High-Throughput Screening: Principles, Applications and Advancements

HTS involves the use of automated, miniaturized assays and supporting data analysis to rapidly identify novel compounds.

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Published: September 25, 2025

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David Elder, PhD

Close-up of pipette tips dispensing green liquid into a microplate for high-throughput screening.

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High-throughput screening (HTS) is a rapid assessment approach typically used in drug discovery¹ but also more broadly in toxicology,² genomic and functional screening³ and biologics and antibody discovery.⁴ HTS offers advantages over rational drug design (i.e., via an in-depth understanding of a specific biological target), or structure-based approaches in drug discovery,⁵ by enabling more rapid delivery of diverse drug leads compared to these other approaches.

What is high-throughput screening?

Key aspects of high-throughput screening

- Sample and library preparation

- Assay development and validation

- Automation and robotics

- Detection technologies

- Data management and analysis

Ultra-high-throughput screening (uHTS)

High-throughput screening applications

- Drug discovery and development

- Toxicology

- Genomic and functional screening

- Biologics and antibody discovery

Future trends and innovations in high-throughput screening

What is high-throughput screening?

HTS involves the use of robotic, automated, miniaturized assays and supporting data analysis of libraries of structurally diverse compounds, to rapidly identify novel lead compounds for new or established diseases.

The benefits of HTS include:

The fast identification of potential hits (10,000–100,000 per day)
Reduction of timelines

The disadvantages include:

Cost
Technical complexity
The potential for false positives and negative hits

HTS approaches in the discovery of small molecules can result in:

Inflated physicochemical properties within these libraries, such as high lipophilicity and molecular weight
Poor aqueous solubility
Lowered clinical exposure in humans
High attrition rates in clinical development.⁶

HTS can also be used to support “fast to failure” strategies, for example, to reject unsuitable candidates as quickly as possible.⁷

Key aspects of high-throughput screening

Sample and library preparation

HTS is reliant on the efficient preparation of combinatorial libraries to test large numbers of compounds against a specified biological target, or multiple targets. The samples need to be prepared in a standardized, automation-friendly manner, typically using microplates. There is a need for larger chemical variability via diverse scaffolds (core chemical structures).⁸ Split and mix combinatorial libraries involve preparing novel scaffolds on solid supports, then repeatedly splitting these compounds, and reacting them with different “building blocks” and then combining the output back into the main library (see Figure 1) ⁹^,¹⁰^,¹¹ However, the need for enhanced quality of the resultant libraries, with respect to enhanced clinical exposure and clinical safety, has also increased.⁶^,⁷

A diagram showing the process of creating a mix-split library.

Figure 1. A typical example of mix-split libraries. Credit: Technology Networks.

Assay development and validation

HTS assays need to be robust, reproducible and sensitive.¹²^,¹³ Assays utilized in HTS also need to be validated for their biological and pharmacological relevance.

These methods should be appropriate for miniaturization to reduce reagent consumption and suitable for automation.¹² HTS assays typically run in 96-, 384- and 1536-well formats, utilizing automated liquid handling and signal detection systems. They require full process validation according to pre-defined statistical concepts. In addition, methods transferred between different laboratories/facilities also require appropriate validation.¹²

Automation and robotics

Automated liquid-handling robots are used for HTS of large combinatorial libraries. These robots are capable of low-volume dispensing of nanoliter aliquots of sample, thereby minimizing assay setup times and providing accurate and reproducible liquid dispensing.¹⁴

Unfortunately, the output from combinatorial chemistry screens is often incompatible with the input for any subsequent biology tests.¹⁵ Compound management was introduced in the early 2000s to address this issue. It is a highly automated procedure, involving compound storage on miniaturized microwell plates together with compound retrieval, nanoliter liquid dispensing, sample solubilization, transfer and quality control (QC).¹⁶

Detection technologies

HTS assays can be generally subdivided into biochemical or cell-based methods. Biochemical targets typically utilize enzymes,¹⁷ e.g., histone deacetylase (HDAC).⁵ HTS methods for novel HDAC inhibitors involve the use of a peptide substrate coupled to a suitable leaving group that would allow the quantification of the HTS substrate that is activated by the HDAC enzyme.¹⁸

Many different methods can be utilized to measure this change in enzymatic activity, including the use of binding-based, e.g., fluorescence, luminescence nuclear magnetic resonance spectroscopy, mass spectrometry (MS) and differential scanning fluorimetry (DSF) approaches. DSF monitors the changes in fluorescence as a function of melting temperatures (Tm) of the enzyme. The binding of a ligand to an enzyme result in the Tm increasing (Figure 2).¹⁹ The fluorescence-based methods⁵ are the most common due to their sensitivity, responsiveness, ease of use and adaptability to HTS formats.¹⁸ MS-based methods of unlabeled biomolecules are becoming more generally utilized in HTS, permitting the screening of compounds in both biochemical and cellular settings.²⁰

Changes in melting temperature observed using differential scanning fluorimetry.

