Multimodal data in drug discovery: Uses, benefits and more

Multimodal data refer to the integration and analysis of datasets from multiple sources and formats, such as text, audio, images, genomic data and other modalities, to create richer datasets. In the context of drug discovery, multimodal data is useful for combining information like genomic data, clinical data and chemical structures to provide a more holistic view of diseases and drug interactions, which can help researchers develop more effective treatments. 

Multimodal data in drug discovery help teams identify and validate targets, improve drug design and optimization, streamline clinical trials and drive effective personalized medicine. But multimodal data comes with many challenges, such as complexity of integrating data, high compute demands and regulatory concerns over data privacy.

Software like TileDB can help teams analyze multimodal data by efficiently handling complex datasets such as genomics, spatial transcriptomics, single-cell data and more. By modeling these data as multi-dimensional arrays, TileDB improves target discovery by making it easier for research teams to collaborate and reducing storage and compute costs. 

This blog offers a detailed walkthrough of the immense impact of multimodal data and how you can make best use of it. Let’s begin with the value multimodal data brings to drug discovery.

How are multimodal data used in drug discovery?

Multimodal data refer to datasets integrated from multiple sources or measurement modalities, such as genomic sequencing, single-cell, imaging, clinical phenotypes, and proteomic or metabolomic profiles. By combining these data types, researchers can gain more comprehensive perspectives on biological systems. This is crucial to better understand disease mechanisms and therapeutic responses.

In the context of drug discovery, multimodal data are valuable because they enable a systems-level view of disease biology. By integrating complex modalities, life sciences researchers can achieve more accurate biomarker identification and patient stratification as well as create better models to predict drug efficacy or toxicity. The result is more effective clinical trials and treatments for all kinds of diseases through multimodal data.

Some effective applications of multimodal data in drug discovery include:

• Identify and validate targets Because multimodal datasets combine data from diverse sources, they can help discover and validate novel therapeutic targets. For example, the Open Targets platform integrates genomics, transcriptomics, animal models and other sources to score and rank disease-gene associations. This helps prioritize drug targets based on biological plausibility and clinical relevance. In addition, using multiple data streams in multimodal approaches helps reduce false positives when selecting targets for development.

• Understand mechanisms-of-action (MoA) better Understanding how a compound affects biological systems (also known as a drug’s mechanism-of-action) often requires integrating multimodal data like transcriptomic changes, protein-protein interactions and phenotypic assays. A 2022 study from the Broad Institute used multimodal analysis from imaging, gene expression and small-molecule screening data to map compound MoAs “across more than 28,000 chemical and genetic perturbations.” Multimodal integration helped these researchers identify previously unrecognized MoAs to guide their compound optimization.

• Stratify patients and predict drug response By linking molecular data with clinical outcomes, multimodal datasets can help identify patient populations with target biomarkers to predict drug response. Put simply, this finds the right patients for the right medicines. One example from oncology was breast cancer researchers who used multimodal methods to combine genomic, imaging, and other data to tailor neoadjuvant therapies targeting breast cancer subtypes. This approach aimed to encourage future research that would offer more precise treatment options for breast cancer patients.

What data types are used in drug discovery?

There are a wide variety of data modalities that life sciences researchers rely on to inform drug discovery. Some common data types in multimodal analysis include:

• Omics data (genomics, transcriptomics, proteomics and metabolomics) These large-scale datasets are generated by analyzing complete sets of DNA, RNA, proteins and metabolites inside a biological system. By exploring these molecular profiles, researchers can gain high-resolution insights into specific biological pathways to support target discovery, mechanism analysis and biomarker identification.

• Phenotypic and imaging data These digital representations of anatomical information are created by medical imaging technology like MRI scanners. High-content screening, cellular morphology and histopathological imaging can show researchers visual evidence of drug effects at the cellular or tissue level.

• Clinical and real-world data (RWD) This data gathered from hospitals and clinics can include electronic health records (EHR), patient registries, insurance claims data and more. Analyzing this data can provide critical context on drug safety, efficacy and population-level outcomes, especially during translational research and post-marketing surveillance.

What are the benefits of using multimodal data in drug discovery?

Multimodal data offer many advantages in drug discovery, including enhanced target identification and validation, improved drug design and optimization, streamlined clinical trials and more effective personalized medicine. Here is a glance at the key benefits of multimodal data in drug development processes:

Let’s unpack how multimodal data analysis delivers these benefits in drug discovery:

• Enhanced target identification and validation Multimodal data equip researchers with a layered view of disease biology by integrating genetic, transcriptomic, proteomic and phenotypic information. This identifies relevant targets with greater confidence while reducing the risk of pursuing false positives that can slow down development. In addition, validating across multiple modalities helps researchers prioritize targets and better annotate genetic functions.

