Melvine's AI Analysis # 48 - 🚀 AI Revolution at Merck: Transforming Pharma from Lab to Patient
Melvine Manchau
Senior Strategy & Technology Executive | AI & Digital Transformation Leader | Former Salesforce Director | Driving Growth & Innovation in Financial Services | C-Suite Advisor | Product & Program Leadership
April 15, 2025
Merck & Co., a global leader in pharmaceuticals and healthcare, has embraced artificial intelligence (AI) and generative AI (Gen AI) to drive innovation, improve efficiency, and develop new therapies. As the healthcare and pharmaceutical industries evolve, AI is pivotal in transforming drug discovery, manufacturing, patient care, and operational processes. This article delves into Merck's AI initiatives, industry trends, competitor strategies, the expected impact of AI, and the associated challenges and regulatory landscape.
AI and Generative AI at Merck
Merck has significantly invested in AI and generative AI to enhance its research and operational capabilities. From drug discovery to manufacturing and patient engagement, AI is a cornerstone of Merck's strategy to remain competitive in the rapidly evolving pharmaceutical industry.
Merck & Co. is evaluating a generative AI platform. As an early user of Variational AI’s technology, Big Pharma will assess the platform's ability to generate novel small molecules that match its target product profiles (TPPs).
Variational Enki technology is a drug discovery spin on an idea that will be familiar to anyone who has used a generative AI platform. Like how AI software like DALL-E and Midjourney can create images based on text prompts, Enki generates small molecules in response to TPPs. The user picks targets they want to hit and those they want to miss, and chooses other attributes. Enki then generates molecules that meet the TPP.
If Variational is right, only a series of prompts about the TPP stands between users and “novel, selective and synthesizable lead-like structures.” The startup trained Enki, using experimental data, to develop molecules based on TPPs and, therefore, help researchers explore a wider swath of chemical space.
Merck found the pitch sufficiently compelling to become an early-access user. Like its peers, the company is exploring ways teams across its organization could use AI. At an investor event early this month, Robert Davis, Big Pharma's CEO, discussed how the company thinks about AI and other technology.
Key Use Cases of AI at Merck
Drug Discovery and Development: Merck uses AI to accelerate drug discovery by analyzing large datasets to identify potential drug candidates. Algorithms help predict molecular interactions, optimize compound designs, and assess the safety and efficacy of drug candidates. Generative AI, in particular, is used to design novel molecules and chemical compounds, significantly reducing the time and cost of research. Merck’s AI-driven drug discovery efforts focus on addressing a broad range of diseases, particularly emphasizing high-priority therapeutic areas where innovation is urgently needed. Using AI and generative AI, Merck accelerates the identification of potential drug candidates, optimizes drug design, and improves the efficiency of research and development. Below is an overview of the specific diseases and therapeutic areas targeted by Merck’s AI initiatives:
1. Oncology (Cancer)
Cancer research is one of Merck’s most prominent focus areas, and AI is critical in advancing oncology drug discovery. Key AI applications include:
Immuno-Oncology: AI helps identify novel immune system targets for developing therapies such as checkpoint inhibitors. For example, Merck’s blockbuster immunotherapy, Keytruda (pembrolizumab), is supported by AI-driven research to expand its applications across various cancers.
Molecular Target Discovery: AI models analyze genomic and proteomic data to identify biomarkers associated with different cancer types, enabling the development of precision oncology treatments.
Combination Therapy Design: AI predicts synergistic effects between drugs, facilitating the development of combination therapies for aggressive cancers.
2. Infectious Diseases
AI is instrumental in combating infectious diseases, including global health threats. Applications include:
Antibiotic Resistance: Merck uses AI to identify new antibiotics to overcome resistance to existing treatments, addressing a growing public health crisis.
Viral Diseases: AI has been used in developing antiviral therapies, including drugs and vaccines for diseases caused by viruses such as HIV, hepatitis, and influenza.
3. Cardiovascular and Metabolic Diseases
Merck employs AI to target complex cardiovascular and metabolic conditions such as:
Heart Failure and Hypertension: AI models analyze patient data to identify druggable targets and predict the efficacy of potential therapies for heart disease.
Diabetes: Generative AI is used to design novel compounds to manage blood glucose levels and address the complications of diabetes, such as neuropathy and kidney disease.
4. Neurological and Psychiatric Disorders
AI-driven research is helping Merck tackle diseases of the central nervous system, where traditional drug discovery has been particularly challenging.
Alzheimer’s Disease: AI is used to identify biomarkers and molecular targets associated with Alzheimer’s progression. This includes analyzing genetic and multi-omic data to develop disease-modifying therapies.
