Introduction
In 2016, Microsoft launched an Artificial Intelligence (AI) chatbot, Tay, with high hopes of engaging in human-like conversations on Twitter. However, within 24 hours, it turned into a disaster. The model had been trained on highly racist input from some users, causing it to generate offensive and inappropriate messages. This incident serves as a reminder of the fundamental truth in AI: the quality of the data directly determines the reliability of the model’s output.
This lesson is just as critical in the biopharmaceutical industry, where the highly risk-prone and costly business of drug discovery has evolved in recent times with the widespread adoption of AI and machine learning (ML) models. From drug discovery to manufacturing and distribution, every step in the biopharmaceutical industry has the capacity to be made more efficient with the use of AI technology. Yet, despite the rapid advancements in AI across fields such as natural language processing (NLP) and computer vision, the level of impact within the biopharma industry has not met expectations. The fundamental bottleneck hindering progress through AI-driven approaches in this space is data quality. Unlike AI models trained on well-structured, vast, and standardized datasets (such as those powering ChatGPT or image recognition tools), biopharma data is often heterogeneous, incomplete, and full of inconsistencies.
In the case of ML models, the proverb ‘as you sow, so shall you reap’ fits perfectly. Feeding ML models and AI algorithms with high quality data ensures that the predictions related to drug discovery paradigms are accurate. Flawed, noisy, or biased datasets compromise model generalizability, leading to misclassifications, erroneous drug-target interactions, and inaccurate toxicity predictions. Key challenges include inconsistent annotations and batch effects that introduce noise, incomplete multi-omics datasets that obscure true biological correlations, and heterogeneous data formats that hinder data analysis and distort conclusions. Additionally, overrepresentation of specific patient populations introduces biases, reducing their applicability across diverse groups, while data silos and incompatibility between legacy and modern AI systems hinder model integration.
To fully understand how poor data quality derails AI-driven drug discovery, this blog will explore 1) the key ML models used across different stages of the drug discovery pipeline, 2) how specific data quality issues impact different steps in drug development, and 3) how Elucidata adopts strategies for mitigating poor data quality to enhance AI-driven insights. By the end, it will be evident that data quality is the foundation which dictates the success rate of AI-driven drug discovery.
AI and ML Models in Drug Discovery
Artificial intelligence (AI) and machine learning (ML) have optimized drug discovery pipelines by speeding up traditionally slow and expensive processes. AI models are now integral at various stages, from target identification and virtual screening to lead optimization and clinical trials, using advanced computational techniques such as deep learning, reinforcement learning, and generative models.
Target Identification
In the initial stage of drug discovery, target identification relies on AI models analyzing multi-omics datasets to uncover novel disease-associated genes and proteins. Resources like the Drug-Gene Interaction Database (DGIdb) and Connectivity Map (CMAp) integrate genomic, transcriptomic, and proteomic data to establish links between genetic variations and disease phenotypes, helping researchers prioritize potential drug targets. AI-driven tools further refine this process by employing NLP to extract insights from vast repositories of biomedical literature and patient data.
AI has also transformed protein structure prediction, which is a critical aspect of target identification. The advent of AlphaFold, an AI-driven deep learning system developed by DeepMind, has solved one of the most complex challenges in biology, which is accurately predicting protein folding and 3D structure from amino acid sequences. AlphaFold has accelerated the identification of druggable protein targets and facilitated structure-based drug design.
Virtual Screening
Following target identification, AI accelerates virtual screening, an essential step in scanning large chemical libraries to identify promising drug candidates. Traditional approaches, such as molecular docking simulations, have been augmented with ML models like random forests, support vector machines (SVMs), and deep learning algorithms, which predict molecular interactions with significantly higher accuracy. Deep learning-based methods, such as convolutional neural networks (CNNs), analyze molecular fingerprints to predict binding affinities, reducing the need for costly high-throughput screening experiments.
Lead Optimization
Once potential compounds are identified, AI enhances lead optimization by predicting pharmacokinetic and toxicity profiles. Quantitative structure-activity relationship (QSAR) models, widely used in drug design, rely on AI to predict chemical properties and optimize drug-like features. Open-source platforms like DeepChem provide tools for AI-driven molecular property prediction, guiding medicinal chemists toward safer and more effective compounds. Generative adversarial networks (GANs) further refine this process by designing novel molecular structures with optimized properties, reducing reliance on traditional trial-and-error synthesis.