Figure 2. Changes in melting temperature observed using DSF. Different transitions that appear during heating can be caused by different physical interactions, e.g., aggregation will increase the Tm, or ligands that stabilize a portion of the protein sample (orange); the latter shows (in orange) one Tm similar to the unchanged protein, together with, one (or more) Tm at a higher temperature during the denaturation process. Credit: Technology Networks.

Data management and analysis

One of the fundamental issues with HTS is the generation of false positive data.²¹ The reasons are complex, but can include assay interference arising from chemical reactivity,²³ metal impurities,²⁴ assay technology,²⁵^,²⁶^,²⁷measurement uncertainty,²⁷ autofluorescence²⁸ and colloidal aggregation.²⁹ This has resulted in the use of several in silico approaches for HTS false positive data detection.²³^,²⁶^,³¹^,³²These methods are generally based on expert rule-based approaches, for example, pan-assay interferent substructure filters²¹^,²²or machine learning (ML) models trained on historical HTS data.²⁸^,²⁹^,³⁰Statistical QC methods for outlier detection can address HTS variability (both random and systematic).³³ HTS triage involves the ranking of HTS output into several different categories: compounds with limited, intermediate or high probability of success.³⁴^,³⁵

Ultra-high-throughput screening (uHTS)

Whereas HTS can achieve throughputs of up to 100,000 compounds a day, uHTS can potentially achieve much higher throughputs (millions of compounds a day). This has necessitated significant advances in microfluidics and high-density microwell plates with typical volumes of 1–2 µL. Indeed, fluid handling considerations have been a significant impediment to the more widespread uptake of this technology.³⁶ Swingle et al. recently established a miniaturized (1536-well plate format), nearly homogeneous, fluorescence intensity enzymatic assays to detect protein phosphatase (PP1C and PP5C) inhibitors.³⁷ The assays were used in a uHTS campaign, testing >315,000 small molecule compounds a day.

One of the most significant challenges faced in speeding up existing HTS methodology is the ability to directly monitor each microwell in the array (see Table 1). Another factor limiting uHTS uptake is that HTS biosensors are often restricted to one analyte, which can constrain the ability to perform multiplex measurements in parallel.³⁸ The advent of miniaturized, multiplexed sensor systems that allow continuous monitoring has addressed this problem. The development and validation of these multiplexed sensor systems for continuous measurement of pH and oxygen concentration in cell-based assays is progressing.³⁸

Table 1. A comparison of HTS and uHTS capabilities.³⁹

Attribute	HTS	uHTS	Comments
Complexity	-	--	Significantly greater for uHTS
Costs ($)	-	--	Significantly greater for uHTS
Speed (assays/day)	< 100,000 +	>300,000 +++	HTS is significantly quicker
Data quality requirements	+	+	A similar internal standard approach to reducing false positives applies to both formats. Cheminformatics is often required, e.g., LIMS or similar
Data analysis requirements	-	--	Need for slightly faster data processing with uHTS. AI may be required to process large datasets
Analytical method – speed and sensitivity	+	+	Fluorescence, luminescence, enzymatic and scintillation proximity assays can be used with both formats
False positive and false negative bias	+	+	No obvious enhancements arising from uHTS
Ability to monitor multiple analytes	-	+	This necessitates the use of miniaturized, multiplexed sensor systems
Ability to monitor individual microwell environment	-	+	This necessitates the use of miniaturized, multiplexed sensor systems

High-throughput screening applications

Drug discovery and development

HTS is one of the main approaches utilized to identify starting compounds for small molecule drug design programs. In general, HTS is the preferred approach if little is known about the pharmacological target, which precludes structure-based drug design. HTS can also be used in parallel to other strategies like computational techniques, e.g., quantitative structure activity relationships.⁴⁰

In chemical development, HTS helps to quickly establish the most favorable synthetic route together with the best conditions for each separate process step that will optimize yields and quality. The incorporation of AI/ML and automation/robotics can iteratively enhance screening efficiency.⁴¹

Toxicology

Historically, in vivo animal studies have been used to predict chemical toxicity. These studies are expensive, low throughput, ethically questionable and are often poor surrogates for toxicity in humans.⁴² Cell-based HTS methodologies are typically utilized as they are more predictive of general or target organ toxicity, and comparative assessments in different cell lines can be performed.⁴³