Parkinson’s Disease: AI models simulate how potential drugs interact with neural targets, accelerating the identification of therapies for motor and cognitive symptoms.
Depression and Anxiety Disorders: AI supports the identification of novel pathways and compounds for psychiatric conditions, aiming to improve treatment efficacy and reduce side effects.
5. Autoimmune and Inflammatory Diseases
Merck uses AI to understand the complex mechanisms of autoimmune diseases better and design targeted therapies, particularly for:
Rheumatoid Arthritis and Psoriasis: AI analyzes immune system pathways to identify therapeutic targets that modulate inflammation and autoimmune responses.
Inflammatory Bowel Disease (IBD): AI supports Crohn’s disease and ulcerative colitis research by identifying biomarkers and predicting patient responses to experimental treatments.
6. Rare and Underserved Diseases
Merck leverages AI to address diseases with limited treatment options, including:
Rare Genetic Disorders: AI analyzes genetic data to identify mutations responsible for rare diseases, facilitating the development of gene-targeted therapies.
Neglected Tropical Diseases (NTDs): AI helps discover affordable therapies for diseases like malaria, schistosomiasis, and leishmaniasis, particularly in low-income regions.
7. Vaccine Development
AI has revolutionized Merck’s vaccine research by optimizing the design and testing of vaccines against infectious diseases. Examples include:
HPV (Human Papillomavirus) Vaccines: AI aids in monitoring vaccine effectiveness and identifying new viral strains for inclusion in future formulations.
Pneumococcal Vaccines: AI supports the development of next-generation vaccines that provide broader protection against pneumococcal infections.
Emerging Pathogens: AI helps predict the evolution of pathogens, enabling preemptive vaccine development for potential future outbreaks.
8. Chronic Respiratory Diseases
AI-driven drug discovery at Merck targets respiratory conditions such as:
Asthma and Chronic Obstructive Pulmonary Disease (COPD): AI models analyze patient data to identify novel biologics that can improve airway inflammation and lung function.
9. Antibody and Biologics Development
Merck employs AI to optimize monoclonal antibodies and biologic therapies, which are used to treat a range of diseases, including:
Autoimmune disorders
Cancers
Infectious diseases
AI accelerates antibody design and helps predict their stability, binding affinity, and effectiveness.
Merck uses Amazon Web Services (AWS) to solve a common problem in the pharmaceutical industry—the occurrence of false rejects. Merck aims to get life-saving drugs into customers’ hands faster and safely. It ingests real-time data from various manufacturing processes and inspection machines and then contextualizes and harmonizes it. Merck uses AWS Glue and Amazon Kinesis to ingest, transform, and contextualize near real-time data, run analytics on that data, and load the harmonized data into Amazon Redshift, which is then used by its analytics dashboards, including Amazon QuickSight. Merck’s artificial intelligence and machine learning (AI/ML) platform is built on Amazon SageMaker and uses AWS DataSync to ingest defect image data from inspection machines across sites. Merck uses generative AI approaches and generative models such as Generative Adversarial Networks (GANs) and Variational Autoencoders to develop synthetic defect image data for complex defects where it has limited training data.
Through the use of AWS services, Merck has improved product availability and yield, enabled rapid response to investigations, and enabled rapid Root Cause Analysis and corrective actions, all while providing significant time and cost savings.
Impact of AI on Disease Targeting
By focusing on these diseases, Merck’s AI initiatives aim to:
Accelerate Time-to-Market: Reduce the time required to identify, test, and develop treatments.
Enhance Precision Medicine: Develop therapies tailored to individual patients based on genetic, clinical, and environmental data.
Address Unmet Needs: Focus on diseases with limited or no existing treatment options, improving healthcare outcomes globally.
Lower R&D Costs: Use AI and generative AI to cut costs associated with traditional trial-and-error drug discovery methods.
Overall, Merck’s AI-driven drug discovery targets a diverse range of diseases, aligning with the company’s mission to improve and save lives worldwide. By leveraging AI, Merck is advancing its pipeline of innovative therapies and setting new standards for pharmaceutical research and development.
Clinical Trials Optimization AI tools are deployed to streamline clinical trials by identifying suitable patient populations, optimizing trial designs, and predicting outcomes. Machine learning models analyze patient data to ensure trials are conducted efficiently and accurately.
Manufacturing and Supply Chain AI is leveraged to optimize Merck's manufacturing processes, ensuring high-quality production and reducing waste. Predictive analytics helps prevent equipment failures, while AI-powered supply chain models anticipate demand and manage inventories efficiently.
