Market Introduction

The global AI-driven drug discovery platforms market was valued at USD 1.85 billion in 2024 and grew at a CAGR of 26% from 2025 to 2034. The market is expected to reach USD 18.65 billion by 2034. The rapid technological advancements will drive the growth of the global AI-driven drug discovery platforms market.

Many important elements within the healthcare industry are encouraging drug developers to adopt AI systems to shorten, simplify and control the costs of developing new drugs. A major reason for all this is how companies are now expected to speed up and lower the costs of drug production. Bringing a drug to market by traditional methods can take more than ten years and a vast budget. With AI, the periods used for target identification, molecule screening and lead development are cut, allowing companies to save effort and expenditure. AI approaches are essential because diseases like cancer, disorders affecting the brain and infectious diseases are becoming more complex. With AI, scientists can look at large biological datasets and bring to light brand-new drug targets which makes the technology key in handling these issues. The rising use of big data and improved technology now make it ideal for AI to be adopted. The processing of massive biological, chemical and clinical data is possible today with AI. This means insights can now be discovered that could not be accessed in the past. As a result, there is a growing emphasis on using individual genetic and molecular makeups to choose treatments. AI-driven solutions take patient-specific information into account to make therapies more suitable and effective for each patient. In addition, researchers, academic institutions, pharmaceutical companies and biotech companies are cooperating closely and rapidly advancing and innovating to increase the adoption of AI. Regulatory groups are also starting to accept and endorse AI-driven ways of working, motivating players in the industry to spend more on these technologies.

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Recent Development

• The China-based company Viva Biotech recently organized the event “Enchantment of Drug Discovery” to launch its AI-Driven Drug Discovery (AIDD) platform, introducing this advanced and comprehensive platform to industry professionals. Viva Biotech officially unveils its AI-Driven Drug Discovery platform for the first time, which is powered by three core modules akin to the three hallows. The V-Scepter is akin to the elder wand, as it provides the computational modeling with basic principles.  The biological system's underlying mechanisms are revealed by V-Orb. And V-Mantle serves as an unseen garment crafted at Viva, aimed at delving into the concealed realm of drug discovery through generative AI models.

Market Dynamics

Drivers

The rapidly rising need for developing drugs, medications, therapies and other treatments globally – The need for AI-driven platforms in drug discovery mainly arises because pharmaceutical and biotech companies rely on better efficiency and new innovations for their R&D. The desire to decrease both the length and expense of developing new drugs is a major motivation. Adding AI to the process means companies can more quickly spot targets, check compounds and make better leads which reduces research costs and shortens the product timeline. Additionally, AI’s ability to predict allows scientists to spot out good drug prospects early in the process which means there are fewer issues in clinical trials and the final programs are more likely to succeed. Many life science organizations aim to come up with new ideas to tackle difficult problems like cancer and various disorders affecting the brain and genes. For these important health fields, it takes powerful technology to analyse big biological data and AI tools fulfil that requirement. At the same time, companies are devoting more attention to personalized medicine which calls for tailored therapy choices based on a patient’s unique gene and molecular analysis. AI helps organizations gather and process patient-specific information which allows them to develop therapies that are more likely to succeed. Working with other organizations forms a major part of the company’s activities. There is an increasing trend for pharmaceutical companies to unite with AI vendors, universities and biotech businesses to tap into the best AI technology and share what they have.

Restraints

Capital intensive nature of AI-drive drug discovery platforms – An important challenge is the high cost needed to develop or acquire, deploy and maintain AI technology systems. Platforms of this kind require lots of money, special skills and strong infrastructure, making them tough for small or mid-sized businesses to manage. Also, since bringing AI systems into current research setups and past databases involves much technical work and more time which also hinders its adoption. Resistances toward change, sticking with convention is common for some researchers and decision-makers and they are not always convinced about what AI can provide which also hampers the market’s growth. the lack of qualified professionals also limits the market’s growth. Reliable results from AI models come from having a large amount of well-prepared and standard data. There are often difficulties for companies in getting, ordering and keeping these kinds of data. Imperfect data can affect the accuracy of AI and break people’s trust in their predictions. When there is little clarity about the performance of AI, many companies might be reluctant to depend entirely on these platforms.

