To address scientific and operational bottlenecks, research teams are exploring technologies like machine learning to enroll patients, collect data, and manage ongoing studies. 

With AI in healthcare surpassing $21 billion in market value, new tools for streamlining clinical research are emerging rapidly. 

This post is a snapshot of AI use cases in clinical research, companies fueling innovation in the sector, and the current state of VC funding. 

This is the final part of the series covering AI applications in drug R&D. Explore our overview of machine learning use cases in drug discovery and preclinical research. 

Why clinical trials fail 

9 out of 10 drug candidates don’t go past clinical research. Despite multiple attempts to fine-tune preclinical research so that it only yields promising leads, high failure rates persisted for decades. 

The most cited reasons as to why clinical trials fail are lack of clinical efficacy (40-50% of reported cases), high toxicity (30% of surveyed RCTs), suboptimal drug-like properties (10-15%), and lack of market demand or poor planning (10%). 

While each of these issues stems from different parts of the R&D pipeline, together they highlight a critical need for more predictive tools, better trial design, and smarter patient matching, all areas where AI is starting to make a tangible difference.

The balance between efficacy and toxicity

The failure to reach clinical efficacy is the primary reason why drug candidates do not go past Phase I clinical trials. That does not necessarily mean that tested molecules are ineffective per se, rather that they fail to show therapeutic benefits at maximum tolerable doses (MTD). 

At a higher concentration, many drug candidates would have been effective, but by then, they show toxicity in healthy organs, creating a balancing act between maximizing the potency and reducing side effects. 

This particular aspect tends to be overlooked in drug discovery and clinical development because it is hard to predict why two structurally similar molecules can yield drastically different clinical outcomes.

For example, the analysis of over 600 RCTs conducted on FDA-approved or failed selective estrogen receptor modulators (SERMs) showed that slight structural changes, which did not alter the compounds’ drug-like properties, led to significant variations in efficacy and toxicity. 

Having better visibility of patient response to a drug and the ability to identify early signs of drug toxicity would help reduce the rate of clinical failure. 

Suboptimal drug properties 

Traditional drug candidate screening often begins with large-scale testing of chemical libraries, but the criteria used are typically generic and overlook how different patients, diseases, and tissues interact with the compound.

Researchers report that the lack of personalization makes assessing drug properties “a shot in the dark” and lends itself to their failure in the clinical context. 

The research team behind Remdesivir, a prospective COVID-19 therapeutic, failed to consider the importance of drug exposure to different tissues. 

While preclinical tests of the compound showed promise, in clinical research, it achieved little exposure in the target tissue, the lung, yet tended to precipitate and cause high toxicity in the kidney. 

Cases like this underscore the importance of developing tools that can quantify tissue-specific drug exposure and predict pharmacokinetic behavior under real-world conditions.

AI-powered models now offer promising capabilities to simulate drug distribution, analyze how molecules behave in diverse biological environments, and tailor compound selection to patient-specific profiles, making trial outcomes far less random.

Operational bottlenecks

Even when a drug shows potential, operational breakdowns often prevent it from reaching approval. These issues aren’t about the compound’s efficacy or safety; they stem from how trials are designed, funded, and executed.

Here are the most impactful operational bottlenecks in clinical research: 

• Patient recruitment. In the field of cancer research, 25% of trials fail to enroll the needed number of participants, and only about 2-5% of the total population of adult patients participate in clinical research. 

• Lack of guidance on inclusion/exclusion criteria. Clearly defined eligibility criteria help match the patient profile used in research to the target population. Yet, in many therapeutic areas, there are no clear-cut diagnostic criteria to begin with. For instance, there is high variability in diagnosing heart failure with preserved ejection fraction (HFpEF). Some researchers use EF > 50% as a cut-off, while others choose different values. This ambiguity creates difficulties in clearly defining a target patient profile. 

• Financial strain. While the costs of clinical trials vary from study to study, they may reach up to $2.5 billion. It is unsurprising, then, that 22% of aborted Phase III studies fail due to lack of funding. White part of these expenses, such as site and patient management costs, are more challenging to slash, optimizing processes and cutting the duration of clinical research could help address the underfunding challenge. McKinsey notes that cutting the duration of clinical studies by a year would add over $400 million to a sponsor’s portfolio.

Discovering ways to automate operations, build stable clinician-patient relationships, and slash costs would help increase the completion rate of clinical trials. 

How AI is improving and fast-tracking clinical research

Clinical research is entering a new phase where AI doesn’t just support the process, but actively accelerates it. Early adopters of machine learning are already reporting both scientific and operational wins.

McKinsey reports that research teams have successfully used AI to optimize trial sites, boost patient enrollment by up to 20%, and predict enrollment trends to attract new sources of funding. 

Using AI across the research pipeline helped compress the duration of a randomized clinical trial by an average of 6 months. This is a massive gain in a field where time equals both cost and patient outcomes.

But even greater efficiencies are emerging with generative AI. By automating time-consuming manual tasks, from drafting regulatory documents to assisting with decision-making and compliance communications, genAI has the potential to cut process costs by up to 50%.

Biotech companies are driving this change by building ML-enabled solutions that streamline all key areas of clinical research. 

Here is a snapshot of promising AI applications and innovators to watch.

Patient enrolment

AI platforms help process providers’ patient data records and identify populations that meet the inclusion and exclusion criteria of the trial. 

