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Biotechnology companies are increasingly leveraging Artificial Intelligence (AI) to accelerate drug discovery, optimize clinical trials, and navigate complex regulatory landscapes. As AI continues to demonstrate its value, investment in AI-driven biotech has surged, signaling strong confidence in its transformative potential.
One of AI’s biggest advantages is its ability to address resource constraints and high failure rates—challenges that are especially amplified for small and emerging biotech companies with limited budgets and tight timelines. Traditional drug development approaches are becoming unsustainable due to soaring costs and inefficiencies. AI is changing that by streamlining clinical trial design, enhancing predictive modeling, and improving R&D success rates.
This growing impact has made AI in biotech one of the most attractive areas for healthcare investment. In 2024, AI-focused healthcare companies secured a significant share of venture capital funding, with total healthcare VC investment rising to $23 billion from $20 billion in 2023. Nearly 30% of that funding went to AI-focused startups, with biotech AI alone attracting $5.6 billion.
Looking ahead, AI in drug discovery is expected to play an even larger role in shaping the next generation of therapeutics. More drug candidates will be identified through AI-powered screening, offering a promising solution to the industry’s 86% failure rate for small-molecule clinical trials. The AI-driven biotechnology market, valued at $4.7 billion in 2024, is projected to reach $27.43 billion by 2034 , growing at a 19.29% CAGR.
At this pace, AI is no longer just a competitive advantage - it’s becoming essential to the future of biotechnology. In this whitepaper, we’ll explore how leading organizations are applying AI across the value chain and the impact it’s having on biotech R&D success.
AI in Drug and Molecule Discovery
The process of discovering new drugs has traditionally been long, expensive, and highly uncertain - often taking years and billions of dollars. Today, AI in drug discovery is transforming this process by analyzing molecular structures, predicting new designs, and rapidly identifying viable drug candidates. By integrating vast scientific knowledge and enhancing molecular simulations, AI is enabling researchers to move from concept to breakthrough much faster.
How AI is making a difference
Turning decades of research into real-time insights
AI tools can <a href='https://www.indegene.com/what-we-think/case-studies/drug-cost-effectiveness-analysis' style={{color: '#034ea2'}} target='_self'> ingest and analyze unstructured data </a> from millions of journal articles, patents, and molecular databases. Instead of manually searching through years of research, scientists can now quickly determine whether a molecule has been studied before, uncover potential side effects, and explore related targets. This level of knowledge integration accelerates decision-making and uncovers insights that might otherwise be missed.
Faster, more accurate simulations
Traditional drug discovery relies on physics-based simulations, which are computationally expensive and time-consuming. AI-powered “surrogate” simulations, on the other hand, use deep learning models to predict molecular interactions at a fraction of the time and cost. This allows researchers to run significantly more simulations within the same computational budget - rapidly testing and refining hypotheses for potential drug candidates.
AI-Driven Molecular Design and Development
Using generative AI, researchers can work backward from a set of desired properties to design novel molecules that meet specific therapeutic needs. This approach, known as AI-driven molecular lead generation and retrosynthesis planning, acts as a form of reverse engineering - asking AI, “How do we create a molecule that meets these constraints?”
Proven success in antimicrobial research
A standout example AI in drug discovery comes from researchers at the University of Oxford, who have demonstrated the power of generative AI in rapidly identifying antimicrobial candidates.
Two of these molecules proved to be highly potent and exhibited low toxicity in mice. This represents a 10% success rate - significantly higher than the industry standard, which often falls below 1%. The accelerated 48-day timeline also marks a major improvement over traditional drug discovery processes, which typically take much longer.
By embedding AI in drug development workflows, biotech companies are reducing wasted resources, improving precision in candidate selection, and accelerating the delivery of life-saving treatments to patients.
AI in Protein Binding Hot Spot Prediction
Understanding how drugs interact with proteins is crucial for developing effective treatments. AI-driven models like AlphaFold from DeepMind are revolutionizing this process by accurately predicting protein structures - including ligand binding sites, where drugs attach to proteins and trigger cellular responses.
