Drug discovery is the initial step in the process of a novel drug candidate’s identification and its therapeutic target. Drug discovery or new drug target are being evaluated based on efficacy, potency, bioavailability, and toxicity. Artificial intelligence is widely used in the healthcare industry and especially used for drug discovery. Artificial intelligence technology has the capability to recognize drug targets, and play an important role in drug design, discovery, identification and screening of molecules instantly and effectively. The global AI for the drug discovery market is highly fragmented and have a lower level of competition as few companies matched the required level of expertise. For instance, there are more than 100 AI companies in the world today but only a few of them can enter the AI in the drug discovery sector due to the lack of expertise.

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The AI for Drug Discovery market is rapidly growing, due to the rising number of cross-industry partnerships and collaborations, a significant increase in venture capital investments. Furthermore, the growth in importance of drug discovery and rise in funding of the R&D activities for the use of AI technology in the field of drug discovery is expected to fuel the market growth. Moreover, untapped market opportunities in developing regions such as India and China will support the growth of artificial intelligence in drug discovery in the coming years. However, reluctance among medical practitioners to adopt AI-based technologies, ambiguous regulatory guidelines for medical software and lack of interoperability between AI solutions offered by different vendors are few factors which may impede the growth of AI for drug discovery market during the forecast period.

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Various notable players operating in the market, include Atomwise, Insilco Medicine, Inc., BIOAGE, Exscientia, twoXAR, Incorporated, Numerate, Inc., NuMedii, Inc., Envisagenics, Globavir, Inc. Cloud Pharmaceuticals, Inc, BenevolentAI among others. Major players are looking toward AI to speed up drug discovery, cut R&D costs, decrease failure rates in drug trials, and eventually create better medicines. For instance, Pfizer entered into research collaboration with IBM Watson to design a system that uses machine learning, to power its search for immuno-oncology drugs.

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The global AI for drug discovery market has been segmented on the basis of drug type, technology type, therapeutic type, end-use, and key geographies. Based on drug type, the market comprises of the drug type small molecule and large molecules. The research report “global AI for Drug Discovery market” provides in-depth analysis of global AI for drug discovery market based on drug type, technology, therapeutic, end-user and major geographies for the forecast period from 2018 to 2025. The report also highlights the major market drivers propelling the growth as well as challenges faced by market participants. The research report provides market size and forecast for the global AI for the drug discovery market. In addition, the report also analyses the competitive landscape, major players and their strategies in 2018. The competitive landscape section of the report captures and highlights the recent developments in the market.

Key questions answered in this research report:

At what pace is AI for the drug discovery market growing? What will be the growth trend in the future?
What are the key drivers and restraints in the current market? What will be the impact of drivers and restraints in the future?
What are the regional revenue and forecast breakdowns? Which are the major regional revenue pockets for growth in the AI for the drug discovery market?
What are the various application areas and how they are poised to grow?

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