Clinical Trials
AI is also reshaping biomarker discovery and clinical trials. AI further optimizes clinical trial design by predicting patient eligibility and treatment responses based on electronic health records (EHRs), enhancing trial success rates.
Despite these advances, there are very few AI-developed drugs in the market, which can be attributed to the many challenges associated with AI-driven drug discovery. Model interpretability remains a major hurdle, as deep learning models often function as “black boxes” with limited transparency into decision-making processes. For example, how AI models arrive at certain conclusions remains hidden from developers and biologists alike, which calls into question their validity and biological interpretability. Ethical concerns, such as biased training datasets and potential disparities in patient representation, raise concerns about AI-driven medical decisions. Importantly, AI’s reliance on high-quality, well-structured data underscores the need for standardized datasets and rigorous validation protocols, as poor-quality data can lead to erroneous conclusions and failed drug candidates.
The Ripple Effect of Poor Data Quality Across Drug Development Stages
The success of AI-driven drug discovery and development is inherently tied to the quality of data fueling these models. Errors, inconsistencies, and biases in datasets can create significant roadblocks at every phase of the drug development pipeline, leading to inefficiencies, inaccurate predictions, and ultimately, costly failures. Below, we examine how specific data quality issues disrupt different stages of drug development, from early discovery to manufacturing.
1. Target Identification: The Challenge of Incomplete and Noisy Datasets
The initial step in drug discovery of identifying molecular targets linked to disease relies on large-scale multi-omics datasets. However, missing data, inconsistent annotations, and batch effects in genomic and proteomic databases can obscure true biological correlations. Batch effects, which refer to variations in data arising due to differences in experimental conditions across laboratories and samples, are particularly dangerous since they can introduce patterns in data which are independent of the biological phenomena under study. This leads to erroneous associations between genes and diseases, misdirecting researchers toward ineffective targets. AI models trained on such flawed data can yield unreliable predictions, increasing the likelihood of pursuing non-viable therapeutic targets.
2. Hit Discovery and Lead Optimization: The Bias of Unbalanced Training Data
AI-driven virtual screening uses ML models to predict promising drug candidates, but these models often suffer from biased training datasets. If certain chemical scaffolds or biological targets are overrepresented in the training data, the model may favor them, overlooking structurally novel but potentially effective compounds. Similarly, incomplete datasets on drug-target interactions can lead to false positives or negatives, delaying the identification of viable leads and increasing the need for costly experimental validation.
3. Preclinical Testing: The Risk of Misclassified Toxicity Profiles
In preclinical research, AI models help predict the pharmacokinetics and toxicity of candidate molecules. However, inconsistencies in historical toxicity data stemming from variations in experimental conditions, errors in manual data entry, or lack of standardization across laboratories can skew these predictions. This results in either the premature rejection of safe compounds or the advancement of toxic ones, requiring additional rounds of testing and delaying progression to clinical trials. Additionally, advancing compounds at toxic dosages can result in regulatory violations, expensive lawsuits, and reputational damage.
4. Clinical Trials: The Impact of Patient Data Bias and Incompatibility
Clinical trial success hinges on AI models that analyze patient data to optimize trial design, predict adverse effects, and stratify patient populations. However, if training data primarily represent certain ethnic groups, genders, age ranges, or geographic locations, the model fails to be applicable across diverse populations. Moreover, discrepancies in EHR formats, reliance on doctors’ notes and incomplete patient histories create integration challenges, reducing the effectiveness of AI-driven patient selection and trial monitoring.
5. Manufacturing and Scale-Up: The Consequences of Process Data Variability
During drug production, ML models are increasingly used to monitor and control bioprocess parameters. However, biopharmaceutical manufacturing data is often heterogeneous, high-dimensional, and subject to missing values due to sensor failures or irregular sampling frequencies. Irregular sampling frequencies, is a common issue which results due to the imbalance between continuously monitored parameters, such as temperature and pH, and less frequent safety measurements taken weekly or monthly. This discrepancy can create the illusion of missing data, potentially distorting the model’s interpretations and predictions. Poor data integrity in this phase can lead to deviations in critical quality attributes (CQAs), batch failures, and inefficiencies in scale-up, ultimately affecting drug availability and cost.
