Biodiversity of small molecules – a new perspective in screening set selection

Petrone, Paula; Wassermann, Anne Mai; Lounkine, Eugen; Kutchukian, Peter S.; Simms, B. T.; Jenkins, Jeremy L.; Selzer, Paul M.; Glick, Meir

doi:10.1016/j.drudis.2013.02.005

Cited by 59 publications

(76 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…21 Due to the retrospective nature of our analysis, for individual assays, between 21,362 and 39,959 compounds that had been screened were selected for the initial screen (Methods, Supplementary Table S3). …”

Section: ■ Resultsmentioning

confidence: 99%

Experimental Design Strategy: Weak Reinforcement Leads to Increased Hit Rates and Enhanced Chemical Diversity

Maciejewski¹,

Wassermann²,

Glick³

et al. 2015

J. Chem. Inf. Model.

Self Cite

View full text Add to dashboard Cite

High Throughput Screening (HTS) is a common approach in life sciences to discover chemical matter that modulates a biological target or phenotype. However, low assay throughput, reagents cost, or a flowchart that can deal with only a limited number of hits may impair screening large numbers of compounds. In this case, a subset of compounds is assayed, and in silico models are utilized to aid in iterative screening design, usually to expand around the found hits and enrich subsequent rounds for relevant chemical matter. However, this may lead to an overly narrow focus, and the diversity of compounds sampled in subsequent iterations may suffer. Active learning has been recently successfully applied in drug discovery with the goal of sampling diverse chemical space to improve model performance. Here we introduce a robust and straightforward iterative screening protocol based on naı̈ve Bayes models. Instead of following up on the compounds with the highest scores in the in silico model, we pursue compounds with very low but positive values. This includes unique chemotypes of weakly active compounds that enhance the applicability domain of the model and increase the cumulative hit rates. We show in a retrospective application to 81 Novartis assays that this protocol leads to consistently higher compound and scaffold hit rates compared to a standard expansion around hits or an active learning approach. We recommend using the weak reinforcement strategy introduced herein for iterative screening workflows.

show abstract

Section: ■ Resultsmentioning

confidence: 99%

Experimental Design Strategy: Weak Reinforcement Leads to Increased Hit Rates and Enhanced Chemical Diversity

Maciejewski¹,

Wassermann²,

Glick³

et al. 2015

J. Chem. Inf. Model.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The Laplacian-modified naıve Bayes classifier in PipelinePilot 33 8.5 (PP) was trained with the unfolded ECFP4. 10 The default parameters were used.…”

Section: ■ Methods and Materialsmentioning

confidence: 99%

Using Information from Historical High-Throughput Screens to Predict Active Compounds

Riniker

Wang

Jenkins

et al. 2014

J. Chem. Inf. Model.

101

View full text Add to dashboard Cite

Modern high-throughput screening (HTS) is a well-established approach for hit finding in drug discovery that is routinely employed in the pharmaceutical industry to screen more than a million compounds within a few weeks. However, as the industry shifts to more disease-relevant but more complex phenotypic screens, the focus has moved to piloting smaller but smarter chemically/biologically diverse subsets followed by an expansion around hit compounds. One standard method for doing this is to train a machine-learning (ML) model with the chemical fingerprints of the tested subset of molecules and then select the next compounds based on the predictions of this model. An alternative approach would be to take advantage of the wealth of bioactivity information contained in older (full-deck) screens using so-called HTS fingerprints, where each element of the fingerprint corresponds to the outcome of a particular assay, as input to machine-learning algorithms. We constructed HTS fingerprints using two collections of data: 93 in-house assays and 95 publicly available assays from PubChem. For each source, an additional set of 51 and 46 assays, respectively, was collected for testing. Three different ML methods, random forest (RF), logistic regression (LR), and naïve Bayes (NB), were investigated for both the HTS fingerprint and a chemical fingerprint, Morgan2. RF was found to be best suited for learning from HTS fingerprints yielding area under the receiver operating characteristic curve (AUC) values >0.8 for 78% of the internal assays and enrichment factors at 5% (EF(5%)) >10 for 55% of the assays. The RF(HTS-fp) generally outperformed the LR trained with Morgan2, which was the best ML method for the chemical fingerprint, for the majority of assays. In addition, HTS fingerprints were found to retrieve more diverse chemotypes. Combining the two models through heterogeneous classifier fusion led to a similar or better performance than the best individual model for all assays. Further validation using a pair of in-house assays and data from a confirmatory screen--including a prospective set of around 2000 compounds selected based on our approach--confirmed the good performance. Thus, the combination of machine-learning with HTS fingerprints and chemical fingerprints utilizes information from both domains and presents a very promising approach for hit expansion, leading to more hits. The source code used with the public data is provided.

