Carlton | UHY Collaboration: AI-Powered Drug Development

We’re proud to share this insightful Carlton Corner article, created in collaboration with the team at UHY-US . UHY brings deep expertise in program management, digital transformation, and technical consulting across life sciences and other high-impact industries. Together, we’re exploring how AI is not only changing the future of pharmaceuticals but already reshaping the way therapies are discovered, developed, and delivered.

As AI continues to accelerate scientific innovation, this piece highlights where the technology is making the biggest impact, how it’s redefining talent needs, and why it matters to both clients and consultants.

“AI is transforming pharmaceutical development by accelerating drug discovery, optimizing clinical trials and personalizing medicine. AI is making drug development faster, cheaper and more effective, and Carlton is here to support our clients and consultants as these technologies and tools evolve and proliferate.” — Robert (Bobby) Brown , CEO/CLO, Carlton National Resources

“AI is definitely here in full force and truly shaking things up! We’re seeing pharma clients rethink their teams as AI tools become more embedded in the workflow. Roles focused on data science, automation, and AI integration are in high demand and it's creating exciting new opportunities for both clients and consultants.” — Allie Johnson , Sr. Business Development Manager, Life Sciences

“I’ve seen firsthand how AI is transforming the biopharma space speeding up everything from discovery to clinical trials. It’s exciting to know that the work we support helps bring critical treatments to patients faster, when every moment counts.” — Benjamin Dick , Business Development Manager, Life Sciences

About the Author

Tim Shaw , Senior Manager at UHY Consulting, led the research and writing of this article. With extensive experience in program management, technical solutions, and business process optimization, Tim specializes in helping organizations align innovation with operational strategy. His background spans product architecture, risk control, and cross-functional leadership across high-performing teams.

He holds certifications including PMP, CISA, CRISC, CISM, CMS, and Practical Gen AI for Project Managers. Tim earned his MBA from Capella University and a BS in Business Administration & Operations Management from the University of Southern Illinois.

Special thanks to Cory McNeley , Managing Director at UHY, and Bryan Besco , Business Development Manager at UHY, for their efforts and collaboration. We look forward to many more impactful partnerships ahead.

Accelerating Drug Discovery: How AI is Transforming Pharmaceutical Development

Written by: Tim Shaw, Senior Manager, UHY Consulting

The pharmaceutical industry has long been at the forefront of scientific innovation, yet drug development remains slow, expensive, and complex. Traditionally, bringing a new drug to market can take over a decade and cost billions of dollars. With today’s technology, there is hope that artificial intelligence (AI) is rapidly changing how it is viewed by streamlining workflows, reducing inefficiencies, and accelerating the discovery of new therapeutics. As AI becomes more deeply integrated into pharmaceutical research, it has the potential to revolutionize how new drugs are discovered, tested, and approved.

The AI-Driven Shift in Drug Discovery

AI has been around for a while and is no longer a futuristic concept in biotech; it is actively reshaping how major pharmaceutical companies and startups operate. Many leading organizations across the industry are investing heavily in AI-driven solutions to enhance drug discovery. These companies leverage AI to analyze vast biological datasets, predict drug-target interactions, and identify promising compounds faster and more accurately than traditional methods.

One example of how AI is already influencing decision-making in the pharmaceutical space involves a major global pharma company partnering with an AI innovation lab to integrate AI into drug development. Another prominent organization has collaborated with AI-focused firms to optimize drug discovery in neuroscience and other therapeutic areas. Across the board, more companies are investing in AI technologies to reduce costs, shorten timelines, and improve the likelihood of success in drug development.

Drug Target Identification

One of the most challenging and time-consuming aspects of drug development is identifying viable drug targets, but with AI-driven machine learning models companies can analyze complex biological pathways and pinpoint new therapeutic targets with remarkable precision.

One tech company specializing in AI has employed advanced models to predict drug-target interactions, allowing researchers to identify promising candidates more efficiently. Another firm used deep learning techniques to sift through biomedical literature and genomic data, uncovering potential drug candidates that may have been overlooked through conventional research methods. In 2020, one such firm successfully repurposed an existing drug as a potential treatment for COVID-19 in record time, demonstrating AI's power in accelerating drug repurposing efforts.