6. Regulatory Approval: The Challenge of Non-Reproducible Insights
Regulatory agencies require robust, reproducible evidence for AI-assisted drug discovery. However, data integrity issues such as inconsistent labeling, lack of metadata, and the inability to trace AI-generated insights back to raw datasets can hinder regulatory approval. Without a transparent audit trail, AI-driven findings may not meet compliance standards, delaying market entry and diminishing confidence in AI applications in drug development.
Ensuring high-quality data is crucial for the success of AI-driven drug discovery. Implementing robust data quality strategies can mitigate the adverse effects of poor data and enhance the reliability of machine learning (ML) models in biopharma.
How Elucidata Ensures High-Quality Data for ML in Biopharma
Recognizing the challenges of poor quality data and their impact on AI models, Elucidata employs a comprehensive data quality framework to ensure that datasets powering AI models in biopharma are reliable, reproducible, and ML-ready.
Beyond FAIR: A Holistic Approach to Data Quality
While the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles[1] form a blueprint for effective data management systems, they are not sufficient for ML applications that demand higher precision. Elucidata goes beyond FAIR by addressing both intrinsic (inherent to the data) and extrinsic (arising from data handling and processing) quality issues.
Intrinsic Data Quality: Ensured at the source through rigorous experimental design, proper controls, and standardized measurement protocols.
Extrinsic Data Quality: Improved through systematic curation, metadata enrichment, and structured data processing.
Comprehensive Data Curation and Quality Assurance
Elucidata applies a multi-step quality assurance (QA) and quality control (QC) framework to every dataset, addressing key challenges in biomedical data:
- Data Cleaning and Structuring: Unifying data formats, resolving inconsistencies, and standardizing metadata annotations to ensure seamless integration across studies.
- Automated and Manual QA/QC: Over 50 checkpoints assess data schema compliance, field-specific validations, and logical consistency. This is done to reduce inconsistencies that can confuse ML models.
- Metadata Standardization and Annotation: Aligning datasets with controlled vocabularies and ontologies to enhance interoperability and contextual understanding. This results in accurately labeled data, allowing ML models to internalize data seamlessly, enabling effective insight generation.
- Measurement Reprocessing: Identifying and correcting artifacts, normalizing batch effects, and detecting mislabeled or outlier samples.
Continuous Monitoring and Iterative Improvement
Elucidata’s approach to data quality is an ongoing and dynamic process. We employ:
- Automated Validation Pipelines: Continuous assessment of datasets to flag missing values, incorrect labels, and format inconsistencies.
- Iterative Data Refinement: A feedback loop that analyzes recurring quality issues, prioritizes fixes based on user impact, and implements corrective strategies at the source.
Future-Proofing Biopharma Data for AI
By integrating standardized processing pipelines with real-time data validation, Elucidata ensures that biopharma companies can confidently leverage ML for biomarker discovery, drug repurposing, and predictive modeling. High-quality, well-annotated datasets improve model accuracy, enhance reproducibility, regulatory compliance, and overall scientific impact.
Through this meticulous data quality strategy, Elucidata empowers AI-driven drug discovery with rigorously validated, ML-ready datasets and help pharma companies turn raw biomedical data into actionable insights.
Conclusion
The success of AI-driven drug discovery depends on the quality of the data it learns from. Poor-quality data leads to flawed predictions, wasted resources, and failed drug candidates. Don’t let bad data compromise your ML models. Work with Elucidata to ensure your AI-driven drug discovery efforts are backed by clean, harmonized, and high-quality datasets. Contact us today to learn how our platform can enhance your R&D efforts.
Author: elucidata .io
Website: https://www.elucidata.io/ Website: https://www.elucidata.io/ Elucidata leverages its platform, Polly to augment the quality of data in pre-clinical drug discovery. It curates multi-omics and assay data to make them ML-ready or analysis-ready. Our exceptional multi-disciplinary team of experts use Polly’s powerful curation engine to harmonize a diverse array of data-types, curate metadata and process data consistently at affordable costs while maintaining information-richness. We are one of the only companies to offer a tech-enabled approach to multi-modal data curation that serves the life science industry. Polly’s technology and experts have helped R&D teams arrive at multiple validated drug targets across immunology, oncology, and metabolomic disorders. Currently, 25+ research organizations, including 4 of the largest 10 pharma companies are using Polly and its allied solutions to accelerate their discovery programs. Many other data-driven healthcare companies also use Polly to process, harmonize and store public or in-house biomedical data. Address: 114 Sansome Street, Suite 250 San Francisco, CA 94104 Phone No: 9716140329 Contact Email: info@elucidata.io