Machine learning algorithms help research teams organize unstructured EHR data to identify potential matches. In 2024, the NIH built a matching engine to connect volunteers to relevant clinical trials. The algorithm was trained on the RCT records kept at ClinicalTrials.gov. 

Smaller institutions with limited data access can still use machine learning to automate patient screening. 

Altru, a North Dakota-based healthcare service provider, partnered with Paradigm Health to build a data-driven patient screening system. Paradigm’s technology used machine learning to assess each patient admitted to Altru as a potential RCT candidate. 

This ML-enabled approach helped increase the annual clinical trial enrolment rate from 4% to 11%. 

AI startups innovating patient enrolment: 

• Paradigm Health analyses each provider’s patient population and incoming study protocols with LLMs to “source smarter” and pick trials that truly fit local patients. 

• Deep6 AI created algorithms to score, qualify, and prioritize matches across a 1,000-site research ecosystem. The platform gives research teams real-time visibility into where they can find recruitable patients.

Protocol design and optimization

On average, principal investigators on a research team need between 150 and 200 hours to write a clinical trial protocol that would lay out the objectives, key steps, and criteria for patient inclusion/exclusion. 

Standardizing and automating this process with AI reduces this strain. 

Xenoss engineers help clinical trial teams automate protocol design by building proprietary large-language models trained on real-world protocol data, as well as the pre-clinical records kept by the research team. 

These models follow structured prompts from clinical teams and generate protocol drafts aligned with industry standards and regulatory expectations, cutting turnaround times and minimizing manual revisions.

AI startups that streamline protocol design:

• Healx mines literature, biomedical data, and patient-reported insight to support protocol writers with outcome measures for small-patient, rare-disease trials. 

• PhaseV delivers real-time dashboards to help teams accurately estimate sample size, add arms, or pivot endpoints. 

Clinical trial management

McKinsey reports that industry-standard “control tower” teams, which are centralized systems for aggregating patient data across trial sites, capture only 70–80% of actual clinical progress. The remaining 20–30% gap represents missed signals, delays, and manual overhead that slow down decision-making and risk regulatory setbacks. 

Machine learning has the tools to automate patient data gathering and processing to cover the gaps in understanding a patient’s response to the drug. 

Furthermore, there is a spike in enthusiasm around using agentic AI solutions to automate complex workflows like email exchanges between the clinical and research teams, patient communication oversight, progress tracking, and reporting. 

Research teams can now build multi-agent systems that collectively oversee data aggregation, patient monitoring, fact-checking, and reporting outcomes. All agents report to the coordinator agent and have shared access to RCT data.

AI startups optimizing clinical trial management: 

• Lindus Health built SPROUT, an internal LLM tool, that shortens protocol creation from weeks to hours by drafting and red-lining new protocols based on historical RCT and KOL data. 

• R.grid uses ML to monitor task queues, flag bottlenecks, and set up robotic process automations. The platform’s users report up to 45 % cost cuts and months of timeline reduction.

Decentralized clinical trials

Following the COVID-19 pandemic and the surge of telemedicine, there has been increased interest in running decentralized clinical trials. If they were to become industry standards, clinicians and researchers would have more ways to enroll and monitor the progress of stay-at-home patients.  

At the moment, decentralized trials are met with friction due to compliance, patient monitoring, around-the-clock data gathering, and outcome reporting challenges. 

Machine learning makes the process more sustainable by improving recruitment, retention, and post-trial monitoring of DCT patients. 

Research teams report using phenomapping algorithms to identify ideal patient profiles for decentralized trials. 

Tapping into generative AI to automate the process of filling surveys, logging events, and writing disease diaries lifts a significant burden for patients enrolled in remote trials, which currently contributes to a 30% dropout rate and causes 40% of patients to stop adhering to the protocol within 150 days of the study. 

Some teams successfully trained patient-specific deep learning models to offer DCT participants personalized guidance and promote compliance. 

Biotech companies leveraging AI for decentralized clinical trials: 

• Castor uses human-in-the-loop AI on Microsoft Azure to read eSource files, push clean data into the EDC, and auto-complete Source Data Verification. This system eliminates up to 70% of manual data entry. 

• Huma combined continuous sensor ingestion with ML models to create digital biomarkers and risk scores. The use of the platform in the DeTAP atrial-fibrillation trial helped researchers reach 94 % recruitment in 12 days and raise medication adherence from 85 % to 96%. 

Supply chain

According to Deborah Knoll, Director of Clinical Supply Optimization Services at Thermo Fisher Scientific, clinical trial supply planners typically face supply chain challenges in three areas: 

• Demand vs. delivery speed: In some cases, the production rate does not match the pace of the study. 

• Regulatory burdens related to labeling, documentation, export, and import of drug candidates. 

• Environmental bottlenecks, such as changing weather conditions, can delay delivery. 

Machine learning helps optimize supply chains in all three areas. 

For one, AI models can analyze historical supply chain data and identify trends and best practices for future shipments. Later, this insight can be used by other models to run real-time simulations of the entire supply chain and identify risks before they manifest in real-world conditions. 

Companies applying AI to the pharma supply chain: 

• S lab healthcare predicts both the probability and precise timing of temperature excursions by analyzing multiple supply-chain variables. 

