title: Data-Driven Derivation of an "Informer Compound Set" for Improved Selection of Active Compounds in High-Throughput Screening.
abstract: Despite the usefulness of high-throughput screening (HTS) in drug discovery, for some systems, low assay throughput or high screening cost can prohibit the screening of large numbers of compounds. In such cases, iterative cycles of screening involving active learning (AL) are employed, creating the need for smaller "informer sets" that can be routinely screened to build predictive models for selecting compounds from the screening collection for follow-up screens. Here, we present a data-driven derivation of an informer compound set with improved predictivity of active compounds in HTS, and we validate its benefit over randomly selected training sets on 46 PubChem assays comprising at least 300,000 compounds and covering a wide range of assay biology. The informer compound set showed improvement in BEDROC(α = 100), PRAUC, and ROCAUC values averaged over all assays of 0.024, 0.014, and 0.016, respectively, compared to randomly selected training sets, all with paired t-test p-values <10(-15). A per-assay assessment showed that the BEDROC(α = 100), which is of particular relevance for early retrieval of actives, improved for 38 out of 46 assays, increasing the success rate of smaller follow-up screens. Overall, we showed that an informer set derived from historical HTS activity data can be employed for routine small-scale exploratory screening in an assay-agnostic fashion. This approach led to a consistent improvement in hit rates in follow-up screens without compromising scaffold retrieval. The informer set is adjustable in size depending on the number of compounds one intends to screen, as performance gains are realized for sets with more than 3,000 compounds, and this set is therefore applicable to a variety of situations. Finally, our results indicate that random sampling may not adequately cover descriptor space, drawing attention to the importance of the composition of the training set for predicting actives.
authors.affiliation: Centre for Molecular Informatics, Department of Chemistry, University of Cambridge , Lensfield Road, CB2 1EW, Cambridge, United Kingdom.|Division of Medicinal Chemistry, Leiden Academic Centre for Drug Research, Leiden University , P.O. Box 9502, 2300 RA Leiden, The Netherlands.|Developmental & Molecular Pathways, Novartis Institutes for BioMedical Research , Novartis Pharma AG, Novartis Campus, 4056 Basel, Switzerland.
authors.forename: Shardul
authors.initials: S
authors.lastname: Paricharak
authors.affiliation: Division of Medicinal Chemistry, Leiden Academic Centre for Drug Research, Leiden University , P.O. Box 9502, 2300 RA Leiden, The Netherlands.
authors.forename: Adriaan P
authors.initials: AP
authors.lastname: IJzerman
authors.affiliation: Developmental & Molecular Pathways, Novartis Institutes for BioMedical Research , Cambridge, Massachusetts 02139, United States.
authors.forename: Jeremy L
authors.initials: JL
authors.lastname: Jenkins
authors.affiliation: Centre for Molecular Informatics, Department of Chemistry, University of Cambridge , Lensfield Road, CB2 1EW, Cambridge, United Kingdom.
authors.forename: Andreas
authors.initials: A
authors.lastname: Bender
authors.affiliation: Developmental & Molecular Pathways, Novartis Institutes for BioMedical Research , Novartis Pharma AG, Novartis Campus, 4056 Basel, Switzerland.
authors.forename: Florian
authors.initials: F
authors.lastname: Nigsch
mesh_terms: D004353:Drug Evaluation, Preclinical / Q000379:methods*; D057166:High-Throughput Screening Assays / Q000379:methods*; D048088:Informatics / Q000379:methods*; D000069550:Machine Learning