Personalized Medicine: Merck is exploring the potential of AI in tailoring treatments to individual patient needs. By analyzing genetic data and patient histories, AI systems can recommend personalized therapies, improving treatment outcomes.
Real-World Evidence and Pharmacovigilance AI help analyze real-world data from electronic health records (EHRs), insurance claims, and wearable devices to monitor drug performance and detect adverse effects. This supports post-market surveillance and regulatory compliance.
Generative AI for Knowledge Management: Generative AI tools synthesize insights from vast internal and external datasets. These tools assist researchers by summarizing scientific literature, generating hypotheses, and even drafting scientific publications.
Merck’s AI and Gen AI Initiatives
Merck has launched several initiatives to harness the power of AI and generative AI:
Collaboration with AI Companies Merck collaborates with leading AI companies like NVIDIA, Microsoft, and academic institutions to integrate AI into its operations. These partnerships focus on building advanced computational platforms and algorithms for drug discovery and development.
In-House AI Centers of Excellence: Merck has established internal AI innovation hubs to develop proprietary solutions tailored to its specific needs. These centers focus on embedding AI across the organization to drive data-driven decision-making.
AI Talent Acquisition Merck invests heavily in hiring AI experts and data scientists. It also partners with universities and research institutions to foster innovation and skill development.
AI in Vaccine Development AI played a crucial role in developing vaccines, including Merck’s COVID-19 vaccine efforts. Machine learning algorithms were used to predict immune responses and optimize vaccine formulations.
Digital Health Solutions Merck is incorporating AI into digital health platforms, enabling remote monitoring, early disease detection, and patient engagement.
BCG Announces GenAI Collaboration With Merck
Boston Consulting Group announced it has entered a strategic collaboration with Merck, known as MSD outside of the US and Canada. The partnership will leverage artificial intelligence and generative artificial intelligence (AI and GenAI) to enable the discovery and assessment of novel drug targets.
As part of the collaboration, BCG X (BCG's tech build and design unit) will work with Merck to develop algorithms that use AI to mine robust omics data. The goal is to better understand causal relationships between biomarkers and disease biology to facilitate drug target discovery. The collaboration will initially focus on disease networks and pathways for chronic and degenerative diseases.
“We look forward to working with Merck to explore the possibilities of leveraging AI and GenAI to identify novel targets and accelerate the drug discovery process,” said Sylvain Duranton, global leader of BCG X.
Merck's Collaboration with NVIDIA
Merck has partnered with NVIDIA, a global leader in AI computing, to enhance its drug discovery and research capabilities. Key aspects of this collaboration include:
AI-Powered Drug Discovery Platforms
Computational Chemistry and Molecular Modeling
Collaboration on Federated Learning
Digital Twin Technology
Merck's Collaboration with Microsoft
Merck’s partnership with Microsoft focuses on utilizing cloud computing, AI, and data analytics to accelerate innovation across its value chain. Notable highlights of this collaboration include:
Azure Cloud for Data Processing and AI
AI for Precision Medicine
Project Silica for Data Storage
Collaboration on Real-World Evidence
Digital Transformation of Operations
AI-Powered Knowledge Management
Significance of These Collaborations
Accelerated Drug Discovery. By leveraging NVIDIA’s AI computing power and Microsoft’s cloud infrastructure, Merck can identify and optimize drug candidates faster, reducing the time and cost of bringing new therapies to market.
Enhanced Computational Capabilities The partnerships enable Merck to tackle computationally intensive tasks, such as molecular modeling, with unprecedented efficiency and accuracy.
Data-Driven Decision-Making Microsoft’s cloud and AI solutions provide Merck with actionable insights from large and diverse datasets, enhancing decision-making across research, clinical trials, and operations.
Scalable and Secure Infrastructure Microsoft Azure and NVIDIA’s AI platforms offer scalable and secure solutions, ensuring that Merck’s sensitive research data is protected while allowing for global collaboration.
These collaborations exemplify Merck’s commitment to leveraging the best technological resources to drive innovation and maintain its competitive edge in the pharmaceutical industry. By working with NVIDIA and Microsoft, Merck is building a robust AI-enabled ecosystem that has the potential to transform healthcare and improve patient outcomes.
Merck also launched its AIDDISON™ drug discovery software, the first software-as-a-service platform that bridges the gap between virtual molecule design and real-world manufacturability through SynthiaTM retrosynthesis software application programming interface (API) integration.