show abstract

“…One notable exception is a recent retrospective analysis of the Novartis screening collection (9), showing that library subsets selected for high performance diversity achieve high hit rates in more assays than those selected for high chemical diversity alone. Performance diversity was measured as the number of unique target annotations for a set of compounds.…”

mentioning

confidence: 99%

“…One common practice is analyzing structural features of compounds to maximize chemical structural diversity. However, the success of this approach requires that similarities and differences in chemical structure be reflected in biological activities-a similarity principle known to have limited applicability (8,9). Other common strategies include controlling physicochemical parameters (10), exploiting natural selection by sourcing natural products, or relying on natural product-like analogs (11).…”

mentioning

confidence: 99%

Toward performance-diverse small-molecule libraries for cell-based phenotypic screening using multiplexed high-dimensional profiling

Wawer¹,

Li²,

Gústafsdóttir³

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

195

177

View full text Add to dashboard Cite

High-throughput screening has become a mainstay of small-molecule probe and early drug discovery. The question of how to build and evolve efficient screening collections systematically for cellbased and biochemical screening is still unresolved. It is often assumed that chemical structure diversity leads to diverse biological performance of a library. Here, we confirm earlier results showing that this inference is not always valid and suggest instead using biological measurement diversity derived from multiplexed profiling in the construction of libraries with diverse assay performance patterns for cell-based screens. Rather than using results from tens or hundreds of completed assays, which is resource intensive and not easily extensible, we use high-dimensional image-based cell morphology and gene expression profiles. We piloted this approach using over 30,000 compounds. We show that small-molecule profiling can be used to select compound sets with high rates of activity and diverse biological performance.chemical diversity | biological performance diversity | biological activity | chemical similarity P rofiling small molecules based on multiple biological activity measurements can illuminate mechanisms of action by comparing profiles with compounds whose mechanisms of action are known (1-5). Here, we describe a previously unidentified use of small-molecule profiling-enabling the creation of activityenriched and performance-diverse compound libraries for smallmolecule probe and drug discovery.Biochemical and cell-based high-throughput screening (HTS) is routinely used to discover novel bioactive molecules through unbiased testing of up to several million compounds per screen (6). However, despite ongoing advances in throughput, compound libraries will always represent only a small fraction of all relevant compounds theoretically accessible through chemical synthesis (a concept often referred to as "chemical space") (7). Library composition therefore presents a strong source of bias and potential limitation for any screening endeavor.There is little dissent about the notion that a good screening collection should yield many high-quality hits for a wide range of biological targets or phenotypes. In other words, it should be enriched for bioactive compounds and have high biological performance diversity. A high percentage of compounds lacking any activity will contribute to high cost and low performance of a high-throughput screen. A practical example is a compound collection containing a high percentage of compounds that fail to penetrate cell membranes-such a library will be unlikely to perform effectively in a cell-based HTS exploring an intracellular process. Similarly, a screening collection of compounds with highly redundant biological activities will be less efficient than an equally sized library with diverse performance (Fig. 1). A systematic path to reach these goals, however, remains elusive. One common practice is analyzing structural features of compounds to maximize chemical structural diversity. Ho...

show abstract

Biodiversity of small molecules – a new perspective in screening set selection

Cited by 59 publications

References 33 publications

Experimental Design Strategy: Weak Reinforcement Leads to Increased Hit Rates and Enhanced Chemical Diversity

Experimental Design Strategy: Weak Reinforcement Leads to Increased Hit Rates and Enhanced Chemical Diversity

Using Information from Historical High-Throughput Screens to Predict Active Compounds

Toward performance-diverse small-molecule libraries for cell-based phenotypic screening using multiplexed high-dimensional profiling

Contact Info

Product

Resources

About