Drug development is one of the most challenging endeavors in science. The journey from laboratory to pharmacy typically takes over a decade, costs around $1-3 billion and succeeds in fewer than 10% of cases—with most failures occurring in clinical trials when compounds prove ineffective or unsafe.
Artificial intelligence is beginning to improve these odds. Today’s systems can scan millions of compounds in hours and detect patterns in vast datasets at a speed and scale that exceeds what human researchers could practically achieve. While still early, the technology is showing concrete results in various areas of drug discovery.
The potential rewards are significant. McKinsey Global Institute estimates AI could unlock $60–110 billion in annual value for the pharmaceutical industry, with some forecasts suggesting it may power 30% of drug discoveries within the next few years. For biotech companies to realize this transformation, they must deploy AI strategically—focusing on applications where it outperforms conventional approaches.
AI’s most promising frontiers
Through our work with biotech companies around the world, we’ve witnessed artificial intelligence deliver measurable impact across drug development programs. Four applications stand out for their ability to accelerate discovery while reducing risk.
1. Unlocking hidden drug targets
The human body contains roughly 20,000 proteins that could potentially serve as drug targets. Yet only about 700 have ever been successfully targeted by approved medicines. The challenge lies in identifying which proteins can be safely modulated without triggering harmful off-target effects—one of biology’s most persistent puzzles.
Traditional target validation crawls forward one hypothesis at a time through years of experiments, generating mountains of inconclusive data and frequent dead ends that drain resources without advancing science. AI transforms this linear process by simultaneously analyzing genetic associations, protein structures, clinical outcomes and molecular pathways—integrating data types that overwhelm human analytical capacity.
Instead of testing individual hypotheses sequentially, machine learning models evaluate thousands of disease-protein relationships in parallel. These systems learn from decades of pharmaceutical failures, recognizing molecular signatures that historically predict safety problems or development roadblocks before teams commit significant resources.
The payoff is becoming clear. Research teams can now identify and prioritize targets with greater confidence early in the process, focusing resources on the most promising opportunities rather than spreading efforts across multiple uncertain paths.
2. Designing better molecules faster
Traditional drug design follows a predictable and inefficient pattern. Chemists create variations on known molecular structures, synthesizing hundreds or thousands of compounds to find one with the right properties. It’s a process that combines deep expertise with educated guesswork and often takes years to produce results.
AI-driven molecular design operates differently. These systems generate novel structures while simultaneously optimizing multiple properties: target binding, safety profiles, metabolic processing and manufacturing complexity. Instead of optimizing for effectiveness first and addressing toxicity or absorption problems later, AI balances critical properties from the outset.
Some platforms work backward—starting with desired therapeutic properties and generating molecular structures designed to deliver them. They can explore vast chemical spaces that human chemists could never fully search, identifying promising molecules that don’t follow traditional design patterns.
Laboratory validation remains essential, of course. No AI system can fully predict how a molecule will behave in living organisms. But by starting with computationally optimized candidates, companies report improved success rates in their experimental programs.
3. Breathing new life into failed compounds
Every major pharmaceutical company maintains vast libraries of compounds that never reached market. These collections contain thousands of molecules that showed initial promise but ultimately failed in development—representing billions in sunk costs and decades of research efforts.
Many of these “failed” compounds could succeed against different diseases than originally intended. A cardiovascular drug that proved ineffective might treat a rare genetic disorder. Or a cancer therapy too toxic at high doses might work safely at lower doses in autoimmune conditions.
AI is showing strong promise in identifying repurposing opportunities. By analyzing molecular interactions and disease pathways, it suggests new applications for existing compounds and predicts safety profiles before expensive clinical trials begin. This capability helps companies prioritize repurposed drugs most likely to succeed. Notable successes include the AI-driven repurposing of baricitinib for COVID-19 and the identification of new applications for existing oncology compounds.
This approach proves particularly valuable for rare diseases, where traditional development economics rarely justify massive investments for small patient populations. Repurposed drugs offer hope to patients with limited treatment options, at a fraction of the cost and time required for entirely new compounds.
