Revolutionizing the Process of Drug Discovery
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Revolutionizing the Process of Drug Discovery

Researching Disease with AI: Revolutionizing the Process of Drug Discovery

To skeptics, AI in drug discovery sound familiar with all intelligent hype; after all, AI and biopharma have promised much since the 1950s.

Near to 7,000 known rare diseases have witnessed any progress in the treatment development. AI solutions can boost scientific research by using accurate and quick in silico testing using deep learning algorithms to recognize new potential drugs at low prices and fast rates.

Developing a new prescription medicine to gain market approval is becoming a cost-intensive process and, in recent times, recording a growth of 145%, amounting to $2.5 billion with an average of 10 years required for the development. The prevalence of rare diseases is also increasing, with more than 400 million people suffering from these and 95% of rare diseases lack an approved treatment from the US FDA. With the emergence of AI and its subfields, such as deep learning and machine learning, we can change these statistics for improved healthcare services.

Investment in healthcare infrastructure profoundly backs the growth of Artificial Intelligence (AI) in the Drug Discovery Industry. AI has a key role in the overall development of biologics. Its usage is keenly focusing on small molecule and chemical research applications, owing to AI’s potential to enhance the specificity and structural understanding resulting from the increasing availability of unstructured and structured scientific data. Although, the usage of AI technologies to boost drug discovery is still at a nascent stage with the availability of applications that are precursors to broader scopes such as biologics AI.

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In drug discovery, AI algorithms are used in available information or research data on the 3D structure and binding properties of small molecules to identify the target specificity with high accuracy using deep learning (DL) processes.

Pharma and Biotech Companies Leveraging the benefit of Data-Driven Decisions in the Industry.

Major biopharma companies are now opting for AI-driven solutions for drug discovery, making it a variety of deals to access this capability. These deals include acquiring intellectual property rights to the assets, technology access fees, royalties, option exercise fees, and income based on the sale and sublicensing of drugs under the collaboration. Researching about companies in the AI in drug discovery market shows that as of 2019, Pfizer, Takeda, and Sanofi disclosed five deals each in the AI-driven big biopharma market, while Roche, Merck, Abbvie each grabbed four deals, and Janssen and GSK with three deals each and so on.

In the past few years, biopharma companies have adopted various strategies to integrate AI into the drug discovery process actively. Some of them are establishing their own dedicated teams, creating collaborations with tech giants and/or research centers, and investing in start-ups. While evaluating the key strategy is too early for AI in the drug discovery market. Still, the trend is becoming consistent with the increasing adoption of more than one AI solution at different stages of the drug discovery process and various collaborations and deals. Start-ups that have secured collaborations with biopharma companies in these areas have developed competitive strategies for large data access. For instance, Innoplexus is pairing blockchain technology with AI to mine data without compromising security and/or breaching ownership rights. With several large biopharma companies as clients, the startup aims to add these companies’ proprietary data to their own database and offer better predictions for their client portfolio. Cyclica is an AI-based company that focuses on polypharmacology’s technological development that developed a drug-centric, proteome-wide approach to recognize all proteins that a small molecule can potentially interact with and deliver information on unwanted leads or target lead prioritization for another type of diseases.

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AI algorithms study and present their analyses similar to the systematic literature review, but in a concise time span which can be in seconds rather than months. The increasing interest in AI-driven solutions for early-stage drug discovery is rising gradually among biopharma companies with a projected market revenue growth at a compounded annual growth rate (CAGR) of 42% during the forecast period. With new R&D collaborations between AI-driven companies and biopharma players- majorly startups, the AI in the drug discovery market witness astonishing growth.


Emma is a freelance writer and content strategist who offers to ghostwrite, blogging, and copywriting services. She has a keen interest in content marketing with a hold on social media management and market research. With over five years of experience writing for different domains, she is currently exploring a new area of interest in “AI in Drug Discovery.” Pitch her out to discuss interesting and niche
healthcare domains that are blooming in integration with modern technologies.

Revolutionizing the Process of Drug Discovery

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