Pharma AI: Revolutionising drug development from lab to market

Artificial intelligence has already brought transformative changes to the pharmaceutical industry and its influence continues to grow. With AI, pharmaceutical companies can bring new treatments to market more quickly, design advanced clinical trials and refine how they market and sell therapies — all to create better experiences for patients.

Still, using AI in pharma comes with hurdles. Let's explore where things stand, where they're headed and how pharma AI may evolve in the coming years.

Pharmaceutical AI (pharma AI) uses artificial intelligence (AI) to speed up and simplify drug development. Agentic AI for pharma, generative AI, machine learning, natural language processing, and deep neural networks — which can process and analyse complex sets of data — work together to predict how drugs will behave. These technologies can recognise patterns faster than human analysts and deliver more precise results than traditional methods.

Traditional clinical drug development often spans 9 to 10 years, plus time for preclinical research and regulatory review. By accelerating tasks like drug discovery, data analysis, predictive modeling, and trial management, AI can trim this lengthy timeline by several years, potentially saving hundreds of millions of dollars with each innovative drug.

Core AI technologies in life sciences — including generative AI, deep learning, natural language processing, and advanced data analysis — improve different parts of the drug discovery and development pipeline. Here are some of the ways AI accelerates the process:

• Design novel drug candidates: By learning from large sets of existing data, generative AI can design and optimise new drug structures.
• Analyse molecular structure: AI image recognition processes complex layers of data, making it possible to recognise and analyse molecular structure for potential drug candidates.
• Predict molecular properties: AI analyses molecular images to predict the potential toxicity of drugs and how they will interact with other medications.
• Identify patients for clinical trials: Advanced data analysis matches eligible patients to clinical trials based on demographic details, medical information, and study criteria.
• Simulate biological processes: Predictive AI can simulate biological processes, providing insight into how a drug will affect the body.
• Assess patient feedback: Natural language processing can quickly analyse patient responses, providing insights that keep drug development moving forward.

One recent example is the machine-learning genomics screen technology used by a leading pharmaceutical company to identify and advance a new drug candidate targeting certain types of cancer. The process of identifying the target — the molecule or pathway the drug is meant to interact with — and receiving approval to move on to Investigational New Drug (IND) trials was completed in just 18 months as compared to an industry average of 42 months.

Artificial intelligence is reshaping drug discovery by predicting molecular interactions, spotting promising candidates, and streamlining preclinical testing. Key applications include:

• Identifying drug candidates: AI examines biological and chemical data to help scientists understand how diseases work, predict interactions, find promising drug candidates, and uncover new ways to treat illness.
• Designing and predicting drug properties: Generative AI can create molecules with desired traits and predict ADMET properties (adsorption, distribution, metabolism, excretion, and toxicity) of promising drug candidates.

For example, one AI-focused biotechnology company is working in partnership with a pharmaceutical company to identify new drug therapies for amyotrophic lateral sclerosis (ALS). They used an AI-powered platform to identify promising drug targets and confirm their effects using models that mimic real diseases. Two of the drug targets are currently in development to make significant progress in treatment options for ALS.

Clinical trials are one of the most complex, time-consuming, and costly phases of drug development. But with AI and machine learning, researchers can accelerate many aspects of the trial process, saving companies money and time while maintaining the same or greater levels of accuracy. As AI technologies continue to evolve, they are fundamentally changing how pharmaceutical companies approach trial design, patient recruitment, and trial management to deliver more efficient and successful outcomes.

AI can improve clinical trial design by analysing huge amounts of data quickly and accurately. AI-powered pharma software tools review historical trial data, regulatory submissions, and real-world evidence to:

• Optimise trial protocols
• Predict optimal dosing schedules
• Estimate sample sizes
• Recommend parameters for trial location and structure
• Identify the most relevant endpoints
• Determine ideal trial durations

AI can also simulate trial scenarios and protocols before the trial begins, increasing the likelihood of success. In one case, a clinical trial design company used AI to predict phase 2 clinical trial results with 88% accuracy.

Traditional patient recruitment can take months or even years, with many trials failing to meet enrollment targets. AI can speed up this process by analysing genetic markers, biomarkers, and patient history data to design more targeted inclusion and exclusion criteria. Generative AI and NLP help researchers find ideal participants by analysing medical records, matching candidates to trials, estimating recruitment rates, and predicting dropout risks. This approach helps ensure trials enroll patients most likely to respond to treatment, improving success rates while reducing the number of participants needed to achieve statistical significance.

AI-powered platforms can make patient recruitment and retention more efficient by:

• Screening electronic health records to identify potential candidates who match specific trial criteria
• Predicting patient likelihood to complete the trial based on demographic and behavioural factors
• Optimising site selection by analysing population density, disease prevalence, and historical enrollment patterns
• Personalising outreach strategies to improve patient engagement and reduce dropout rates
• Tailoring recruitment messages based on patient conversation history
• Refining follow-up strategies to keep participants engaged

For example, an AI-focused health tech company has developed a patient prescreening tool that assesses eligibility for clinical trials. The company has been able to reduce prescreening times by more than 50%.