• Clinion employs generative AI modules to model complex supply scenarios, such as testing routing and allocation strategies in silico before trial start-up.

Pharmaceutical manufacturing

Companies using AI to streamline pharmaceutical manufacturing: 

• MultiplyLabs empowers imitation-learning robots that watch expert scientists performing tasks (e.g., resuspending cell flasks) and copy those motions. The platform then “auto-programs” itself for new cell-therapy processes.

• Inscripta blends Directed Evolution, CRISPR+ (royalty-free MAD7 nuclease), and machine learning models to accelerate DBTL cycles and lower the cost per variant to ≈$0.10. 

Data analysis

Clinical trials used to rely on retrospective data analysis but this approach comes with limitations. If there are errors in clinical data, there is no way to go back and correct the parameters of the study after the fact and valuable time has been lost. 

Integrating real-time data into clinical research, on the other hand, allows research teams to proactively respond to protocol deviations, recruitment delays, or other bottlenecks along the pipeline.

That said, the need for advanced analytics capabilities is a barrier to collecting and processing large volumes of clinical data in real time. 

Machine learning can streamline analytics by harmonizing the data that enters the system from different sources (patient-reported outcomes, wearables, EHRs). By automating data cleaning and validation, researchers no longer need to run repetitive sorting and can focus on high-value analysis. 

Companies using AI for clinical data analysis: 

• Qureight uses computer vision to segment CT scans into fibrotic, vascular, and airway compartments and turn raw scans into quantitative biomarkers for safety read-outs.

• Formation Bio ingests all trial sources (EDC, ePRO, RWD, documents) into its Universal Data Platform. Then LLM/agentic AI pipelines auto-clean, standardize, and push data to dashboards, replacing manual data entry. 

Pharmacovigilance

After the drug hits the market, it is essential to continue monitoring patient response and tracking previously unknown adverse effects. 

Pharmacovigilance efforts struggle to scale due to a massive underreporting problem. On average, the therapeutic responses to a chosen course of treatment are off researchers’ radar for 94% of patients. 

Machine learning helps overcome pharmacovigilance limitations by generating automated reports from EHRs, wearable sensors, and other data sources. 

Clinical teams are already applying AI to detect drug-drug interactions, detect early signs of adverse drug effects, and improve communication between the patient and the clinician. Deep learning models like the Multi-Dimensional Feature Fusion (MDFF) model analyze the structural properties of drugs patients are prescribed in polytherapy and identify the probability of DDIs and ADRs. 

Biotech companies using AI in pharmacovigilance:

• Century Health abstracts unstructured EHR notes, imaging, and -omics into standardized OMOP/SNOMED formats to create accurate chart reviews and longitudinal patient records. 

• Selta Square runs a PV-as-a-service platform that marries IBM Robotic Process Automation to automate global literature and database surveillance for adverse drug reactions.

Funding trends in AI-enabled clinical research

Where drug discovery saw increased investor interest in the last eighteen months, the scale of funding for AI projects in clinical research has been dropping consistently since 2021. Interestingly, the number of deals has remained stable, but the average deal size has shrunk.

Despite a slower stream of venture capital, startups in the sector are showing consistent operational growth. Headcounts at AI clinical development startups grew by 14% last year, and in some markets, like protocol design and optimization tools, they increased by nearly 40%. 

This operational growth and declining funding may suggest that the sector is becoming mature, with companies that generate revenue independently and become less dependent on VC funding. 

Several indicators point to an upcoming wave of IPOs and acquisitions:

• There are now six unicorns in the AI-for-clinical space, with Huma being the most recent (2024)
• Owkin and Inscripta lead the field in IPO readiness, with CB Insights assigning them IPO probabilities of 21% and 13%, respectively

If these firms successfully transition from private unicorns to public companies, it would validate the commercial promise of AI in clinical development. It could trigger a fresh wave of investor confidence across the field.

Bottom line

Among drug R&D development stages, clinical research is the most mission-critical, expensive, and time-consuming. 

There’s a clear need for innovation that would move the needle on the high failure rate of clinical trials. At the same time, heavy regulatory oversight makes innovating in this space challenging, so VC engagement in the sector is moderate. 

Thus, clinical research is somewhat stuck between a rock and a hard place. The good news is that emerging technologies like genAI and agents have found a way to trickle into the space. The pace of innovation is not yet rapid enough to make a statistically significant difference, but the adoption rate is growing, and launched AI pilots tend to perform well. 

AI won’t replace clinical research. But it will make it faster, smarter, and more resilient.

The post AI applications in drug R&D: How machine learning is reshaping clinical trials  appeared first on Xenoss - AI and Data Software Development Company.

Despite this rapid advancement, science has so far failed to help advance more drugs past preclinical research. On the contrary, the cost of pushing a new drug into clinical development is expected to double every nine years. 

R&D teams and clinicians are increasingly concerned by the emerging rift between preclinical research and clinical testing, known as the “Valley of Death.” 

This article examines how AI in the pharmaceutical industry helps bridge the gap between in vitro or animal stimulations and the clinical success of a drug candidate. 

It draws a landscape of emerging AI trends in the pharmaceutical industry, biotech companies innovating preclinical research, and offers a VC market outlook to help team leaders pinpoint the highest-yield automation areas. 

This post is the second article in our series on AI in drug R&D. Part 1 offers a detailed review of AI use cases in the pharma industry, top innovators, and VC market landscape in drug discovery.  

