Deep Learning in Drug Discovery Market Size and Analysis, Trends, Recent Developments, and Forecast Till 2035

The global deep learning in drug discovery market is anticipated to grow at a CAGR of 21.9% from 2023-2035, reaching USD 34.5 billion by 2035.

Deep Learning in Drug Discovery: Market Overview

The current deep learning market landscape features the presence of over 70 players that are actively engaged in offering deep learning technologies / services for the purpose of drug discovery. It is worth mentioning that majority of the small players (67%) prefer to operate as technology / software providers. A similar trend can be observed for mid-sized companies. In contrast, most of the very large players (83%) claim to operate as service provider in this domain.

Deep learning is a complex machine learning algorithm that uses a neural network of interconnected nodes / neurons in a multi-layered structure, thereby enabling the interpretation of large volumes of unstructured data to generate valuable insights. The mechanism of this technique mimics the interpretation ability of human beings, making it a promising approach for big data analysis. The potential applications of deep learning algorithms are currently being explored in multiple sectors of the healthcare domain. Personalized medicine, lifestyle management, drug discovery, clinical trial management, public health management, medical image processing and diagnostics are some of the key areas for which deep learning based digital solutions are being developed.

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Deep learning offers great promise in the healthcare sector, as a result of which, various technology developers are shifting their focus towards this industry. however, unlike the environment observed in the technology industry, where an unsuccessful model can be easily improved in subsequent versions, the models developed for healthcare sectors have to be the best versions of themselves in order to achieve success.

This can be attributed to the fact that the healthcare field is highly regulated and hence, demands significant resources and capital for the launch of any new product. In a nutshell, deep learning technique has evolved as an excellent computational resource, that has the ability to process a large amount of data using neural networks. Deep learning is a rapidly evolving segment of artificial intelligence, and these both are believed to greatly influence the creation of new business models.

The players offering deep learning-powered technologies / services for drug discovery were analyzed across several relevant parameters, such as year of establishment, company size and location of headquarters, along with the information on companies’ service and product centric models. It is evident from the figure, majority of the players claim to offer technology / software as a service. An equal share of players offer deep learning solutions through research / discovery partnerships. It is worth noting that companies have recently started offering contract research services / fee for service model to support deep learning-based drug discovery  initiatives.

Further, It is worth mentioning that there has been a steady increase in the number companies providing deep-learning powered drug discovery services / platforms. In fact, more than 45% players were established post 2015. This can be attributed to the rising interest of industry stakeholders towards the implementation of advanced technologies in the drug discovery process. Examples of recently established firms include (in alphabetical order; established post-2020) Cortex Discovery (2021), Ensem Therapeutics (2021), Isomorphic Labs (2021) and Merative (2022).

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About Roots Analysis

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