4. Predicting safety and toxicity earlier
Traditional safety testing relies heavily on animal models and limited human data, often failing to predict adverse effects that only emerge in large patient populations. This creates a critical blind spot that has led to high-profile drug withdrawals and billions in losses.
AI safety platforms complement traditional testing by analyzing molecular structures, biological pathways and historical toxicity data to flag potential safety concerns before compounds enter expensive testing. By identifying liver toxicity, cardiac risks or drug-drug interactions early, these systems help companies avoid late-stage failures that devastate both timelines and budgets.
More sophisticated models can predict how different patient populations—based on genetics, age or existing conditions—might respond to specific treatments. This capability is particularly valuable for precision medicine, where understanding individual risk profiles can mean the difference between therapeutic success and dangerous side effects.
What’s coming next
As the decade progresses, AI is transitioning from a promising tool into a core collaborator in drug discovery—one that’s reshaping how researchers tackle the field’s most complex challenges. The next wave of innovation lies in the technology’s ability to integrate massive data streams, generate entirely new possibilities and optimize the entire journey from lab to patient.
Multimodal AI is gaining sophistication
Different types of biomedical data—genomic sequences, protein structures, medical imaging and patient histories—have traditionally been analyzed in silos. Multimodal AI breaks down these barriers by integrating diverse datasets to reveal relationships invisible to human analysis, showing how genetic mutations translate into structural protein changes and, ultimately, clinical symptoms.
Consider a rare neurodegenerative disease, for instance. A multimodal platform might link a specific mutation to misfolded proteins detectable on brain scans, then predict how these changes drive disease progression. By seeing the complete picture rather than isolated fragments, researchers can identify potential targets and interventions far earlier than traditional methods allow.
Generative AI enters the lab
Generative AI models, similar to those behind today’s conversational systems, are revolutionizing molecular design. Scientists can specify highly precise requirements—a molecule that crosses the blood-brain barrier, binds to a particular receptor, and degrades within an exact timeframe—and AI generates dozens of candidate compounds meeting all criteria.
This represents a fundamental shift in how drugs are conceived. Traditional medicinal chemistry involves months of iterative optimization, constantly balancing effectiveness against safety and manufacturability. Generative AI approaches the challenge holistically, optimizing multiple properties simultaneously while exploring chemical spaces that human chemists might never consider.
The result? Novel molecules that would otherwise remain undiscovered. While laboratory validation remains essential, starting with computationally optimized candidates dramatically improves downstream success rates, ultimately shortening the timeline from discovery to viable therapy.
Clinical trials are finally getting the AI treatment
Patient recruitment has long created critical bottlenecks—approximately 40-50% of clinical trials, particularly in oncology and rare diseases, struggle to enroll enough participants, and many recruit patients unlikely to respond while excluding those who might benefit most. AI is beginning to solve this challenge by mining electronic health records, wearable device data and social determinants of health to identify ideal trial participants with unprecedented accuracy.
Beyond recruitment, AI predicts patient dropout risk, suggests adaptive protocol changes based on interim results, and models how different populations respond under varying conditions. These capabilities extend far beyond saving time and money—they can meaningfully accelerate access to new therapies, particularly for conditions where delays prove life-altering.
For rare diseases with small patient populations, AI-driven optimization can transform previously infeasible trials into viable research programs, offering hope where traditional approaches fall short.
Real-world evidence is accelerating validation
AI is also transforming how companies validate their discoveries using real-world data from electronic health records, insurance claims, and wearable devices. Rather than relying solely on controlled clinical trials, researchers can now analyze how treatments perform across diverse patient populations in actual clinical practice.
This approach proves especially valuable for understanding long-term safety profiles and identifying patient subgroups that respond particularly well to specific therapies. By combining traditional trial data with real-world evidence, companies can build more comprehensive safety and efficacy profiles, potentially supporting regulatory submissions with richer datasets.
The bottom line
AI is already working in drug discovery. Companies are using it to find new drug targets, design better molecules, give old drugs new uses and catch safety problems early—and they’re seeing measurable results. The technology keeps getting better, and companies that adopt it now will have a significant advantage over those that wait.