Even with limited prior data, these models enable researchers to design new drug candidates more efficiently, making them a powerful tool in biotech R&D.
How AI is improving drug targeting
Expanding the range of drug targets
Many proteins have remained unexplored because their structures were too complex to map. AI can now predict protein shapes and binding sites, even without prior experimental data, opening up new possibilities for drug discovery.
Simulating drug interactions with precision
Much like weather forecasting or stock market predictions, AlphaFold and similar AI models leverage vast protein databases to simulate how different molecular compounds fit into binding sites. This allows researchers to better understand protein-ligand interactions and prioritize the most promising drug candidates before conducting costly lab experiments.
Validating AI predictions in drug discovery
To ensure AI-generated predictions are reliable for real-world applications, researchers use two key validation techniques:
Retrospective studies
AI models are tested with known compounds to confirm that predictions align with experimental lab results.
Prospective studies
Researchers input completely new compounds into AI models to assess how well predictions match real-world interactions, helping to refine drug discovery models.
Beyond AlphaFold, machine learning and deep learning techniques continue to advance protein-ligand interaction predictions.
By integrating AI in drug development workflows, biotech researchers are reducing the time, cost, and uncertainty of traditional methods—delivering smarter, faster breakthroughs in biotech R&D.
AI in Drug Development
AI in drug development is transforming the way treatments are created—making the process faster, more precise, and cost-effective. Traditional approaches have long been hindered by slow, manual workflows that demand extensive time and resources. Now, AI is changing the game by automating data analysis, optimizing drug design, and improving decision-making at every stage. These advances are driving the next wave of biotech innovations and setting new standards for efficiency and impact across biotech R&D.
How AI is being leveraged across the entire drug development lifecycle
Drug discovery and target identification
AI rapidly scans vast biological datasets to uncover viable drug targets and prioritize the most promising candidates for further research
Lead optimization and drug design
AI-driven molecular modeling refines and enhances drug candidates, reducing costly failures in later stages
Preclinical testing and toxicity prediction
AI models analyze biological data to predict toxicity and efficacy, reducing animal testing and improving early drug safety.
Clinical trial design and optimization
AI refines trial protocols, enhances patient recruitment strategies, and identifies potential risks early to improve trial success rates
Biomanufacturing and process optimization
AI streamlines manufacturing workflows, ensuring consistency, reducing waste, and improving scalability
Regulatory and compliance automation
AI simplifies regulatory submissions by structuring and organizing clinical data, expediting approval timelines
Personalized and precision medicine
AI analyzes patient-specific data to develop tailored therapies, ensuring the right treatment reaches the right individual
By leveraging AI’s predictive power and automation capabilities, biotech firms are not just accelerating drug development - they’re increasing precision, improving scalability, and significantly raising the success rates of new treatments.
AI in Study Protocol Development
Every clinical trial begins with a protocol - a detailed document outlining how the study will be conducted to ensure patient safety and data integrity. However, protocol development is a complex process, and even minor amendments can introduce significant delays and costs.
Study protocols typically require amendments due to adjustments in inclusion and exclusion criteria to refine patient selection, regulatory updates necessitating compliance changes, safety concerns emerging from interim study data, or study design modifications aimed at improving trial outcomes. While some amendments are unavoidable, organizations like PhRMA emphasize that rigorous initial protocol design is crucial for minimizing costly revisions and keeping trials on schedule.
How AI is making a difference
Enhanced data accessibility
AI extracts key data from protocols, standardizes documents, and creates a searchable content repository, allowing teams to quickly access and analyze protocol information.
Evidence-based protocol design
AI categorizes and analyzes historical protocols, helping researchers compare trial designs, identify best practices, and refine new protocols based on past insights. When synced with real-world data sources like claims data, AI can help define trade-offs in inclusion/exclusion criteria, ensuring that protocols are both scientifically sound and executable in real-world settings.
Optimized study burden assessment
AI evaluates clinical procedures to determine their impact on study sites and patients. By analyzing visit schedules, assessments, and data collection requirements, AI recommends study designs that balance efficiency and scientific rigor.