Opportunities

Technological advancements, rising investments and regulatory support – rapid progress in big data systems and computer chips is one of the biggest drivers of the market. New AI techniques can handle enormous collections of biological, chemical and clinical data because there are now powerful computers and plenty of data. With these advancements, AI is more likely to be used in finding and developing new drugs. Since complex and chronic diseases such as cancer, diseases of the nervous system and infections are becoming more widespread worldwide, finding new treatments is increasingly necessary. These problems encourage the industry to look at AI-based approaches to discover new solutions faster and meet medical challenges that remain unaddressed. Many regulators are now adjusting their rules to help AI be used in pharmaceutical development. With more helpful guidelines from relevant regulatory authorities like the FDA, companies can rely on clearer paths to getting AI approved for use. Also, more partnerships and groups in the industry contribute knowledge and data to AI, supporting fresh solutions and decreasing any roadblocks to adoption.

Segment Analysis

Regional segmentation analysis

The regions analyzed for the market include North America, Europe, South America, Asia Pacific, the Middle East, and Africa. North America emerged as the most significant global AI-driven drug discovery platforms market, with a 41% market revenue share in 2024.

This market is largely dominated by North America because it has well-developed healthcare sector, advanced technology and substantial capital and effort that is invested into research and development. The area gains from several top pharmaceutical and biotech companies as well as from AI startups working on advanced drug discovery methods. The generous financial support from the public and private players significantly augments the market’s growth. There are strong and evolving safety rules in the region, headed by the FDA, to help AI technology gain clearance and approval. Because of this regulation, companies no longer worry so much and are more willing to use AI-based systems. Highly skilled experts in AI, machine learning and biomedical fields are available in North America which helps in developing AI tools for drug discovery a much easier and speedier process. Besides, AI’s impressive data-handling and prediction skills are useful for the North American market’s shift towards personalized treatments. Because of these advantages—strong innovation, financial support, friendly rules, good talent and healthcare focus—North America is at the forefront of the AI-driven drug discovery market worldwide.

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• Elix, Inc., a company focused on AI drug discovery and dedicated to the mission of “Rethinking Drug Discovery”, has announced a collaboration with PRISM BioLab, Co. Ltd. for drug discovery, effective April 1, 2025.  By merging Elix's state-of-the-art AI drug discovery platform with PRISM BioLab's exclusive peptide mimetic technology for regulating protein-protein interactions, this collaboration will expedite research on these difficult molecular targets.  This collaboration will see the integration of Elix Discovery, Elix's AI drug discovery platform, with PRISM BioLab's proprietary small molecule peptide-mimetic PepMetics technology. With this collaboration, the realm of AI-driven exploration will encompass small molecules that were not considered before. This will facilitate the effective identification of promising compounds for targets that are traditionally challenging and expedite the development of breakthrough drug candidates.

Component Segment Analysis

The component segment is divided into software, hardware and services. The software segment dominated the market, with a market share of around 40% in 2024. The main reason behind software’s dominance in the market is its support in many stages in drug discovery. AI applications in the drug development area rely on software solutions to supply the main algorithms, tools for data processing and computational models for fast and precise selection of potential drugs. Software platforms give pharmacies scalable, repeatable and customizable tools they can build right into their research routines making them highly sought after components in the market. A major reason for software’s success is that it can process and study large and complicated biological data far more productively than traditional methods which saves both money and time. All in all, the AI-driven drug discovery market relies heavily on software because it scales well, is efficient, can handle large data and drives both innovation and productivity in modern drug development.

• Rubedo Life Sciences, Inc. (Rubedo), a biotech company in the clinical stage that is driven by AI and aims to find and swiftly develop selective cellular rejuvenation therapies aimed at aging cells, has announced that opensource codes incorporated into its proprietary drug discovery platform, ALEMBIC, facilitated the identification of senescent neurons in a new study showing that these neurons are responsible for chronic pain resulting from injury and aging.

Application Segment Analysis

The application segment is divided into target identification, drug design & optimization, preclinical testing and clinical trials support. The target identification segment dominated the market, with a market share of around 35% in 2024. Recognizing and confirming which proteins, genes or pathways play a role in disease is necessary for creating good drugs. With AI, it becomes possible to quickly and accurately scan much more information about biological, chemical and clinical data than standard methods can handle. AI is applied in platforms for target identification to find important information hidden in big datasets, including genomics, proteomics and transcriptomics. Researchers can detect targets and biomarkers connected to particular diseases which helps design medicines with improved results and lower risks. Companies in the pharmaceutical sector emphasize this step, since it supports every following drug development activity and is why they make up most of the market. The main reason for this is that target identification plays a crucial part in drug development, is greatly influenced by AI and is key to the development of precision medicine initiatives.