• Improved drug design and optimization More detailed data analysis enables more effective medicine. Multimodal data lets researchers combine structural biology data with screening results and downstream phenotypic effects to better refine drugs for potency, selectivity and safety. Multimodal integration of datasets like compound structures, gene expression profiles and imaging phenotypes helps model structure–activity relationships (SAR) more precisely. This shortens design cycles and helps discover viable leads.

• Streamlined clinical trials Finding the right patient populations is key to effective clinical trials. Multimodal data informs trial design by identifying predictive biomarkers, optimizing inclusion criteria and stratifying patient subgroups in real time. By integrating omics data with clinical endpoints and digital health metrics, researchers can enhance early efficacy signals and detect adverse effects sooner. This accelerates trial decisions, reduces costs and improves overall study efficiency.

• More effective personalized medicine Multimodal data make it easier to create therapies for individual patients. By integrating patient-specific data from genomics, imaging, clinical history and real-world outcomes, multimodal approaches help develop tailored treatments designed for an individual's molecular and clinical profile. This leads to more accurate responder identification and improved therapeutic outcomes. On the population level, multimodal datasets also help identify subpopulations that may benefit from repurposed or combination therapies.

What are the challenges of using multimodal data in drug discovery?

While multimodal data has many advantages for drug discovery, it is not without its complexities. Some challenges pharma and biotech companies face when using multimodal data include:

• Complexity in data integration and interoperability Multimodal data is by definition derived from all kinds of data types and sources, ranging from genomic sequencing and imaging to EHRs and population data. Each of these data modalities has its own formats, ontologies and standards that do not fit easily into a shared tabular dataset. This makes harmonizing all this data for multimodal analysis a complex and technically demanding challenge. Researchers often spend significant effort creating sophisticated pipelines to normalize, align and manage metadata for multimodal datasets. If data quality is inconsistent or missing values, this makes the complexity harder to navigate—limiting the scalability and reproducibility of multimodal research.

• High compute demand to analyze multimodal datasets Datasets from sources like high-resolution imaging, omics and single-cell data are typically quite large; combining such large datasets together for multimodal analysis creates massive, high-volume datasets that require huge amounts of computing power to process. Even if research teams have the engineering expertise and cloud computing resources to handle all this demand, the cost of multimodal analysis can dominate research IT budgets. This makes it important for researchers to find scalable solutions for multimodal analysis so computational bottlenecks do not delay research insights.

• Regulatory concerns over data privacy Using patient data for research always requires navigating regulations around privacy and data security, with statutes like GDPR, HIPAA and SOC 2 Type 2 governing how patient data can be collected, shared and processed. Because multimodal analysis integrates datasets from different sources, it’s important to comply with the data security rules for each modality and source. This becomes harder as multimodal datasets become more complex, which hinders secure, cross-institutional collaboration when datasets are linked or re-identified for longitudinal analysis. This challenge demands robust governance and de-identification protocols inside trusted research environments.

As life sciences firms look for ways to take full advantage of the potential of multimodal data while navigating its challenges, the right database technology is crucial. The next section will examine how such technology can help analyze multimodal data for drug discovery.

How can you analyze multimodal data in drug discovery?

To analyze multimodal data in drug discovery, researchers need a unified analytical framework to uncover patterns or relationships that would remain hidden within a single data type. Creating this framework is usually a multistep process that includes data preprocessing, feature extraction, dimensionality reduction, alignment or co-registration of modalities and downstream modeling using machine learning or statistical inference methods. 

Database technology and tools are a vital part of this. Popular open-source frameworks include Scanpy and Seurat for single-cell multimodal analysis as well as TensorFlow and PyTorch for developing deep learning models. For integration across omics layers, tools like MOFA+ (Multi-Omics Factor Analysis) and MultiAssayExperiment in R are widely used. In imaging and spatial transcriptomics, libraries such as Napari, Squidpy, and DeepCell enable joint analysis of visual and molecular data. Database software like Databricks and Snowflake can offer research teams benefits like simplified collaboration and easier management for multimodal data analysis. Here is a guide with nine key criteria for choosing a multimodal data platform.

TileDB designed its database platform to address the unique challenges of multimodal data management. Using multi-dimensional arrays, TileDB simplifies data management by consolidating multimodal data types like omics, single-cell and imaging in a unified data architecture. This reduces integration complexity while providing a scalable cloud-native foundation. 

In addition, TileDB’s arrays are powered by a serverless engine that scales its performance to meet the demand of complex multimodal use cases. To address data governance and privacy concerns, TileDB makes it easy for life sciences firms to adopt FAIR data principles and use federated queries to enable remote access to sensitive data for designated users without moving the data from its proper place. The result is a database designed for discovery, offering simplified and comprehensive cataloging and analytics for research teams inside and outside life sciences organizations.

To learn more about how TileDB can unlock the potential of your multimodal data, contact us.