DELRAY BEACH, Fla., Nov. 12, 2024 /PRNewswire/ — The global artificial intelligence (AI) in drug discovery market is projected to reach USD 6.89 billion by 2029 from USD 1.86 billion in 2024, at a CAGR of 29.9% from 2024  to 2029. The rising shift towards integrating AI for understanding diseases and small molecule design and optimization use cases augments the growth of the market. AI tools play a major role in accelerating target identification, optimizing lead compound selection, and predicting drug efficacy and toxicity. Supervised methods such as regression, decision trees, and neural networks help predict material properties and drug candidate profiles. In contrast, unsupervised techniques such as clustering algorithms (k-means and hierarchical clustering) and dimensionality reduction identify hidden trends, patterns, and groupings in data, aiding in novel drug discovery. Deep Learning is employed to predict molecular properties, design new compounds, and other applications. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are more commonly used for handling complex data, such as sequences or molecular structures. Generative Adversarial Networks (GANs) are utilized to generate de novo or novel drug candidates, molecular structure simulation, and lead optimization. These tools help streamline processes, as well as reduce the time and cost involved in screening, optimizing, and discovering new drug candidates.

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Based on therapeutic area, the artificial intelligence (AI) in drug discovery market is segmented into oncology, infectious diseases, neurology, metabolic diseases, cardiovascular diseases, immunology, mental health, and others (respiratory diseases, nephrology, dermatological diseases, genetic disorders, inflammatory diseases, and gastrointestinal). The neurology segment held the fastest market share in the Al drug discovery market due to the increasing number of neurological disorders and the difficulties associated with drug discovery in this field, The use of Al technologies accelerates drug development because neurological diseases such as Parkinson’s, Alzheimer’s, and multiple sclerosis are difficult to diagnose and treat.

According to the WHO, in 2021, neurological conditions affected over 3 billion people worldwide. This emphasizes the need for innovative healthcare solutions. Al-powered drug discovery platforms work well in neurology, allowing faster and more accurate analysis of complex neurological data to identify potential drug candidates. The increasing availability of large datasets from clinical trials, genomics, neuroimaging, and electronic health records (EHR) records, as well as the growth investments in neurology, also contribute to market growth. In 2023, venture investments in neurology companies were USD 1.14 billion in the US. Resources like the Alzheimer’s Disease Neuroimaging Initiative (ADNI) provide vast data for machine learning applications.

Based on the process, artificial intelligence (AI) in drug discovery market is broadly classified into target identification & selection, target validation, hit identification & prioritization, hit-to-lead identification/lead generation, lead optimization, and candidate selection & validation. Hit-to-lead identification/lead generation accounts for the largest process segment in this market; it is also expected to register the highest growth owing to its critical role in streamlining the early stages of the drug development process. AI can significantly reduce the number of compounds and experiments required to find and optimize leads. This procedure uses Al algorithms to analyse large datasets, such as chemical libraries and biological interactions, to identify potential drug candidates or hits and develop them into viable leads. AI helps address long design-make-test cycles across discovery journeys. Faster screening and better exploration of vast chemical libraries are fuelling market growth for AI solutions for hit-to-lead identification/lead generation. For example, activity prediction enables more targeted experiments, and predictive analytics can help forecast compound properties. However, the lack of existing clinical data for a new molecule can hinder market growth.

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There are several opportunities in AI in drug discovery market such as growing biotech industry, emerging markets, AI in single cell experiments, Growing demand for precision medicine and personalized medicine. Key players in the artificial intelligence (AI) in drug discovery are NVIDIA Corporation (US), Exscientia (UK), Google (US), BenevolentAI (UK), Recursion (US), Insilico Medicine (US), Schrödinger, Inc. (US), Microsoft (US), Atomwise Inc. (US), Illumina, Inc. (US), Valo Health (US) among others. These players not only have a comprehensive and diverse product portfolio but also a strong geographic presence.