Once trials begin, AI continues to deliver value through enhanced monitoring and management capabilities. Real-time data analysis can detect safety signals earlier, identify protocol deviations, and predict potential issues before they impact trial integrity.

AI has been used successfully in several aspects of trial management:

• Automated adverse event detection through natural language processing of clinical notes
• Clinical trial risk assessment to improve safety and effectiveness
• Adaptive trial design based on emerging data patterns
• Decentralised trials to provide more flexibility for patients and perform trials on a larger scale

Digital twinning is another emerging capability with the potential to transform clinical trial management. A digital twin is a simulation that uses real patient data to create virtual patient populations. These can be used to assess safety and efficacy and may reduce the number of actual patients needed to conduct the trial.

One AI-powered biopharma company used digital twinning to test a new asthma treatment with virtual asthma patients (digital twins). The test produced results similar to the predicted outcomes of an actual Phase 1b trial, confirming that digital twinning could save time and resources during the trial phase.

AI brings a new level of speed and accuracy to pharmacokinetic and pharmacodynamic research. By using predictive analytics and advanced data modelling, AI can project how drugs will be absorbed, metabolised and distributed, as well as how they might interact with other substances. These tools , powered by deep neural networks and machine learning algorithms, can sift through vast datasets to forecast molecular behaviour, absorption rates, bioavailability and much more.

With these capabilities, AI helps researchers anticipate how drugs will work in the body, even before human trials begin. This insight opens the door to personalised therapies shaped by each patient's genetic makeup, health profile and environment.

Key applications include:

• Absorption prediction: Deep neural networks can predict how long it will take drugs to enter the bloodstream and whether certain interactions might slow or speed up that process.
• Distribution modelling: Machine learning and neural networks can combine large datasets to estimate how drugs are distributed throughout the body, predict drug concentrations within various tissues , and model how strongly they bind to proteins.
• Metabolism and elimination analysis: AI models can examine chemical structures and other factors to estimate how quickly a drug breaks down and leaves the body and how stable it remains in various patient groups.
• Dosage and response: Neural networks can help researchers predict the right dose for the best effect, considering patient-specific details that might affect outcomes.
• Therapeutic effects: Machine learning tools can anticipate how drugs will perform across diverse patient populations and in combination with other therapies.
• Drug safety: AI can identify signs of potential adverse effects long before they appear in a patient's chart, making it easier to prevent harmful outcomes.

These capabilities can help bring new drugs to market faster, identify risks earlier, and enable course corrections before issues produce harmful outcomes.

Early successes with generative AI in healthcare have sparked optimism throughout the pharmaceutical industry. This technology is already changing lives. Consider the rapid response to COVID-19 and breakthroughs in anti-cancer drug design, where AI accelerated research and improved our ability to predict outcomes.

Pharma and MedTech companies are finding many ways to tap into AI:

• Clinical trial recruitment: Generative AI simplifies the search for qualified participants. For example, life sciences AI helps researchers raise trial awareness, find participants who meet the criteria, and speed up onboarding.
• Trial site selection: Agentic AI tools act autonomously to achieve specific trial goals using artificial intelligence "agents." For example, a tool like Agentforce can review healthcare and geographic data, work with stakeholders, and suggest trial sites that align with research objectives.
• Advanced therapy management: Personalising treatments for rare diseases often requires deep data analysis. AI helps doctors conduct that analysis, automate administrative tasks, and support personalised medicine efforts.
• Improved patient support: Assistive AI agents help patients receive the right medicine at the right time.

Understanding how AI works in the pharmaceutical industry can help companies improve production by bringing smarter technology to the manufacturing process. These systems help manufacturers spot problems early, run more efficiently, and maintain the high-quality standards that patients depend on.

Here are some of the ways AI can help get new treatments to patients faster:

• Digital twins: These detailed computer models mirror real manufacturing equipment, letting companies test changes and see what might happen without risking actual production.
• Predictive maintenance: AI watches equipment performance data and gives a warning when machines need attention, preventing breakdowns that can shut down production lines.
• Quality control: AI-powered systems identify quality issues faster and more reliably than traditional inspection methods.
• Inventory management: AI helps companies order the right amount of materials at the right time by studying demand patterns and supply chain trends.
• Process optimisation: These systems analyse hundreds of data points to make the production process more consistent and efficient.
• Regulatory compliance: Automated tracking systems document everything that happens during manufacturing, making it easier to meet strict regulatory requirements and demonstrate compliance.