Automated protocol digitization
Organizations like TransCelerate BioPharma are digitizing study protocols using frameworks such as Unified Study Definitions Model (USDM) and Study Definitions Repository (SDR). AI-driven automation integrates protocols seamlessly with electronic data capture (eDC) systems and regulatory submission formats, reducing trial execution time and costs.
Beyond protocols
Looking ahead, AI-driven NLP algorithms could expand beyond protocol creation to automate other time-consuming regulatory documents, including investigator’s brochures, IND/NDA submissions, safety reporting packages, and regulatory briefing documents.
AI in Study Design
Clinical trials are complex, expensive, and time-consuming. AI predictive modeling is reshaping the way studies are designed, helping researchers make data-driven decisions that improve efficiency, patient recruitment, and study success rates. By analyzing large datasets, AI can identify patterns that refine trial parameters and reduce uncertainties in study outcomes.
Key applications of AI in study design
Feature selection and extraction
AI algorithms can automatically identify relevant features from large datasets. For example, they can analyze patient demographics, lab results, and medical history to determine which variables are most predictive of outcomes.
Model building and selection
AI models, such as random forests, gradient boosting, or neural networks, can learn patterns from historical data. They automatically adjust their parameters to optimize predictive accuracy so researchers can compare different models to choose the most effective one.
Hyperparameter tuning
AI algorithms have hyperparameters (settings) that affect their performance. Using techniques like grid search or Bayesian optimization help find the best hyperparameters for a given problem.
Cross-validation and validation metrics
AI assists in cross-validation, where the dataset is split into training and validation subsets. Utilizing metrics like accuracy, precision, recall, and F1-score evaluate model performance.
Predictive insights
AI models provide probabilities or predictions for specific outcomes. Clinicians can use these insights to tailor treatment plans or allocate resources effectively
Adaptive clinical trial design
AI helps optimize trial designs by dynamically adjusting sample sizes, treatment arms, and endpoints based on accumulating data. This reduces costs and accelerates clinical trials.
Patient recruitment and retention
AI improves patient recruitment by identifying eligible participants quickly and predicting dropout rates.
Real-time monitoring
AI monitors patient data during trials, detecting potential data integrity issues, potentially unreported adverse events or treatment responses promptly
AI in Clinical Trials
Site feasibility and patient recruitment
Transforming Site Feasibility
<a href="https://www.indegene.com/what-we-think/blogs/transformation-of-clinical-trials-through-digitization-are-we-yet-thinking-about-the-site" style="color: #034ea2;" target="_blank" rel="noopener noreferrer"> AI is transforming site feasibility assessments </a> by evaluating trial locations based on historical performance, patient demographics, and infrastructure capabilities.
Site Selection Optimization
AI analyzes past trial data to identify the most effective sites for recruitment, ensuring trials are conducted in locations with access to the right patient populations.
EHR-Driven Patient Identification
NLP tools, as part of broader AI in drug development strategies, can scan electronic health records (EHRs) to identify eligible patients who meet inclusion and exclusion criteria, improving recruitment speed and accuracy.
Patient Diversity with AI
AI suggests approaches to diversify patient populations, ensuring a more representative trial cohort.
AI-Driven Site Education
AI also plays a role in educating trial sites about patient eligibility, increasing their confidence in recruitment.
Transforming personalized research with digital twins
AI-Powered Digital Twins
AI-powered digital twins are revolutionizing clinical trials by creating virtual patient models that mirror real-world individuals. These digital counterparts continuously update with real-time data.
Data Collection & Integration
AI consolidates patient data from EHRs, wearable sensors, and prior clinical trials to build a comprehensive digital representation of an individual.
Predictive Modeling with ML
Machine learning identifies patterns in health data, enabling simulations of how a patient’s condition might evolve under different treatment scenarios.
Personalized Treatment Prediction
By simulating outcomes on a patient’s digital twin, researchers can predict treatment responses and tailor therapeutic strategies before real-world application.