The key players in this market can tap these opportunities to enhance and improve their product offerings and develop innovative AI solutions for various processes, use cases, and end users. Moreover, these companies can integrate AI in drug design and optimization, understanding diseases, drug repurposing, single-cell analysis, and identification of biomarkers, disease types, and subtypes. Additionally, key players invest in AI algorithms that can examine complicated data and interpret disease mechanisms and pathways. Collaborations and partnerships between key players continue to grow rapidly, which in turn has accelerated the development of targeted therapies and contributed to rapidly expanding drug pipelines.

These market players have offered a diverse range of products and services and have operations worldwide with a major focus on North America. They rely on technological advancements to avail themselves of innovative end-to-end solutions, software, and services and increase their global footprint. These players focus on product launches and enhancements, investments, partnerships, collaborations, agreements, joint ventures, funding, acquisitions, expansions, conferences, FDA clearances, sales contracts, alliances, and other recent developments to expand their global reach and develop AI in drug discovery solutions.

NVIDIA Corporation:

NVIDIA Corporation, a prominent technology company, has made remarkable advances in artificial intelligence (AI), particularly drug discovery. NVIDIA GPUs power AI workloads in drug discovery applications such as molecular modeling, simulation, and machine learning. Nvidia’s AI software includes development tools, libraries, and frameworks that can be used to build and deploy AI applications for drug discovery. The company provides generative AI platforms like BioNeMo and Clara Discovery. NVIDIA BioNeMo simplifies the deployment of AI models, hastening the transition to AI-driven drug discovery. It offers models for 3D protein structure prediction, molecule generation, property predictions, and molecular docking. Users can customize AI models with their data, access pre-trained models, and integrate them into drug discovery workflows. NVIDIA Clara Discovery is a software suite that includes a variety of tools for AI-driven drug discovery.

NVIDIA Corporation operates more than 50 offices across the Americas, Asia, and Europe, along with manufacturing facilities in the US and Taiwan. The company collaborates with several pharmaceutical companies and biotech startups to develop solutions for AI-driven drug discovery. For example, in November 2023, NVIDIA collaborated with Genentech to improve computational models and incorporate generative AI into drug discovery processes, allowing for faster exploration of molecular designs.

Insilico Medicine:

Insilico Medicine is an end-to-end generative AI-driven biotech company accelerating drug discovery and development to treat cancer and age-related diseases. Its proprietary Pharma.AI platform spans biology, chemistry, and clinical development. The company pioneered the application of reinforcement learning and generative adversarial networks (GANs) to develop new molecular structures for diseases with known and unknown targets. Its products and services are used by pharmaceutical companies, biotechnology companies, and academic research institutions. Insilico Medicine has produced a diversified internal pipeline of 31 programs for 29 drug targets. The company’s lead drug is ISM001-055, a small-molecule chemical drug candidate primarily designed to treat fibrosis-related indications, including idiopathic pulmonary fibrosis. The company has offices in the US, Hong Kong, Canada, and the UAE. The company has more than 150 academic and industrial collaborations worldwide. For instance, in September 2024, Insilico Medicine collaborated with Inimmune to leverage its proprietary AI platform, Chemistry42, in accelerating the discovery and development of next generation immunotherapeutic.

Exscientia:

Exscientia is an AI-driven small-molecule drug discovery and design company. The company designed and developed an end-to-end solution of AI and experimental technologies for target identification, drug candidate design, translational models, and patient selection. The company’s patient-first AI process comprises four elements, namely, Precision Target (experimental and literature-based AI systems to prioritize projects), Precision Design (an extensive platform of AI technologies to design innovative drugs), Precision Experiment (tech-enabled precision experimentation to derive better data), and Precision Medicine (integrated analysis of patient data to ensure clinical relevance). The platform has helped design candidate drug molecules that have progressed into clinical trials and improve disease understanding through AI-guided assessment and generative AI. Exscientia collaborates with pharmaceutical companies such as GSK, Sanofi, and Roche. The company has offices in the UK, the US, and Japan. For instance, in July 2024, Exscientia announced the expansion of its collaboration with Amazon Web Services, Inc. to utilize AWS’s AI and machine learning services, further enhancing its end-to-end drug discovery and automation platform.

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