There are multiple data-generation and data-sharing programs available for processing HTS data, facilitating toxicity studies, such as the “rpubchem” R package in PubChem and “EMBL-EBI” KNIME package in ChEMBL.⁴⁴ Several approaches to access and incorporate multiple bioassay data outputs in order to better profile toxic molecules have been reported. For example, a study assessed over 12,000 bioassays and around half a million data points for 2000 compounds with animal toxicity. The authors highlighted an innovative methodology for selecting the most relevant bioassays that can be correlated with acute rat toxicity.⁴⁵

The Tox21 program has been developed by a consortium of public health agencies to identify toxicity issues of novel molecules in a high-throughput, concentration-responsive manner using a combination of in vitro assays.⁴²

Genomic and functional screening

HTS is used in functional genomics to identify the biological rationale of specific genes and (meta)genes. This involves the rapid analysis of large numbers of genes to identify those that have effects on specific diseases or biological pathways.⁴⁶ As of 2022, 3.1 million human sequencing data and 1.7 million mouse sequencing data were deposited in the National Center for Biotechnology Information Sequence Read Archive.⁴⁷ Several approaches have been commonly utilized for high-throughput genomic screening:

RNA sequencing is utilized to sequence complementary DNA to assess the sample’s RNA content.
Chromatin immuno precipitation sequencing with DNA-encoded libraries is utilized to identify protein-binding sites on DNA requiring AL/ML data manipulation.⁴⁸

HTS is used in conjunction with different types of microarrays. Firstly, expression-profiling measures gene expression of thousands of genes in parallel, using oligonucleotide probes, usually ≤50-mer (or base pairs) in length.⁴⁹ Secondly, tiling microarrays⁵⁰ are used for finding the positions of epigenetic markers, e.g., histone modifications, or mapping transcription factor binding sites. They utilize interconnecting oligonucleotide probes (typically ≤50 bp) covering several megabases of the genomic sequence.⁵⁰

Biologics and antibody discovery

The early discovery phase for a biologic/antibody molecule centers on the identification of the optimal sequence and scaffold for the drug.⁵¹ These factors typically include phage display libraries and bespoke hybridoma-derived libraries of antibodies, created for specific targets.⁵¹

Phage display libraries involve substantial collections of variable region antibody fragments (VRAF) presented on the surface of the bacteriophage. Those VRAFs with high binding affinity for the target can be detected and isolated via iterative in vitro selection processes versus the specified antigen, DNA encoding these fragments can then be determined. The extremely large size of these phage libraries permits the HTS of large numbers of diverse molecules. This is typically greater than even the largest small molecule libraries.

Alternatively, individual hybridoma cells that can secrete functional antibodies can be selected. This process can be automated, allowing repetitive HTS of thousands of hybridoma samples.⁵² The biological functionality of antibodies, can often be compromised by aggregation, and this is often only seen after scale-up of the molecule. HTS screening of antibody fragments, to test for susceptibility to aggregation, can now be performed much earlier in the drug discovery process, saving time and money.⁵³

Future trends and innovations in high-throughput screening

To process large-scale sequencing data, new bioinformatics methodologies are required. Currently, AI/ML is enhancing various areas of HTS. AI/ML programs can reduce the rates of false readouts, facilitating greater reliability and decreasing the time required for addressing false positives.⁵⁴ However, AI/ML screening of DNA-encoded libraries may introduce widespread false negatives, i.e., an inability to recognize valid hits.⁵⁵ This may require fundamental changes to AI/ML data screening in the future.

With the advent of AI/ML platforms, virtual screening may be able to provide workable starting points for lead optimization.⁵⁶ This paradigm shift is largely defined by the abundant knowledge of various ligand properties, together with their binding to therapeutic targets and 3D structures of these ligands. In parallel, there is plentiful computing capacity and on-demand virtual libraries of billions of drug-like small molecules available. Using this virtual approach requires fast computational methodologies for successful ligand screening. This incorporates structure-based computer-based HTS of huge chemical spaces, further enabled by fast iterative screening approaches. Additionally, there are significant developments in AI-based deep learning predictions of ligand properties and target activities, without knowing the actual receptor structure.⁵⁷

HTS toxicological approaches have the potential to significantly reduce animal testing, aligning with the 3R strategies (Replace, Reduce, Refine).⁴²^-⁴⁵ Indeed, the US National Research Council issued a “vision for toxicity testing in the 21st century” that stressed the importance of HTS approaches and in silico models to ultimately replace animal testing.⁵⁸

HTS and green chemistry can be used in tandem to develop safer and more environmentally sustainable products and supporting processes. The former can be used to rapidly screen new chemicals from a safety perspective, whilst the latter focuses on designing processes that minimize waste and hazardous components or by-products. Over 4,700 chemicals have been screened as part of the ToxCast initiative.⁵⁹ Similarly over 8,900 chemicals have been screened by the Tox21 initiative.⁶⁰ Green chemistry initiatives have been used to reduce solvent consumption, replace toxic and environmentally damaging chemicals with greener alternatives and introduce enzymatic transformations to replace palladium-catalyzed synthesis.