Looking at a 2019 case study from an academic research clinic, researchers used an AI model to analyze extensive chemical datasets, leading to the discovery of a novel antibiotic capable of combating multiple drug-resistant bacteria. Traditional methods for antibiotic discovery often take years and require screening thousands of compounds, but AI identified this compound in a fraction of the time. Breakthroughs like this validate AI’s ability to accelerate drug discovery while addressing urgent global health challenges, such as antibiotic resistance.

Drug Design and Molecule Synthesis

Beyond target discovery, AI is critical in designing and synthesizing new drug compounds. By employing generative adversarial networks (GANs) and reinforcement learning, AI can rapidly generate molecular structures designed to specific biological criteria.

A leading biopharma company recently developed an AI-designed drug candidate for a rare lung disease in just 18 months, a process that typically takes several years. With AI tools, the system generated a potential therapeutic molecule in just 46 days, demonstrating the record speed at which AI can assist with designing new compounds. This shows AI's ability to optimize molecular design while ensuring efficiency and safety, and reducing the time and costs associated with traditional drug development.

Another innovative research group uses deep learning algorithms to screen millions of molecular compounds in silico, reducing the time required to identify viable drug candidates. These AI-powered approaches enable researchers to prioritize the most promising compounds for further laboratory validation and clinical testing, ultimately accelerating development.

Clinical Trials and Patient Recruitment and AI’s Role

Once a promising drug candidate has been identified, it must undergo extensive pre-clinical and clinical trials. This phase is notorious for high failure rates and prolonged timelines, but AI is helping to mitigate these challenges in several ways.

Utilizing AI-driven automation to conduct large-scale cell imaging experiments, offering a view into how potential drugs interact with biological systems. This accelerates preclinical validation by quickly identifying compounds with the highest likelihood of success while filtering out less promising candidates.

Traditional recruitment methods can be slow and inefficient, often leading to delays, but with AI, it also enhances patient recruitment and monitoring in clinical trials.  AI-powered platforms like Deep 6 AI analyze electronic health records and clinical trial databases to match eligible patients with trials more effectively. AI-driven remote monitoring tools also improve trial adherence and data collection, making the clinical testing more efficient and cost-effective.

Challenges and Ethical Considerations

Despite the potential of AI-driven drug discovery, the industry still faces several challenges. One of the primary obstacles is data quality and accessibility. AI models require large, diverse, well-annotated datasets to make accurate predictions. However, much of the pharmaceutical industry’s data remains proprietary or siloed, which makes it challenging to train robust AI models without extensive common data. Establishing data-sharing frameworks while ensuring accordance with regulations like GDPR and HIPAA is crucial for advancing AI adoption.

Additionally, regulatory bodies such as the FDA are working to develop guidelines for evaluating AI-generated drug candidates. Ensuring that AI-driven discoveries meet stringent safety and efficacy standards is essential to gaining public and regulatory trust.

Ethics will also have to play a role in AI-driven research. Algorithmic biases, lack of transparency, and explainability issues must be addressed to ensure that AI-generated understandings are reliable and equitable. Researchers and regulatory agencies must work together to create AI models that are interpretable and free from biases that could affect certain populations.

The Future of AI in Drug Development

The integration of AI into drug discovery and development is already yielding promising results, and its impact is expected to grow exponentially in the coming years. Several pioneering AI firms are leading the way in demonstrating AI’s ability to accelerate research, reduce costs, and improve the likelihood of success in drug development.

As AI technologies advance, we can expect even more sophisticated models capable of predicting drug efficacy and safety with greater accuracy. Collaborations between pharmaceutical companies, AI startups, and regulatory bodies will be key to unlocking AI’s full potential in drug discovery.

Ultimately, AI is not just making drug development faster, it is reshaping the entire pharmaceutical landscape. The coming years with AI, innovations could be groundbreaking and redefine how the overall industry approaches medicine, offering new hope for the rapid development of life-saving treatments and cures.