Some insights featured in the article draw upon the takeaways from the AI in Pharma: The New Playbook for Drug Research & Development” webinar hosted by Ellen Knapp, Senior Intelligence Analyst in Healthcare & Life Sciences at CB Insights.

What created the Valley of Death

The deep chasm between theoretically groundbreaking biochemical research and practical improvements to clinical medicine is no news to researchers, pharma companies, or physicians. 
The traditional pipeline of preclinical research, which involves gene testing in vitro followed by experiments on animal models, seems to translate poorly to human physiology. For several decades, the rate at which preclinical trials fail to apply to human clinical trials has been roughly 92%.

Plenty of reasons can account for such spectacular failure, from human biological heterogeneity to poor hypothesis formulation or a lack of funding and incentives, but two limitations are rooted in the pharma value chain. 

Root cause #1: Limitations of animal models

The physiological differences between humans and lab animals make it nearly impossible to generate accurate drug toxicity or metabolism predictions. Drug Discovery Today points out that the failure to mirror human bodies comes at especially high costs in targeting complex therapeutic areas, like neurology or psychiatry. 

Even if a close neurological match were possible, ethical concerns severely limit such research. Studies on primates, for instance, are widely discouraged. In fact, the NIH recently suspended all primate testing and retired existing test subjects to sanctuaries.

Both inherent limitations and ethical concerns around animal testing are pushing the FDA to endorse the 3R framework (Reduce, Recycle, Refine) model and try to substitute animal tests with computational analysis. 

Root cause #2: Reliability and reproducibility

A paper on the challenges of preclinical research in myogenic cell therapy, published last year in Cells, attempted to reproduce the findings of 193 papers published in the research area. They found out that only four published the statistical data needed to recreate these experiments. 

In 67% of cases, preclinical CROs had to modify the protocol to complete the experiments as the real-world context did not align with preclinical data, and only 41% of adjustments were feasible. 

This is not an isolated case of expectation-reality misalignment. 

Consider the infamous failure of Bial, touted as a first-in-class drug candidate that failed to predict neurotoxicity in animal models. That oversight sent six volunteers to intensive care, resulting in one death and four long-term neurological injuries.

More recently, the EVELYDIS clinical trial, a drug indicated for treating Duchenne muscular dystrophy and supported by seemingly impeccable pre-clinical records, recorded the death of a 16-year-old in a Phase I clinical trial. 

With human lives at stake, low prediction accuracy, reliability, and reproducibility of preclinical research in drug development are alarming at least and unacceptable at most. 

Can artificial intelligence in pharma bridge the Valley of Death in preclinical research? 

Poor data accuracy, a lack of alternatives to animal testing, and a lack of guardrails that control the reproducibility of research before the authors share it with the rest of the scientific community seem within the grasp of machine learning. 

Indeed, the applications of AI in preclinical research are on the rise and cover all key areas of the pipeline: 

• In vivo simulations: Testing drug effects in living organisms
• Drug R&D process chemistry: Designing and optimizing drug synthesis routes
• Drug development strategy and compliance: Automating the research pipeline and the submission of IND applications 
• Drug formulation and tablet development: Creating effective and stable drug delivery forms (e.g., pills, patches, sprays)
• Toxicology and in vivo studies: Assessing safety and biological effects in animal models
• Drug analytics and material science: Analyzing drug composition and material properties

AI biotech companies are emerging in every area. Let’s zoom in on the ways in which these teams use artificial intelligence to improve the flow of preclinical testing and the milestones they have reached. 

In vivo simulations

Achieving an optimal pharmacokinetic (PK) profile is essential for a drug candidate’s clinical success. Traditionally, this insight has relied on a combination of in vitro experiments and in vivo animal testing.

However, these approaches are resource-intensive and often fail to capture the synergistic effects of human physiology on drug behavior.

AI researchers are addressing the challenge by designing in-vivo simulators that predict key pharmacokinetic properties (pKa, crystal density, apparent permeability, protein unbound fraction, plasma clearance, etc.) of drug candidates based on molecular data. 

Early studies show that AI-generated PK predictions closely match human-curated data, typically falling within two-to-threefold error margins, a level of accuracy that makes them viable complements to lab-based testing.

Biotech startups to watch

• Deep Kinetix creates patient-specific digital twins to simulate PK/PD responses, predict doses, and discover side effects before the drug enters clinical trials. The company’s technology ingests data from organs-on-chips (miniature systems that simulate the physiology of target organs) and enriches it with machine learning for more accurate predictions. 

• CytoReason builds computational models of cells targeted by drug candidates. The company leverages AI in the medical field to analyze disease progression by combining multi-omic layers (RNA sequences, proteomics, cytometry) with natural language processing. 

Drug R&D process chemistry

Optimizing drug candidates helps cut preclinical phase costs and time-to-trial compared to designing molecules de novo. While existing AI models are unreliable in creating effective molecules from scratch, they show more promise in improving the structure of shortlisted candidates. 

To that end, process chemists are increasingly leveraging generative AI in pharma for structure completion and property refinement. REINVENT, 15DeepFMPO, and MCMG17 are a few up-and-coming models that focus on optimizing molecular scaffolds for key performance indicators such as binding affinity, solubility, and metabolic stability.