Merck Launches AIDDISON™, the first solution to integrate drug discovery and synthesis. Pictured here: AIDDISON™ drug discovery software interface
It combines generative AI, machine learning, and computer-aided drug design to speed up drug development. Trained on more than two decades of experimentally validated datasets from pharmaceutical R&D, AIDDISON™ software identifies compounds from over 60 billion possibilities that have key properties of a successful drug, such as nontoxicity, solubility, and stability in the body. The platform then proposes ways to synthesize these drugs best.
Trends in the Pharmaceutical Industry Regarding AI
The pharmaceutical industry is witnessing a rapid adoption of AI and generative AI, driven by the need for efficiency and innovation. Key trends include:
AI-Driven Drug Discovery AI revolutionizes drug discovery by enabling researchers to process enormous datasets, predict molecular structures, and simulate clinical trials.
Precision Medicine AI-powered tools are helping companies develop treatments tailored to individual patients based on genetic and phenotypic data.
Digital Transformation Pharmaceutical companies are adopting AI for automation, predictive analytics, and operational efficiency across the value chain.
Collaborations and Acquisitions: Large pharmaceutical companies are partnering with tech firms and acquiring AI startups to gain a competitive edge.
Regulatory Oversight As AI adoption grows, regulatory bodies are developing frameworks to ensure patient safety, data privacy, and ethical AI use.
AI Initiatives by Merck’s Competitors
Merck’s competitors, such as Pfizer, Johnson & Johnson, and Roche, are also investing heavily in AI:
Pfizer uses AI to streamline its R&D process, particularly in vaccine development. The company partnered with IBM Watson to accelerate drug discovery and optimize clinical trials.
Johnson & Johnson employs AI in surgical technologies, using robotic systems powered by AI to improve precision. They also leverage AI for drug discovery and supply chain management.
Roche integrates AI into diagnostics and personalized healthcare. Flatiron Health's subsidiary uses AI to analyze oncology data and improve cancer care.
Novartis has launched an AI innovation lab and uses AI to design clinical trials, discover biomarkers, and optimize manufacturing.
Expected Impact of AI in Pharmaceuticals
The adoption of AI in pharmaceuticals, including at Merck, is expected to deliver transformative benefits:
Faster Drug Discovery AI reduces the time and cost of drug development by analyzing vast datasets and predicting outcomes.
Improved Patient Outcomes AI-driven personalized medicine enables tailored treatments, enhancing patient care and satisfaction.
Operational Efficiency Automation and predictive analytics optimize manufacturing and supply chain processes, reducing costs and waste.
Regulatory Compliance AI tools streamline data collection and analysis, ensuring compliance with regulatory requirements.
Risks and Challenges of AI in Pharmaceuticals
Despite its potential, AI adoption in the pharmaceutical industry faces several challenges:
Data Quality and Availability AI models require large amounts of high-quality data, which can be difficult to obtain due to privacy concerns and fragmented healthcare systems.
Ethical and Bias Issues AI algorithms may perpetuate biases present in training data, leading to inequitable outcomes.
Regulatory Uncertainty The regulatory landscape for AI in healthcare is still evolving, creating uncertainty for companies.
Integration with Legacy Systems Integrating AI into existing systems and workflows can be complex and resource-intensive.
Cybersecurity Risks The use of AI in handling sensitive patient and proprietary data increases the risk of cyberattacks.
Regulatory Environment for AI in Pharmaceuticals
Regulatory bodies worldwide are working to establish guidelines for AI use in healthcare and pharmaceuticals:
FDA (U.S.) The FDA has issued guidelines for AI and machine learning in medical devices and is exploring frameworks for AI in drug development.
EMA (Europe) The European Medicines Agency (EMA) emphasizes transparency and data integrity in AI applications, particularly in clinical trials.
Data Privacy Regulations Laws like GDPR in Europe and HIPAA in the U.S. impose strict requirements on data handling, impacting AI implementation.
AI Ethics Guidelines Global initiatives, such as the OECD AI Principles, advocate for ethical AI use, emphasizing fairness, accountability, and transparency.
Conclusion
Merck’s adoption of AI and generative AI demonstrates the transformative potential of these technologies in the pharmaceutical industry. By leveraging AI for drug discovery, clinical trials, and personalized medicine, Merck aims to accelerate innovation and improve patient outcomes. However, the company must navigate challenges such as data quality, regulatory uncertainty, and ethical concerns to realize AI’s potential fully.
As competitors also invest in AI, the pharmaceutical industry is poised for a new era of innovation. Companies like Merck can harness AI to revolutionize healthcare and deliver life-saving therapies to patients worldwide by addressing the associated risks and adhering to regulatory standards.
By Melvine Manchau, Digital & Business Strategy at Broadwalk and Tamarly
https://melvinmanchau.medium.com/