Synthetic control populations
AI can analyze large datasets to identify individuals who closely resemble those in the treatment arm based on key characteristics such as age, demographics, medical history, and lab results. Through statistical matching, AI ensures these individuals serve as realistic comparators. Using this matched data, AI simulates how the disease might progress in these individuals if they had not received the treatment. This creates a synthetic control group, allowing for direct comparison with the treatment group.
Why synthetic controls matter
1.
Reducing Traditional Bias
Reduces bias found in traditional RCTs, where control groups receive placebos
2.
Ethical Alternative to Placebos
Ethical alternative when using a placebo is impractical or unethical
3.
Efficient for Niche Trials
More efficient trials, especially in rare diseases or small patient populations, where recruiting a separate control group is challenging
It's important to note that at the time of this writing, AI-generated digital twins and synthetic controls are still under development. Regulatory bodies are actively evaluating their validity and integration into clinical trial design. However, the potential benefits for faster and more efficient drug development are significant.
The accepted use of digital twins and synthetic controls by regulatory authorities, also directly benefits patient recruitment and retention in clinical trials by increasing the chances that a patient may receive an active and effective treatment under investigation instead of a placebo control or no more than the current standard of care.
AI in Clinical Data Management
Designing and constructing a clinical trial database is a time-intensive process that varies based on trial complexity, therapeutic area, and regulatory requirements.
The challenge:
Manual database design is slow, resource-intensive, and susceptible to errors, increasing the risk of protocol amendments and delays.The opportunity:
AI is revolutionizing database design by automating data extraction, reducing manual workload, and ensuring seamless alignment with study protocols.Cutting database development timelines by 50%: AI can rapidly extract critical information from digitally structured study protocols, automatically identifying the necessary case report forms (CRFs), variables, and assessment schedules using standardized metadata libraries. This automation streamlines the design and assembly of clinical study databases, ensuring they are accurately aligned and ready to capture patient data.
Advanced AI-driven tools—including those based on ICH M11, TransCelerate BioPharma’s Digital Data Flow (DDF) Initiative, and CDISC’s Unified Study Definition Model (USDM)—are transforming protocol development and database construction.
These technologies have the potential to cut development and testing timelines by over 50%, dramatically accelerating the process from IRB-approved study protocol to first patient data capture. Additionally, AI enhances data quality, reduces errors, and minimizes the risk of costly protocol amendments, ensuring greater efficiency and compliance in clinical trials.
AI in Clinical Study Analytics & Reporting
Enhancing Clinical Analytics with AI
Revolutionizing Clinical Trial Analytics
AI is revolutionizing clinical trial analytics by detecting safety signals, treatment efficacy trends, and patient subgroup responses in real time. These insights help optimize trial design, risk management, and personalized treatment strategies, ensuring more efficient and targeted research.
Early Detection of Safety Signals
AI continuously scans clinical data to identify side effects and adverse events before they escalate, ensuring the safety of trial participants and preventing costly issues later on.
Predicting Patient Outcomes
AI models forecast individual responses to treatment, improving trial stratification and study design. This predictive power helps tailor therapies to patient needs and ensures more accurate outcomes.
Post-Market Surveillance
AI enhances long-term safety monitoring, ensuring real-world efficacy beyond clinical trials. It ensures ongoing assessment of a drugs effectiveness and safety after it reaches the market.
Automating Statistical Analysis with AI
Improving Statistical Analysis with AI
AI and machine learning significantly improve statistical analysis by identifying complex data patterns, automating repetitive tasks, and refining treatment response predictions.
Pattern and Relationship Discovery
AI uncovers hidden correlations in patient demographics, dosage levels, and treatment efficacy, leading to more accurate analysis and insights.
Detecting Treatment Heterogeneity
AI reveals how different patient subgroups respond to treatments, allowing researchers to refine trial designs and enhance precision.
Automating Data Processing
AI accelerates descriptive and inferential analyses, freeing researchers from repetitive tasks and enabling a focus on higher-level interpretation.