AI medical companies to watch

• Phorum uses Phorum-GPT transformers trained in proprietary reaction and in-vitro data to predict synthesis risk, purification routes and formulation tweaks early in route-scouting. 

• Molecule.one applies deep-learning retrosynthesis and condition-prediction engines trained on a real-world reaction library with over 100,000 data points to automate route selection and experiment planning for molecular chemists.
• Cellino creates digital twins to simulate biomanufacturing pipelines. It also uses computer-vision-enhanced deep learning to filter out low-quality pluripotent stem cells, driving bioprocess yield up to 80%. 

Drug development strategy and compliance

Preclinical drug discovery is strictly governed by the FDA in the US and EMA (European Medicines Agency) in Europe. To keep preclinical studies compliant, researchers have to keep track of guidelines and fill out approval paperwork. 

AI helps streamline these workflows by semi-automating reporting, documentation, and quality control. It can also improve the reliability and reproducibility of preclinical testing by enforcing a system of checks and balances that validates the real-world feasibility of published data. 

Looking ahead, when machine learning is ubiquitous in preclinical development, AI-enabled IND platforms can share data with other solutions for AI in drug development (in-vivo stimulators, R&D optimizers, toxicology enhancers) to generate entire sections of the proposals that research teams review and submit to the FDA. 

Biotech AI companies in the space

• Tehistark offers an AI medical documentation designer that reportedly cuts the time needed to draft a proposal by 50%. 

• Lighthouse AI is a medical AI app that scans the FDA and DEA boards. The platform generates reports that help research teams monitor guidelines. Researchers receive real-time alerts if new regulations directly impact their projects. 

• Weave helps teams auto-draft approvals, process source data with AI, and fine-tune proposal texts with generative AI. 

Drug formulation and tablet development

Among all preclinical workflows, formulation and tablet development have emerged as one of the most promising and well-funded areas for AI in the medical field.

Four of the sector’s top ten VC deals in the last two years have gone to medical AI companies in this space, and more than half of the investment dollars have been awarded to the market. Machine learning is used here to optimize formulations for better bio-performance, manufactureability, and stability. 

Why the surge? 

Formulation design has become increasingly complex across drug classes. Biologics, for example, often require sophisticated delivery vehicles like mRNA encapsulated in lipid nanoparticles (LNPs). Meanwhile, many small-molecule drugs demand advanced formulations such as spray-dried dispersions to improve solubility and permeability.

In both cases, formulation development workflows are becoming multi-layered. Therefore, technologies that help streamline them are gaining popularity. 

One standout player is Metis Therapeutics, which takes a dual-pronged approach: its AI-LNP platform targets biologics delivery challenges, while its small-molecule optimization tools address physical-chemical hurdles. 

This strategy has garnered massive investor interest, making Metis Therapeutics the highest-funding biotech startup of the last two years. 

Other AI medical companies reinventing formulation design

• Metis Therapeutics trains transformer models on a 10 M-compound virtual lipid library to predict ionisable-lipid and finished-LNP properties before synthesis to cut wet-lab runs and optimize AI-driven formulation design in pharmaceutical factories. 

• TaBlitz supports drug manufacturing companies with an AI-powered 3-D tablet-design studio, featuring deep-learning models trained on > 160k historical designs output assessed for manufacturability, tooling life, swallowability, and dissolution for every geometry. 

• ViaNautis, recently partnered with Eli Lilly to use AI in pharmaceuticals and backed by a Series A, is pioneering AI-driven polymer-nanoparticle design among other pharmaceutical industry trends. 

Toxicology and in vivo studies

The traditional approach to studying adverse effects relied on initiating molecular events, such as drug-receptor interactions, analyzing the response to the event on the cellular level, and its impact on the subject’s physiological function.

This approach allowed to identify hundreds of adverse outcome pathways but the accuracy and speed of the process can still be improved. 

Researchers are increasingly turning to machine learning to modernize this process, and early results are promising.

In one study, a Bayesian neural network was pitted against empirical dose-response modeling and systems biology modeling in the assessment of oxidative-stress-induced chronic kidney disease. The Bayesian neural network was more precise than the dose-response modeling and more sustainable than system biology modeling. 

Similar reports of neural networks outperforming their traditional counterparts make the scientific community more accepting of using machine learning tools in toxicity prediction. 

The table below reviews machine learning models currently gaining traction in toxicology. 

Biotech startups to watch

• Invivo Cloud uses computer vision for “behavioral sequencing” of animal studies, simultaneously analyzing up to 100 mice. The platform supports noninvasive AI readouts of circadian rhythm, activity, and stress, feeds PK/PD dashboards. 

• Ignota Labs combines cheminformatics and bioinformatics to discover new toxicity pathways and brainstorm resolution mechanisms. The platform deploys graph neural nets and transformers to trace the causes of early adverse effects before they spiral into late-stage organ failures. 

• ModernVivo mines thousands of papers in seconds, extracting doses, models, and endpoints to auto-draft optimal in vivo protocols. The company’s “Design Tools” use semantic search, retrieval-augmented generation, and analytics dashboards to cut study design time and support 3Rs compliance. 

Drug analytics and material science

The ability to process massive volumes of data is one of machine learning’s core advantages, making drug analytics and material science ideal beneficiaries of AI in life sciences.