Technology Segment Analysis

The technology segment is divided into machine learning, deep learning and natural language processing (NLP)). The machine learning segment dominated the market, with a market share of around 39% in 2024. The reason ML is most popular in the drug discovery platforms market is its flexibility, strong performance and versatile use throughout the drug development process. Much like other AI technologies, ML algorithms are great at handling huge amounts of data, making patterns that are hard to notice with normal statistics easier to find. Another advantage is that machine learning models need less processing power enabling them to be used in many places regardless of limited resources. As a result, small and mid-sized companies are picking up AI at a faster pace to save money. In addition, ML is able to process genomic, proteomic, chemical and health data from patients, giving a complete picture of both drug action and patient responses. By collecting detailed knowledge, it is possible to make better choices at every step in drug development. Overall, machine learning’s effectiveness is based on being flexible, efficient, scalable and reliable in generating usable insights which is why it is chosen to drive drug discovery and speed up pharmaceutical innovation.

• Charles River Laboratories International, Inc. and Valo Health, Inc. announced the discovery of a product candidate that can be advanced for treating various types of lupus and other autoimmune conditions where there is a considerable unmet need. This discovery marks the first application of Logica, a drug development solution powered by artificial intelligence (AI) that converts biological insights into optimized preclinical assets.

Therapeutic area Segment Analysis

The therapeutic area segment is divided into oncology, neurology, infectious diseases, cardiovascular diseases and others. The oncology segment dominated the market, with a share of around 37% in 2024. Among all therapeutic areas, oncology leads in the AI-driven drug discovery platforms market because of the various challenges, how common cancer is and how urgent the care requirements are. Cancer involves many kinds of genetic changes, different tumour surroundings and various patient responses making it extremely challenging to find effective drugs for cancer. AI used in oncology can look through many different types of data, from genetic records to clinical information, to discover new drug targets, estimate treatment results and customize care. The increase in oncology is largely due to a greater effort and more money devoted to developing cancer drugs. Many companies and research institutions are prioritizing oncology because of a large group of cancer patients, high cancer-related deaths and the big benefits of successful cancer treatments on the market. AI helps quickly detect important connections in how tumours act and in patients’ information which allows experts to develop better targeted therapies with fewer side effects. In addition, precision medicine which tests DNA and RNA to design treatments for specific patients, is developing rapidly in oncology. Through use of AI, this process becomes much easier. In addition, government entities now favour new oncology therapies which also augment’s the segment’s growth. The field of oncology commands the AI-driven drug discovery market mostly because cancer is complex to treat, there are big gaps in care, there is rapid rise in cancer patients and plenty of funding is provided for cancer research.

End-User Segment Analysis

The end-user segment is divided into pharmaceutical & biotechnology companies, contract research organizations (CROs) and academic & research institutes. The pharmaceutical & biotechnology companies segment dominated the market, with a share of around 44% in 2024. Most of the AI-driven drug discovery platforms are used by pharmaceutical and biotechnology companies, who depend on them to stay ahead in the market with new drugs, medication, therapies and treatments. They can make use of AI technologies in drug discovery because they have enough resources, rich private data and competent staff. Since bringing a new drug to the market is both difficult and expensive, AI-based tools are being used by these companies to make the process more efficient, faster and successful. In addition, the need to innovate due to rising competition and the interest in providing specific treatments motivates these companies. Overall, the main companies in this market are pharmaceutical and biotechnology firms that lead with their major role in drug development, strong investments, focus on new ideas and direct advantages from applying AI to discovering drugs.

Some of the Key Market Players

• Atomwise
• BenevolentAI
• BioXcel Therapeutics
• Cyclica
• Exscientia
• Healx
• Insilico Medicine
• InVivo AI
• Owkin
• Recursion Pharmaceuticals
• Schrödinger
• TwoXAR

Report Description

• Market Introduction
• Market Dynamics
• Segment Analysis
• Some of the Key Market Players