug like properties. The selective hit compounds were docked and the hydrogen bond interactions with the critical amino acids present in Aurora kinase-B were compared with the chemical features present in the Hypo1. Finally, we suggest that the chemical features present in the Hypo1 are vital for a molecule to inhibit the Aurora kinase-B activity.