Researchers have long used machine learning to unify and mine complex datasets. Now, biotech innovators are advancing the field by building analytics engines for digital twins — AI-modeled simulations that reflect patient profiles and track real-time biomarker data such as blood chemistry, hormone levels, and circadian cycles.

These digital readouts offer a more nuanced understanding of how patients may respond to a drug across different physiological states, reducing clinical risk and helping optimize trial protocols before first-in-human testing. 

Smart “liquid handlers” are another promising application for AI in pharma. They automate repetitive materials screen assessments and improve materials scientists’ productivity. 

AI drug development companies in the space

• BioYond Robotics combines robotics, automation, and data engineering to clean, fuse, and interpret high-throughput data for over 100 pharmaceutical teams. 

• Lifespin created an AI agent that delivers differential diagnosis and drug-response analytics at scale and streams actionable, ready-for-R&D application results to dashboards and APIs. 

• Lavo Life Sciences combines quantum-chemistry simulations with deep-learning ranking to screen and score lattices ≈100× faster than legacy CSP workflows. 

AI innovation in preclinical drug development is still in an early stage

Pre-clinical research protocols undergo strict regulatory scrutiny, unlike drug discovery workflows that are not rigidly defined and offer R&D teams and tech innovators more room for maneuver. 

To initiate human trials, the FDA mandates a full toxicology report, safety pharmacology assessments (for cardiovascular, respiratory, and nervous systems), genotoxicity testing, and comprehensive ADME/PK profiles. These requirements demand high data fidelity and explainability, raising the bar for deploying machine learning tools.

As a result, most biotech startups in preclinical AI drug development remain in early-stage funding rounds, with investors waiting for stronger proof of real-world validation.

Among the exceptions, formulation development stands out as the most rapidly maturing vertical in the AI pharma market. New pharmaceutical companies like Metis Therapeutics, ViaNautis, Mana, and PersistAI have all raised Series A to C rounds over the past three years.

Other areas, including compliance automation, material science, and in vivo simulations, are also gaining momentum. Together, these three verticals have attracted over $150 million in VC funding since 2022, indicating rising investor confidence across the preclinical AI landscape. 

Bottom line

Preclinical research is a critical bridge between the highly theoretical drug discovery pipeline and the high-risk testing of a drug candidate in clinical settings. 

It is crucial that, by the end of this stage, drug candidates have a full outline of PK/PD properties, predictable safety profiles, optimal formulations, and defined posology. 

Preclinical tests don’t offer sufficient breadth of insight, especially in the R&D workflows of drugs targeting the nervous system. Physiological differences and ethical dilemmas corner researchers into settling for imperfect models and risk eyebrow-raising failures in a clinical setting. 

AI in biotech can be a reliable bridge across the bench-to-bedside gap by ensuring that, at the time of a patient’s contact with a drug, there are no grey areas in PK/PD profiles and side effects. 

Generative pharma AI, deep lead optimization, and neural networks applied to toxicology all aim to increase the R&D team’s knowledge of the drug before it enters clinical trials. AI also brings operational benefits, cutting the time needed to submit applications and keeping track of regulatory updates. 

AI in the medical field and preclinical research is still in its early innings. But with the right mix of technical talent, capital, and regulatory alignment, it has the potential to solve decades-old pain points and reshape the transition from lab to clinic in the years ahead.

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On average, bringing a new drug to market costs $2.5 billion, and many candidates never make it through pre-clinical and clinical testing. 

Out of hundreds of thousands of molecules screened, only 35% show any therapeutic potential. Of those, just 9–14% survive Phase I clinical trials.

The multi-step process of demonstrating the efficacy of a newly discovered drug, its ability to compete with other formulations on the market, and the lack of side effects that undo the therapeutic benefit explains why the R&D process takes anywhere from twelve to fifteen years. 

In such a challenging market, pharmaceutical companies are always looking for ways to cut costs and time from drug discovery to commercialization. Can AI models, now maturing at mind-boggling speeds, be the solution to the problem? 

Seeing AlphaFold successfully predict protein structures and win the 2024 Nobel Prize in Chemistry sparked interest and enthusiasm in finding other ways to leverage machine learning in biotechnologies. 

In the last three years, there’s been a proliferation of AI startups that help streamline every step of drug development, from discovery and pre-clinical research to clinical trials and final regulatory checks. 

Our series of posts on AI use cases in drug development will examine the current state of AI adoption in drug R&D across three levels: 

• Drug discovery
• Pre-clinical studies
• Clinical research

This is Part 1 of the series, focusing on machine learning applications in drug discovery. 
A significant portion of reflections shared in the article was inspired by the “AI in Pharma: The New Playbook for Drug Research & Development” webinar hosted by Ellen Knapp, Senior Intelligence Analyst in Healthcare & Life Sciences at CB Insights.

The rising momentum of AI in drug development

The interest in AI-powered drug development has exploded in recent years. 

The graph highlights that as recently as 2015, there was barely any enthusiasm about AI-enabled drug R&D. 

In 2022, after ChatGPT showed the far-reaching potential of machine learning models, interest took off.  

In 2024, AI and R&D were mentioned side-by-side 1200 times per month. Clinicians are becoming more receptive to AI-assisted therapeutics, and investors are increasingly channeling capital into biotech startups that embed machine learning into their development pipelines.