Advances in miniaturization tools will help improve HTS performance. Reduction in assay volumes together with microfluidic and nano-dispensing platforms reduces solvent consumption (green chemistry) whilst maintaining higher throughput. This is important in the screening of complex biological samples and fragment-based drug discovery, as it enables larger libraries without a commensurate increase in costs.²⁰

The addition of multiomics approaches into HTS, together with high-resolution mass spectrometry, will facilitate the discovery of better quality small molecule candidates. Multifaceted diseases such as cancer often involve multiple, interconnected biological systems. Consequently, multiomics approaches, rather than studying isolated targets, have a greater chance of finding new drug candidates.²⁰^,⁴⁶^-⁵⁰

Recent trends in HTS include the use of affinity selection mass spectrometry (ASMS), self-assembled monolayer desorption ionization (SAMDI), target protein degradation (TPD) and clustered regularly interspaced short palindromic repeats (CRISPR).²⁰ASMS can rapidly generate high-quality data and can be used across a broad spectrum of targets, such as oligonucleotides, proteins and complexes. SAMDI, coupled with ASMS, can rapidly assess protein-protein interactions and TPD. Whereas, CRISPR has been utilized to elucidate many aberrant biological pathways implicated in disease processes.²⁰

As HTS has progressed, it has moved beyond simply generating large libraries of ever-increasing numbers of compounds. The quality of those libraries is now paramount. Currently, compounds with reduced lipophilic attributes and higher three-dimensional properties are required in drug discovery. HTS libraries have evolved to meet these changing needs. It is a similar message in other areas where HTS is utilized – the quality of the supporting libraries is key.

In parallel, the usage of miniaturized automated assays and robotics will continue to grow. AI/ML will play a key role in profiling the vast data sets that modern HTS generate, facilitating triage of false positive data. Recent publications are suggesting that ML-based screening of DNA-encoded libraries may result in widespread false negatives, which may require a fundamental rethink of these approaches.⁵⁶ Lastly, in the omics era, multiple targets are likely to be the norm as this better reflects the complexity of many disease states.

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What is high-throughput screening?
High-throughput screening involves the use of robotic, automated, miniaturized assays and supporting data analysis of libraries of structurally diverse compounds, to rapidly identify novel lead compounds for new or established diseases.

What are the pros and cons of high-throughput screening?
The benefits of high-throughput screening include the fast identification of potential hits (10,000–100,000 per day) and, as a result, a reduction of discovery timelines. The disadvantages include cost, technical complexity and the potential for false positives and negative hits.

What is the difference between high-throughput screening and ultra-high-throughput screening?
Whereas high-throughput screening can achieve throughputs of up to 100,000 compounds a day, ultra-high-throughput screening can potentially achieve much higher throughputs (millions of compounds a day). This has necessitated significant advances in microfluidics and high-density microwell plates with typical volumes of 1–2 µL. Indeed, fluid handling considerations have been a significant impediment to the more widespread uptake of this technology.

How can artificial intelligence be used in high-throughput screening?
Currently, artificial intelligence/machine learning (AI/ML) is enhancing various areas of high-throughput screening. AI/ML programs can reduce the rates of false readouts, facilitating greater reliability and decreasing the time required for addressing false positives. However, AI/ML screening of DNA-encoded libraries may introduce widespread false negatives, i.e., an inability to recognize valid hits. This may require fundamental changes to AI/ML data screening in the future.

Meet the Author

David Elder, PhD

Dr. David Elder has worked in the pharmaceutical industry for over 48 years. He worked for 4 different large pharmaceutical companies over a 40-year period, and his last 23 years were spent with GSK in several different roles of increasing seniority. Since 2016, he has worked as an independent CMC consultant.

Biopharma

Biopharma

High-Throughput Screening: Principles, Applications and Advancements

HTS involves the use of automated, miniaturized assays and supporting data analysis to rapidly identify novel compounds.

What is high-throughput screening?