Companies can use AI tools like these to reduce production costs, speed up processes, and improve product quality. In 2023, for example, one pharmaceutical company reported to investors that they use AI to detect anomalies and recommend improvements in the manufacturing process. They hope to see a 10% increase in product yield and a 25% improvement in cycle time.

Healthcare AI tools offer enormous potential for the life sciences and pharma industries, yet their insights only reach as far as the data that fuels them. If that data is incomplete, biased or inaccurate, even the most advanced AI falls short. Recognising these hurdles is the first step toward creating healthier outcomes for everyone.

• Data quality and integrity: Unreliable or sparse data can lead AI astray, risking patient safety and trust. Investing in careful data validation and robust quality checks helps maintain confidence in AI-driven insights.
• Potential bias: Without proper oversight, AI may unintentionally favour certain groups, raising questions about fairness and equal access to care. The life sciences community must address these issues to ensure everyone benefits equally.
• Regulatory compliance: As AI becomes central to drug development, it's raised some ethical concerns around patient consent, equitable trial enrolment and adherence to strict manufacturing guidelines. The concern is that AI could be used to make treatment decisions and whether or not it will maintain compliance with drug manufacturing guidelines . Meeting these standards requires transparency and thoughtful governance.
• Patient privacy: Researchers must work within rules such as the Health Insurance Portability and Accountability Act (HIPAA ) and General Data Protection Regulation (GDPR) to protect sensitive health information and maintain trust.

As pharma organisations weave AI into their research and development efforts, thoughtful oversight becomes essential. A few strategies stand out:

• Address ethical considerations: Build trial designs that respect privacy, informed consent, and fairness, and that remain transparent about when and how AI guides decisions.
• Establish AI governance policies: Set clear guidelines on AI development and use, covering data validation, training practices, and compliance standards. Review FDA principles that address data handling, model fairness, and clarity around regulatory requirements.
• Choose a secure AI platform: Select trusted AI solutions designed to protect patient data, encourage diverse and equitable trial recruitment, and comply with privacy laws. For example, life sciences AI can support trial diversity and uphold patient privacy standards.

As pharma AI applications become more sophisticated and efficient, their economic impact is reshaping the industry. By shortening drug development timelines, enhancing analysis and optimising clinical trials, AI offers cost savings that can exceed $1 billion for an average research investment — transforming not just bottom lines, but also industry-wide practices.

Real-world cost savings

One striking example is Insilico Medicine's AI-generated anti-fibrotic drug candidate , which entered phase 1 clinical trials this year just 30 months after the discovery programme began. Insilico reported that AI's ability to improve efficiency during key stages such as target discovery, drug candidate identification and predictive drug response resulted in an accelerated pre-clinical development timeline and lower costs.

• Workforce transformation: Existing roles may evolve to better use AI as a part of healthcare software. Clinical trial managers and quality control specialists, for instance, may need reskilling to incorporate AI tools into their workflows.
• Job creation: New job categories will emerge to accommodate AI-driven pharma software, including AI and machine learning specialists focused on pharma, biotech data analysts, digital health architects, and other roles.
• Reduced failure rates: Traditional drug development has an 80% to 90% failure rate . AI can significantly improve success rates by enhancing target selection and trial design, reducing development costs, and increasing ROI.
• Market expansion: AI-driven personalised medicine creates opportunities for new market segments and revenue streams, particularly in areas like oncology and rare diseases. Understanding what a healthcare CRM is and implementing it effectively can help drug companies identify healthcare providers (HCPs) in these new segments, determine whether they are likely to be interested in a new drug, and engage with HCPs by providing the right information in the right context.

For example, a platform like Health Cloud engages both patients and providers, integrates care management, and manages patient health data that may be valuable for trial assessments.

The rapid growth of AI in life sciences is unlocking new avenues for personalised care. Combining AI with electronic health records (EHRs) and other medical software can create treatments tailored to each patient's unique needs. AI agents and copilots can gather, analyse and refine data, improving accuracy and leading to better patient outcomes.

We're also moving toward more flexible clinical trials. AI already supports decentralised trial models , making it easier to recruit, screen and monitor patients remotely. Some experts predict a future where trials rely more on existing data, reducing the number of participants required and opening the door to faster, more cost-effective studies.

Innovations like these call for strong protocols that respect compliance, security and privacy. Trusted AI solutions such as Salesforce's Einstein Trust Layer let healthcare organisations harness AI without risking patient confidentiality.

AI has shifted from a distant possibility to a key resource in drug development. While data integrity, regulatory compliance and patient privacy remain concerns, AI's ability to cut costs and speed drug discovery makes it a cornerstone of future medicine.

Fully realising AI's promise means choosing solutions that respect ethics and follow regulations, all while focusing on patient needs. Starting small — integrating AI into administrative tasks or patient support — can help teams gain confidence and expertise. Over time, a stronger AI foundation can pave the way for new ideas and fewer barriers to safer, faster treatments.