Stages of drug development where AI comes in

Drug development typically unfolds across three key stages, each with its complexity and opportunities for AI intervention.

1. Discovery. Identification of targets (proteins, metabolites, RNA/DNA sequences, or microorganisms) and compounds that interact with these molecules. AI has the potential to improve drug discovery through faster candidate improved understanding of underresearched conditions. 

AI assists drug discovery teams by:

• Mining large datasets to uncover underexplored conditions or pathways.
• Generating new candidate molecules using de novo molecular design.
• Prioritizing hits and leads with favorable properties (binding, toxicity, manufacturability).

AI startups in the drug discovery space: Generate: Biomedicines, Relation, Genesis Therapeutics

2. Pre-clinical research. Animal (in vivo) and test tube (in vitro) testing of candidate models, determining their pharmacokinetic and pharmacodynamic profiles: bioavailability, solubility, toxicity, therapeutic index, etc.  

AI can ramp up the speed of pre-clinical assessment by 

• Predicting pharmacokinetic (PK) and pharmacodynamic (PD) profiles.
• Assessing bioavailability, solubility, and toxicity through in silico models.
• Streamlining protocol design and regulatory strategy.

Companies introducing AI to pre-clinical research: Metis Therapeutics, Cellino, CytoReason

3. Clinical research. The drug is tested on humans in a four-phased research pipeline. Each stage of clinical development can be a target for AI enablement, from designing better drug combinations to predicting the outcome of an RCT by combining patient records and real-world data. 

Machine learning is driving progress in clinical research by: 

• Optimizing patient recruitment and site selection.
• Designing better drug combinations or personalized trial arms.
• Predicting outcomes by integrating clinical trial data with real-world evidence.

Biotech innovators in clinical research: FormationBio, HUMA, Deep6 AI

After a candidate drug passes all clinical trials, it is reviewed by the FDA and approved for commercial distribution. 

While the process may appear linear, real-world drug development is often iterative, with setbacks, revisits to earlier stages, and parallel workflows. AI tools are especially valuable in navigating this complexity, helping companies make faster, data-informed decisions throughout the pipeline.

Where traditional drug discovery falls short

Although molecular biology advances helped pharma companies better identify drug targets and design molecules that bind to them, there hasn’t been much progress in scaling the beneficial effects of these drug-like molecules across larger populations. 

In the last five years, drug discovery costs have been rising steadily

McKinsey reports that, in 2014, the pharma industry invested $144 billion in drug development. By 2022, R&D expenses shot up to $251 billion. By 2029, pharma companies are expected to funnel up to $350 billion in developing new therapeutics. 

The rising R&D expenditure has not yielded higher regulatory success. 

FDA reports tracking the rate of drug approvals by the Center for Drug Evaluation and Research show that, after peaking in 2018 at fifty-nine new therapeutics, the rate of drug approvals have been fluctuating. 

Multiple research groups studied the decline of R&D productivity but failed to trace it back to a single root cause. 

The limitations of material chemistry are a considerable barrier for R&D progress. As most structurally unsophisticated drugs have been discovered in the last 70 years, chemists are called upon to design more complex candidates. 

The synthesis and purification of increasingly robust molecules are expensive and time-consuming, hence, drug design teams only yield a limited amount of new compounds per year.  

Depending on the drug in the works, research teams may face specific challenges. For instance, the complexity of experiments that would help identify optimal antigenic sequences and ensure high binding affinities slows down progress in vaccine design.

As the visual shows, challenges span across the pipeline, from long design-make-test cycles in small molecules to suboptimal mRNA translation in vaccines and difficulties in antibody optimization.

Additionally, pharma companies have to find a balance between capitalizing on approved portfolios and identifying novel hits. 

Both institutional and venture investors appear to be skeptical about diversified research portfolios, instead backing companies with a handful of battle-tested therapeutics. 

In this landscape, R&D is on the verge of becoming an afterthought, and productivity plummets. 

How machine learning transforms drug discovery 

AI offers a new source of hope for solving the industry’s persistent challenges. At the moment, tech companies go about doing that through two major solution categories: Discovery engines and software platforms. 

AI-enabled discovery engines

AI-enabled drug discovery engines are platforms that integrate lab data, traditional automated testing, and machine learning into a single workflow. Their primary focus is on discovering new candidate molecules. 

AI-enabled discovery engines are a new frontier in identifying both candidate small molecules (molecules that act on the target following consistent chemical reactions, such as aspirin) and biological drugs (substances of formerly animal origin now derived through recombinant genetics, such as antibodies and vaccines). 

AI software platforms accelerating discovery 

Unlike full-stack discovery engines, point-based platforms enhance specific tasks within the discovery process. For instance, advances in computer vision have led to the surge of platforms that aid in image analysis, which can better analyze cell-and-tissue responses for in vitro assay screenings. 

Currently, hundreds of AI-enabled platforms support the key steps of the discovery pipeline. Below is but a shortlist of rapidly-growing market players in some areas of drug discovery.  

• Target identification platforms: BenchSci, Insitro, Owkin, Euretos, Valo Health, Healx
• Protein engineering platforms: Inceptive, Profluent, DeepCure, Arzeda
• AI molecular design platforms: Atomwise, Iktos, Deep Genomics
• Tools for cell and tissue analysis: Cellarity, Cytel, Deepcell, Phenomic AI

These tools help speed up high-throughput screening, improve model precision, and enable scientists to explore chemical spaces that were previously inaccessible.

