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How promiscuous are pharmaceutically relevant compounds? A data-driven assessment.

ABSTRACT: Given the increasing notion of target promiscuity of bioactive compounds and polypharmacological drug behavior, a detailed analysis of publicly available compound activity data from medicinal chemistry sources was carried out to determine and quantify the degree of promiscuity of active compounds across all known human target families. The results are surprising. Approximately 62% of currently available compounds with high-confidence activity data are only annotated with a single biological target, whereas 36% are known to act against multiple targets within the same family (i.e., closely related targets). However, only ~2% of bioactive compounds are promiscuous across different target families. Thus, despite general data sparseness, these findings indicate that highly promiscuous bioactive compounds only rarely occur. Because pharmaceutically relevant active compounds represent the pool from which drug candidates emerge, one might extrapolate from these results and conclude that there is a low statistical probability to obtain drugs that act against multiple targets belonging to distinct families.

SUBMITTER: Hu Y

PROVIDER: S-EPMC3535109 | biostudies-literature | 2013 Jan

REPOSITORIES: biostudies-literature

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How promiscuous are pharmaceutically relevant compounds? A data-driven assessment.

Hu Ye Y Bajorath Jürgen J

The AAPS journal 20121023 1

Given the increasing notion of target promiscuity of bioactive compounds and polypharmacological drug behavior, a detailed analysis of publicly available compound activity data from medicinal chemistry sources was carried out to determine and quantify the degree of promiscuity of active compounds across all known human target families. The results are surprising. Approximately 62% of currently available compounds with high-confidence activity data are only annotated with a single biological targ ...[more]

PMID: 23090085

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Project description:BackgroundIn drug discovery, a positive Ames test for bacterial mutation presents a significant hurdle to advancing a drug to clinical trials. In a previous paper, we discussed success in predicting the genotoxicity of reagent-sized aryl-amines (ArNH2), a structure frequently found in marketed drugs and in drug discovery, using quantum mechanics calculations of the energy required to generate the DNA-reactive nitrenium intermediate (ArNH:+). In this paper we approach the question of what molecular descriptors could improve these predictions and whether external data sets are appropriate for further training.ResultsIn trying to extend and improve this model beyond this quantum mechanical reaction energy, we faced considerable difficulty, which was surprising considering the long history and success of QSAR model development for this test. Other quantum mechanics descriptors were compared to this reaction energy including AM1 semi-empirical orbital energies, nitrenium formation with alternative leaving groups, nitrenium charge, and aryl-amine anion formation energy. Nitrenium formation energy, regardless of the starting species, was found to be the most useful single descriptor. External sets used in other QSAR investigations did not present the same difficulty using the same methods and descriptors. When considering all substructures rather than just aryl-amines, we also noted a significantly lower performance for the Novartis set. The performance gap between Novartis and external sets persists across different descriptors and learning methods. The profiles of the Novartis and external data are significantly different both in aryl-amines and considering all substructures. The Novartis and external data sets are easily separated in an unsupervised clustering using chemical fingerprints. The chemical differences are discussed and visualized using Kohonen Self-Organizing Maps trained on chemical fingerprints, mutagenic substructure prevalence, and molecular weight.ConclusionsDespite extensive work in the area of predicting this particular toxicity, work in designing and publishing more relevant test sets for compounds relevant to drug discovery is still necessary. This work also shows that great care must be taken in using QSAR models to replace experimental evidence. When considering all substructures, a random forest model, which can inherently cover distinct neighborhoods, built on Novartis data and previously reported external data provided a suitable model.

Project description:Scientists rely on high-throughput screening tools to identify promising small-molecule compounds for the development of biochemical probes and drugs. This study focuses on the identification of promiscuous bioactive compounds, which are compounds that appear active in many high-throughput screening experiments against diverse targets but are often false-positives which may not be easily developed into successful probes. These compounds can exhibit bioactivity due to nonspecific, intractable mechanisms of action and/or by interference with specific assay technology readouts. Such "frequent hitters" are now commonly identified using substructure filters, including pan assay interference compounds (PAINS). Herein, we show that mechanistic modeling of small-molecule reactivity using deep learning can improve upon PAINS filters when modeling promiscuous bioactivity in PubChem assays. Without training on high-throughput screening data, a deep learning model of small-molecule reactivity achieves a sensitivity and specificity of 18.5% and 95.5%, respectively, in identifying promiscuous bioactive compounds. This performance is similar to PAINS filters, which achieve a sensitivity of 20.3% at the same specificity. Importantly, such reactivity modeling is complementary to PAINS filters. When PAINS filters and reactivity models are combined, the resulting model outperforms either method alone, achieving a sensitivity of 24% at the same specificity. However, as a probabilistic model, the sensitivity and specificity of the deep learning model can be tuned by adjusting the threshold. Moreover, for a subset of PAINS filters, this reactivity model can help discriminate between promiscuous and nonpromiscuous bioactive compounds even among compounds matching those filters. Critically, the reactivity model provides mechanistic hypotheses for assay interference by predicting the precise atoms involved in compound reactivity. Overall, our analysis suggests that deep learning approaches to modeling promiscuous compound bioactivity may provide a complementary approach to current methods for identifying promiscuous compounds.

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How promiscuous are pharmaceutically relevant compounds? A data-driven assessment.

Publications

How promiscuous are pharmaceutically relevant compounds? A data-driven assessment.

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