Reporting with Natural Language Generation
NLG Transforms Statistical Outputs
NLG transforms complex statistical outputs into clear, structured reports, improving communication across stakeholders.
Automated Trial Reports
AI generates structured summaries from clinical data. Example: “The study found a statistically significant difference (p<0.05) in treatment response rates, with the drug group achieving X% and the placebo group Y%.”
Identifying Key Findings
AI highlights critical trial results and efficacy trends. Example: “The new drug significantly improved remission rates compared to standard therapy.”
Customizing Reports for Different Audiences
AI adapts content for regulators (detailed statistical breakdowns and compliance-focused insights), researchers, and patients (simplified summaries of treatment benefits and risks) - making this another standout in modern biotech innovations.
AI in Ensuring Ethical and Secure Use of AI
As AI continues to transform biotechnology, several challenges and considerations must be addressed to ensure its seamless integration, ethical application, and long-term success
Data Privacy and Security
Protecting patient data while leveraging AI is a top priority. Striking the right balance between data accessibility and compliance with stringent privacy regulations is critical to maintaining trust and legal adherence.
Interoperability
AI tools must integrate seamlessly with existing clinical and research systems to maximize efficiency and streamline workflows across all areas of biotech R&D.
Ethical Considerations
The responsible use of AI requires bias mitigation, transparency, and accountability in decision-making to uphold scientific integrity and equitable treatment outcomes.
Regulatory Adaptation
As AI adoption grows, regulatory frameworks must evolve to address the complexities of AI-driven biotechnology, ensuring compliance while fostering innovation.
Navigating these challenges will be essential for unlocking AI’s full potential in biotech research, clinical trials, and drug development, driving faster, safer, and more effective advancements in patient care.
Conclusion
AI is undoubtedly a catalyst for a new era in biotechnology, redefining drug discovery, clinical research, and platform development. For small and emerging biotech companies, AI offers the ability to accelerate R&D, optimize lead selection, refine drug design, and streamline clinical trials like never before. By leveraging predictive analytics, automated decision-making, and digital trial innovations, companies can unlock breakthroughs faster, reduce costs, and improve patient outcomes.
However, responsible AI adoption requires more than just enthusiasm. Seamless integration, interoperability, data security, and regulatory adaptation remain critical for long-term success. As AI reshapes clinical data management, trial reporting, and patient stratification, strategic collaboration with technology partners and CROs will be essential to harness its full potential across biotech R&D.
The future of biotechnology will be AI-driven, data-powered, and patient-centric. Companies that invest in AI today - while maintaining a balanced and pragmatic approach - will not only accelerate innovation but also lead the charge in delivering the next generation of life-saving therapies to patients worldwide.
Bonus Resource
To support your clinical research efforts, we’ve included a Checklist for Evaluating AI in Clinical Research on the link below. This practical tool helps you systematically assess AI’s benefits, risks, and implementation strategies, ensuring that your adoption of AI is informed, ethical, and aligned with best practices. By using this checklist, you can make data-driven decisions that enhance research outcomes while safeguarding data integrity and patient safety.
Acknowledgements
Insights from the Indegene Biotech Council — a collective of industry experts committed to advancing biotech innovation.
Sanjeev Luther
President & CEO, Ernexa TherapeuticsSM
Raul Trillo
President & CCO (retired), Vyluma
Farrell Simon
SVP Comm & Strategy, Trevi Therapeutics
John Glasspool
CEO, VarmX
Vic Clavelli
Founder and CEO, Gables Advisory, LLC
Dan Brennan
SVP, NMD Pharma
Ed Jordan
CCO, Xspray Pharma Inc.
Vineet Singhal
COO, SGN
Rich Daly
President, CARsgen
Shane Shaffer
CEO, Cingulate
Patrick Gallagher
CEO, Voltron
Sandy Loreaux
Former CEO, Bryn Pharma
Brad Bailey
EVP & CCO, GenMab
Kate Hermans
Board Chair, Clue
Jeff Christensen
Head of Market Development, Indivior
Written by Ram Yeleswarapu and Mark Williams from Indegene.
