Design and Evaluation of a Novel Class-Directed 2D Fingerprint to Search for Structurally Diverse Active Compounds

Eckert, Hanna; Bajorath, Jürgen

doi:10.1021/ci600303b

Cited by 39 publications

(47 citation statements)

References 31 publications

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“…In addition, for SVM-based ranking, different percentages of 2D-ZINC were used as negative training examples: 0.001%, 0.01%, 0.1%, and 1% corresponding to 14, 144, 1442, and 14 423 compounds, respectively. Activity classes are abbreviated according to Table 1. print search calculations mirror trends that are generally observed; 29,30 that is, search performance is compound-classdependent and varies among different fingerprint designs. In the calculations reported here, on average, Molprint2D produced the highest recovery rates, followed by Daylight, TGT, TGD, and MACCS.…”

Section: Virtual Screening Trialssupporting

confidence: 55%

Support-Vector-Machine-Based Ranking Significantly Improves the Effectiveness of Similarity Searching Using 2D Fingerprints and Multiple Reference Compounds

Geppert¹,

Horváth²,

Gärtner³

et al. 2008

J. Chem. Inf. Model.

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Similarity searching using molecular fingerprints is computationally efficient and a surprisingly effective virtual screening tool. In this study, we have compared ranking methods for similarity searching using multiple active reference molecules. Different 2D fingerprints were used as search tools and also as descriptors for a support vector machine (SVM) algorithm. In systematic database search calculations, a SVM-based ranking scheme consistently outperformed nearest neighbor and centroid approaches, regardless of the fingerprints that were tested, even if only very small training sets were used for SVM learning. The superiority of SVM-based ranking over conventional fingerprint methods is ascribed to the fact that SVM makes use of information about database molecules, in addition to known active compounds, during the learning phase.

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Section: Virtual Screening Trialssupporting

confidence: 55%

Support-Vector-Machine-Based Ranking Significantly Improves the Effectiveness of Similarity Searching Using 2D Fingerprints and Multiple Reference Compounds

Geppert¹,

Horváth²,

Gärtner³

et al. 2008

J. Chem. Inf. Model.

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“…This model consumes IPFs to measure the similarity of drug pairs and generate presumed DDIs from the non-intersecting interactions of a pair. The concept of IPFs is similar to the molecular structure fingerprint [23]. After identifying IPFs, the similarity between fingerprints are calculated using JC (refer Fig.…”

Section: Interaction Profile Fingerprintsmentioning

confidence: 99%

Predicting Drug-Drug Interactions for Premarket Drug Development Process: A Network Based Approach

Gunawardena

Weerasinghe

Wijesinghe

2018

Int J on Adv. in ICT for Emerging Countries

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Abstract-Drug-drug interactions (DDIs) are responsible for many serious adverse events; their detection is crucial for the safety of the patient but also it is very challenging. In recent years, several drugs have been withdrawn from the market due to interaction related Adverse Events (AEs).This study describes a model which can be used to predict novel DDIs based on the similarity of drug interaction candidates to drugs involved in established DDIs which can be used in a large scale to discover novel DDIs. This model is mainly based on the assumption that if drug A and drug B interact to produce a specific biological effect, then drugs similar to drug A (or drug B) are likely to interact with drug B (or drug A) to produce the same effect. We have created a drug network using the 2011 snapshot of a widely used drug safety database which utilizes 352 distinct drugs and contains 3 700 interactions. Then, it was used to develop the proposed model for predicting future DDIs. The target similarities and side effect similarities (P-score) were calculated for all selected pairs of drugs. Then, it was used to develop the proposed model for predicting future DDIs. The proposed model mainly follows two distinct approaches: 'Which forces the preservation of existing (known) DDIs' and 'Without forced to preserve existing DDIs.' Underneath each of these approaches, three different techniques: target similarity score, side effect similarity (P-score) and resulting score were used to retrieve novel DDIs.The proposed model was evaluated using the Drugbank 2014 snapshot as a gold standard for the same set of drugs which produce novel DDIs with an average accuracy of 95% and 92%, average AUC (Area Under the Curve) of 0.9834 and 0.8651 under each of these two approaches respectively.The results presented in this study demonstrate the usefulness of the proposed network based drug-drug interaction methodology as a promising approach. The method described in this article is very simple, efficient, and biologically sound.

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“…For comparison, similarity search calculations were also carried out using the complexity-independent PDR-FP. [20] Results and Discussion…”

Section: Calculation Protocolsmentioning

confidence: 99%

“…[20] In contrast to bc-fingerprints, PDR-FP is compound-class-directed (that is, for each activity class, a unique PDR-FP consensus fingerprint is generated) and represents a rather specialized fingerprint type, requiring the application of a dot product metric for similarity assessment. [20] Due to the efforts involved in generating the reference set-specific versions of PDR-FP, we only constructed these fingerprints for the activity classes designed for MACCS. PDR-FP hit rates are reported in Table 7.…”

Section: Comparison Of Bc-maccs With Pdr-fpmentioning

confidence: 99%

“…[19] In contrast, thus far only one fingerprint has been designed that consistently produces a constant bit density, the property descriptor value range-derived fingerprint (PDR-FP). [20] In this case, value ranges of 93 property descriptors are binned into differently sized intervals such that the percentage of screening database compounds falling into each interval is exactly the same. For a test compound, the matching descriptor value range intervals are determined and for each descriptor, the bit representing the particular interval is set to "1".…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Rendering Conventional Molecular Fingerprints for Virtual Screening Independent of Molecular Complexity and Size Effects

Nisius

Bajorath

2010

ChemMedChem

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Molecular complexity and size effects represent a known complication of fingerprint similarity searching and virtual screening that often leads to an increase in false-positive rates and a decrease in hit rates. In standard fingerprints, differences in the complexity of reference and database molecules lead to different fingerprint bit densities, which negatively affects similarity search calculations, in particular, when fingerprints of reference molecules have higher bit density than corresponding fingerprints of database compounds. In pharmaceutical research, this is the case in many practical virtual screening applications when chemically optimized reference molecules are used. Herein we introduce an intuitive computational method to make standard fingerprints such as structural keys or pharmacophore feature fingerprints independent of molecular complexity and size effects. This is achieved by applying the concept of 'balanced codes' originating in computer science. Following this approach, binary fingerprints are transformed by incorporating the complement of their bit patterns. This straightforward transformation produces fingerprint representations with characteristic bit patterns that have exactly half of their bit positions set on, corresponding to a constant bit density of 50 % for all test compounds, regardless of their complexity and size. In similarity search calculations in the presence of complexity effects of increasing magnitude, transformed structural key and pharmacophore fingerprints display consistently better performance than their unmodified counterparts and recover active compounds in cases where the original fingerprints fail.

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Design and Evaluation of a Novel Class-Directed 2D Fingerprint to Search for Structurally Diverse Active Compounds

Cited by 39 publications

References 31 publications

Support-Vector-Machine-Based Ranking Significantly Improves the Effectiveness of Similarity Searching Using 2D Fingerprints and Multiple Reference Compounds

Support-Vector-Machine-Based Ranking Significantly Improves the Effectiveness of Similarity Searching Using 2D Fingerprints and Multiple Reference Compounds

Predicting Drug-Drug Interactions for Premarket Drug Development Process: A Network Based Approach

Rendering Conventional Molecular Fingerprints for Virtual Screening Independent of Molecular Complexity and Size Effects

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