Global AI for Drug Development and DiscoveryMarket
The global AI for drug development and discoverymarket is estimated to be worth over USD 26 Bnin 2033 and is expected to grow at CAGR of40.0% during the forecast period (2024-2033).
The global AI for drug development and discovery market is experiencingswift growth, fuelled by the need for faster, more efficient, and cost-effective drug discovery processes. One of the major market drivers is the accelerated drug discovery process allowed by AI. By leveraging machine learning (ML) algorithms and data analytics, AI substantiallyminimizes the time required to identify potential drug candidates, optimize molecular structures, and predict drug efficacy, thus shortening the overall development timeline. AI also improves accuracy, minimizing failure rates in clinical trials, which conventionallyhamper the drug discovery process.
A significant opportunity for the market is the soaring demand for personalized medicine, where AI holds a crucial role in assessing patient-specific data, such as genetics, medical history, and biomarkers, to develop targeted therapies. This personalized approach is particularly essential in fields like oncology, where tailored treatments are becoming the standard of care. AI-driven platforms are helping pharmaceutical companies create more effective, individualized therapies, enhancing patient outcomes and opening new revenue streams.
In terms of developments, the surge of AI-driven drug repurposing is gaining momentum, allowing companies to identify new uses for existing drugs, thereby reducing costs and accelerating time-to-market. In addition, collaborations between AI start-ups and pharmaceutical companies are becoming more prevalent, as these partnerships integrate AI expertise with vast clinical and biological datasets, enhancing the efficacy of research and development efforts.
Overall, AI is revolutionizing the global drug development landscape by improving speed, precision, and cost-effectiveness. With advancements in AI technology and growing industry adoption, the market is set to play a crucial role in transforming drug discovery and bringing innovative therapies to patients faster than ver before.
The market report presents an in-depth analysis, highlighting the capabilities of various stakeholders engaged in this industry, across different geographies. Amongst other elements, the market report includes:
A preface providing an introduction to the full report, AI for Drug Development and Discovery market, 2023-2033.
An outline of the systematic research methodology adopted to conduct the study on AI for Drug Development and Discovery market, providing insights on the various assumptions, methodologies, and quality control measures employed to ensure accuracy and reliability of our findings.
An overview of economic factors that impact the overall AI for Drug Development and Discovery market, including historical trends, currency fluctuation, foreign exchange impact, recession, and inflation measurement.
An executive summary of the insights captured during our research, offering a high-level view of the current state of the AI for Drug Development and Discovery market and its likely evolution in the mid-to-long term.
A brief introduction to the AI for Drug Development and Discovery, highlighting their historical background, as well as information on their types, key aspects, key challenges and the advantages of using AI for Drug Development and Discovery.
A detailed assessment of the market landscape of AI for Drug Development and Discovery that are either approved or being evaluated in different stages of development, based on several relevant parameters, such as By Offering (Software, Services), By Technology (Machine Learning (Deep Learning, Supervised Learning, Unsupervised Learning, Reinforcement Learning, Other Machine Learning Technologies), Context-Aware Processing, Natural Language Processing, Others), By Therapeutic Area (Oncology, Infectious Diseases, Neurology, Metabolic Diseases, Cardiovascular Diseases, Immunology, Other Therapeutic Areas), By End Use (Contract Research Organizations (CROs), Pharmaceutical and Biotechnology Companies, Research Centers and Academic Institutes, Others). Further, the chapter features analysis on key niche market segments. In addition, the chapter features analysis of various AI for Drug Development and Discovery developers, based on their year of establishment, company size, location of headquarters and most active players.
An in-depth analysis of partnerships and collaborations that have been inked between various stakeholders, since 2019, based on several relevant parameters, such as the year of partnership, type of partnership, focus of partnership, purpose of partnership, therapeutic applications and most active players (in terms of number of partnerships). It also highlights the regional distribution of partnership activity in this market.
A detailed analysis of various investments made by companies engaged in this industry, since 2019, based on several relevant parameters, such as year of funding, type of funding (grants, seed, venture capital, initial public offering, secondary offerings, private equity and debt financing), type of HPAPIs, amount invested, geography, purpose of funding, stage of development, therapeutic area, most active players (in terms of number and amount of funding instances) and leading investors (in terms of number of funding instances).
An in-depth analysis of the various AI for Drug Development and Discovery focused initiatives undertaken by big market players, based on several relevant parameters, such as number of initiatives, year of initiative, type of initiative, purpose of initiative, focus of initiative and location of headquarters of the big pharma players.
One of the key objectives of this market report was to estimate the current market size and the future growth potential of the AI for Drug Development and Discovery over the forecast period. Based on several parameters, such as regional analysis as well as segmental analysis rates, we have developed informed estimates of the likely evolution of the AI for Drug Development and Discovery market over the forecast period 2023-2033. Our year-wise projections of the current and future opportunity have further been segmented based on relevant parameters, such as By Offering (Software, Services), By Technology (Machine Learning (Deep Learning, Supervised Learning, Unsupervised Learning, Reinforcement Learning, Other Machine Learning Technologies), Context-Aware Processing, Natural Language Processing, Others), By Therapeutic Area (Oncology, Infectious Diseases, Neurology, Metabolic Diseases, Cardiovascular Diseases, Immunology, Other Therapeutic Areas), By End Use (Contract Research Organizations (CROs), Pharmaceutical and Biotechnology Companies, Research Centers and Academic Institutes, Others), by key geographical regions (North America, Europe, Asia-Pacific, Middle East and Africa, and South America) and leading players. In order to account for future uncertainties associated with some of the key parameters and to add robustness to our model, we have provided three market forecast scenarios, namely conservative, base, and optimistic scenarios, representing different tracks of the industry’s evolution.