High-Throughput Screening Assay Datasets from the PubChem Database

Abstract

Availability of high-throughput screening (HTS) data in the public domain offers great potential to foster development of ligand-based computer-aided drug discovery (LB-CADD) methods crucial for drug discovery efforts in academia and industry. LB-CADD method development depends on high-quality HTS assay data, i.e., datasets that contain both active and inactive compounds. These active compounds are hits from primary screens that have been tested in concentration-response experiments and where the target-specificity of the hits has been validated through suitable secondary screening experiments. Publicly available HTS repositories such as PubChem often provide such data in a convoluted way: compounds that are classified as inactive need to be extracted from the primary screening record. However, compounds classified as active in the primary screening record are not suitable as a set of active compounds for LB-CADD experiments due to high false-positive rate. A suitable set of actives can be derived by carefully analysing results in often up to five or more assays that are used to confirm and classify the activity of compounds. These assays, in part, build on each other. However, often not all hit compounds from the previous screen have been tested. Sometimes a compound can be classified as ‘active’, though its meaning is ‘inactive’ on the target of interest as it is ‘active’ on a different target protein. Here, a curation process of hierarchically related confirmatory screens is illustrated based on two specifically chosen protein use-cases. The subsequent re-upload procedure into PubChem is described for the findings of those two scenarios. Further, we provide nine publicly accessible high quality datasets for future LB-CADD method development that provide a common baseline for comparison of future methods to the scientific community. We also provide a protocol researchers can follow to upload additional datasets for benchmarking.

Evaluation of Health Risk Level of Hand-Arm and Whole-Body Vibrations on the Technical Operators and Equipment in a Tobacco-Producing Company in Nigeria

“…e daily vibration exposure action value according to the European Directive [21] standard value is 0.5 m/sec 2 [24], which indicated a negative health effect is possible on the operators, and several actions must be taken to minimize the exposure and to alleviate the health risks. From the result, the daily vibration exposure limit value is mostly greater than 1.15 m/sec 2 for WBV in almost all the equipment which showed that there is a serious vibration threat on the operator; therefore, an urgent action is required to be taken to control the vibration exposure to a level below the ELV, as also suggested by [21].…”

“…e criteria that are used for the assessment and evaluation of this work are in consistence with the ISO standard [14], and the values of A rms on the technical operators were measured, as shown in Figures 7 and 8, where GD1 and GD2 represent the cigarette maker, TF1 and TF2 represent the Tray filler, FL1 and FL2 represent the fork lift, TB1 and TB2 represent the tobacco boiler, X3-X4 (1 and 2) is the Cigarette Packer model, T10 (1 and 2) represents the cigarette box compiler, and TP1 and TP2 represent the tobacco tipper, by comparative analysis as suggested by the ISO standard [14]; the exposure action value (EAV) [24] and exposure limit value (ELV) of 0.47 and 0.93 m/s 2 must not be exceeded for WBV, as indicated in Figure 7, while EAV of 2.5 m/s 2 and ELV of 5.0 m/s 2 must not be exceeded for HAV, as shown in Figure 8; however, it was seen that most of the values for both WBV and HAV obtained in both figures exceeded [24] stated standard limits.…”

The Distribution of Standard Deviations Applied to High Throughput Screening

“…The following data sets were obtained via PubCHEM 39,40 : AID 488862, AID 504592, AID 540333, AID 1053175, AID 1224905, AID 1053188, and AID 651654. These screens were selected for replicate measurements ( N ≥ 2), the size of the reported compound library, to represent a range of assay formats, and to illustrate a range of behaviours.…”

Machine‐learning scoring functions for structure‐based virtual screening

“…34,116,117 Also, descriptions of how such datasets can be assembled and curated for a given target using PubChem. 24,[118][119][120][121] In this section, we have reviewed the limitations of established SBVS benchmarks to anticipate prospective performance, the overwhelming evidence demonstrating that the latter does not mean that the remarkable accuracy of MLbased SFs is an artifact of the benchmark limitations and the alternatives that already exist for benchmarking SFs. In the next section, we will review the prospective validations of ML-based SFs presented in the last 5 years.…”