The image below summarizes granular use cases for AI software in drug discovery, from image analysis to biological property prediction.

Venture capital investors are open to AI-driven drug discovery

AI-driven drug discovery has become one of the hottest sectors in biotech venture capital, though the scale of funding varies sharply depending on the company’s business model.

Discovery engines, which aim to deliver full therapeutic candidates into clinical trials, require massive investment due to the costs of preclinical and clinical development. In contrast, companies building point-solution AI software, such as molecular design platforms or target identification tools, require far less capital, as they provide modular tools rather than full end-to-end drug development.

This difference shapes market entry strategies: first-time biotech startups often find it easier to break into the market with software platforms rather than full-stack discovery engines.

From 2020 to 2021, funding spiked in step with the global VC market pattern, before tapering off in 2023. But 2024 marked a turning point—investment in AI drug discovery grew 27%, reaching $3.3 billion.

Much of that momentum came from a single player Xaira Therapeutics. This drug-discovery platform combines ML-powered search, multimodal data generation and protein-design technology to interact with proteins that were thought impossible to target.  

Early 2025 data suggests that VC funding in the discovery space is on track to match 2024’s strong performance. Mirroring the previous year’s pattern, Q1’s funding was defined by large investments, with Isomorphic Labs raising $600 million. 

Two trends stand out if one examines the top deals in the AI drug discovery space since 2024. 

Trend #1: The AlphaFold advantage

The largest recent funding rounds all trace back to the creators of AlphaFold, DeepMind’s revolutionary protein-structure prediction system.

Isomorphic Lab secured a record-breaking $600 million Series A in March 2025, and Xaira Therapeutics raised $200 million in 2024. Both companies were founded by AlphaFold’s creators. 

These are not only the biggest deals in AI drug discovery, they’re among the largest biotech VC rounds overall, despite both companies having yet to publish clinical-stage assets.

Trend #2: Surge of interest in biologics

Between 2024 and 2025, three top deals were secured by companies making biologic therapeutics. 

In 2024, for the first time in the history of drug R&D, biologic discovery engines outpaced small-molecule discovery engines in average funding round size. 

There is a clear justification for the trend. Research and regulatory approval of biological drugs are significantly more unpredictable and costly than that of small molecules because biologics tend to produce highly varied responses in the host. 

Thus, there is a pressing need to tap into emerging technologies to drive down the cost and time needed to bring these compounds to market.  

Clinical efficacy remains a challenge for AI-discovered drugs

Clinical viability is one of the most (if not the most) important hurdles to commercializing AI-derived therapeutics.  

To date, no AI-derived therapeutic has been commercialized, though a number of molecules are going through clinical trials. Early results have shown that AI-discovered therapeutics have a high success rate in Phase I, but in the last few years, most have been disappointing in Phase II trials. 

Exscientia, for one, stopped its first AI-discovered oncology drug due to therapeutic index concerns. If AI drug discovery is going to maintain all of the momentum it currently has, AI-based drug discovery needs to yield therapeutics that can hold their own in late-stage trials. 

Nimbus‘s Zasocytinib, currently in a Phase III trial, may be the first AI-assisted drug to do so. The TYK2 inhibitor, aimed to address autoimmune disorders, like psoriatic arthritis, showed high promise in Phase II and was purchased by Takeda for $4 billion, which is a strong vote of confidence in its ability to complete clinical research.

Insilico’s Rentosertib, an AI-aided drug targeting fibrosis, also completed Phase II trials. It is considered one of the more advanced therapeutics to be discovered through the use of machine learning. 

For instance, Insilico Medicine reported that its AI-driven platform accelerated fibrosis drug discovery by reducing the discovery-to-preclinical timeline from the industry average of 4 years down to just 18 months.

The outcomes of these trials will shape the future of AI in drug discovery. If frontrunners like Rentosertib succeed, they will validate the commercial potential of AI-designed drugs and likely trigger a surge in big pharma partnerships and investor confidence. Conversely, high-profile failures could slow the wave of enthusiasm and redirect attention back to more traditional discovery approaches.

Final pulse check on AI-guided drug discovery

Drug discovery is one of the highest-yield areas in machine learning as it would benefit from reducing the time needed to advance along the pipeline, has high volumes of data to train models, and relies on computationally-intensive tasks similar to those that got AlphaFold the 2024 Nobel Prize. 

While AI adoption is still in early stages, drugs discovered with the help of machine learning are entering clinical trials at a much faster pace than their traditionally developed counterparts. Venture capitalists are also bullish on the applications of AI in drug discovery, especially in the biologics sector. 

Progress notwithstanding, it may be too early to celebrate success. At the moment, attention to AI applications is unbalanced as it skews towards data-rich and commercially profitable areas of therapeutics. Less spotlight is placed on therapeutic areas with low data availability or smaller target populations. Ensuring an equal distribution of AI innovation should be an important focus area in the coming years.

Similarly, progress in AI-assisted drug discovery may not yield tangible therapeutic outcomes. It will likely take 2-3 years to gauge the real impact of machine learning techniques on drug design and optimization. 

Yet, there is promise. Pharma leaders looking to de-risk their AI investments should explore Xenoss capabilities in machine learning for pharmaceutics. 

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