False Positives in the Early Stages of Drug Discovery

Abstract

High-throughput screening (HTS) is one of the most powerful approaches available for identifying new lead compounds for the growing catalogue of validated drug targets. However, just as virtual and experimental HTS have accelerated lead identification and changed drug discovery, they have also introduced a large number of peculiar molecules. Some of these have turned out to be interesting for further optimization, others to be dead ends when attempts are made to optimize their activity, typically after a great deal of time and resources have been devoted. Such false positive hits are still one of the key problems in the field of HTS and in the early stages of drug discovery in general. Many studies have been devoted to understanding the origins of false-positives, and the findings have been incorporated in filters and methods that can predict and eliminate problematic molecules from further consideration. This paper will focus on the structural classes and known mechanisms of nonleadlike false positives, together with experimental and computational methods for identifying such compounds.

Development and Use of Assay Conditions Suited to Screening for and Profiling of SET-Domain-Targeted Inhibitors of the MLL/SET1 Family of Lysine Methyltransferases

“…On the other hand, and this need not conflict with the idea just mentioned, MLL1 may play a more widespread role in global H3K4 methylation than is generally suggested. 69,70 An ongoing concern for drug discovery efforts involving high-throughput screening of large compound collections are promiscuous inhibitors or nuisance compounds, 71,72 which by various mechanisms, such as chemical assay interference 73 or compound aggregation-based effects, 74 show up repeatedly, across multiple target classes, as false-positive hits. 75 A different but related concern is that hit compounds exhibit leadlike properties; that is, they have physical and chemical properties suitable for the downstream analytical and chemical modification procedures employed in the further steps of drug development.…”

Shaping a Screening File for Maximal Lead Discovery Efficiency and Effectiveness: Elimination of Molecular Redundancy

“…Applying both filters showed a surprising number of compounds picked up by only a single set of the filters. The merging of the Pfizer and Wyeth screening file gave us the opportunity to revisit the existing filter sets, to combine the experiences from both companies, and incorporate also new findings of structural filters from the literature, [40][41][42][43][44][45][46][47][48] in addition to the creation of a small number of new proprietary filters to remove for example, compounds with very low feature content (no heteroatoms and low structural complexity) and compounds with multiple unattractive features, such as several nitro groups. 48 This knowledge integration resulted in the generation of a combined set of around 540 molecular filters, 55 which were applied to eliminate compounds with structural flaws ahead of the redundancy reduction.…”

“…In parallel, surveying the recent literature describing structural filters allowed us to include additional substructure filters. [40][41][42][43][44][45][46][47][48] Overall, around 140 filters were newly created and combined with the existing filter set, resulting in 540 unique substructure queries.…”

DeepScreen: An Accurate, Rapid, and Anti‐Interference Screening Approach for Nanoformulated Medication by Deep Learning

“…Two kinds of nanoformulated drug system, inorganic‐layered double hydroxide loaded with etoposide (LDH‐VP16) and lipid‐based materials solid lipid nanoparticles loaded with Curcumin (SLN‐Cur), were chosen as test examples for our novel screening system 19, 20. Millions of drug‐treated single‐cell images were obtained from flow cytometry and used to build a classifier that evaluates the efficacy of the drug.…”