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Revolutionizing The Discovery Of Novel Drugs: The Transformative Power Of Bioinformatics In Pharma B

Updated: Oct 19, 2023

The pharmaceutical industry has been at the forefront of scientific innovation for decades, tirelessly working to discover and develop new drugs to enhance human health.


Source: Unsplash


However, this noble pursuit is extremely challenging and its labor-intensive nature is becoming increasingly costly for manufacturers. Between 2003 and 2013, the average cost of bringing a new drug to market nearly doubled, soaring to a staggering USD$2.6 billion on average. Additionally, the time it takes to move one drug from the lab to the market has extended to 12 years. 90% of drugs tested fail in one of the phases of human trials.


As the pharmaceutical industry grapples with these rough waters, it is rapidly turning to the field of bioinformatics and data training as a beacon of hope.

This intersection holds the promise of not only reducing costs but, more importantly, accelerating the drug discovery and development process.

Bioinformatics is a vast interdisciplinary field that merges data science, computer science, and biology. In this case, it can leverage machine learning trained on a vast amount of available data to successfully predict drug compounds with high intended therapeutic effect to accelerate scientists ability to make novel insights and discoveries - an absolute game changer.



The Journey to Market


Drug discovery is the initial phase of the drug development journey. Within this process, often referred to as the drug development pipeline, thousands of drugs undergo testing in the pursuit of efficiently identifying a single promising candidate to propel into further stages of evaluation.


Source: innoplexus

(x) - Out of the 10,000 compounds tested, only one emerges as a potent marketable drug.


How AI is Accelerating Drug Development


  1. Virtual Screening: AI enables high-throughput virtual screening on immensely vast chemical libraries to identify potential drug candidates efficiently.

  2. Target Identification: AI analyzes biological data to predict and validate drug targets, enhancing the selection of promising pathways.

  3. Drug Design: Machine learning aids in designing new drug molecules by predicting their properties, leading to more effective and safer compounds.

  4. Drug Repurposing: AI identifies existing drugs with potential for new therapeutic uses, saving time and resources in development.

The companies that are influential in shaping the aspects mentioned above are often highly innovative small-cap startups. These startups find themselves in one of two situations: either they become acquisition targets for bioinformatic giants like Thermo Fisher Scientific (U.S.), Eurofins Scientific (Luxembourg), and Agilent Technologies (U.S.), which expand their service offerings, or they engage with large pharmaceutical companies such as Pfizer, Roche, and Novartis, licensing their algorithms and data platforms.

This cross-sector collaboration is now automating labor-intensive tasks and reducing trial-and-error experimentation, thereby streamlining the drug development process and ultimately saving valuable time and resources.

How do these bioinformatic companies monetise their services?


  1. Software Licensing to Pharma Companies: Offering specialized software solutions for data analysis and interpretation to pharmaceutical firms.

  2. Data Licensing and Sharing: Collecting and curating valuable biological data, such as genomics and proteomics data, and licensing it to interested parties.

  3. Consulting and Services: Providing consulting services to assist research organizations and pharmaceutical companies in analyzing complex biological data.

  4. Collaborative Research: Collaborating with research institutions, pharmaceutical companies, and biotech firms on joint research projects. These collaborations often lead to revenue-sharing agreements, consulting fees, or co-development of products.

  5. Educational and Training Services: Offering training programs, workshops, and educational materials to help researchers and scientists effectively utilize bioinformatics software and tools.


Recent Licensing Agreement for Drug Discovery Collaboration:


To Conclude


While it may be a few years before drugs designed with the help of AI hit the market, the technology is already set to disrupt the pharmaceutical industry. Investors interested in this sector should beware of this fundamental change, and keep an eye for high performing small cap innovators in the sector.


As we enter the era of AI-driven drug design, we must also confront the challenges that this technology may pose. Concerns arise that this technology could potentially be misused for more nefarious purposes, such as the creation of new, harmful synthetic party drugs or the production of highly processed foods.. Nevertheless, this technology is forging a new path towards a brighter future in the extraordinary field of drug discovery. It promises the development of new drugs to combat current and future diseases, particularly within the realm of personalized medicine, where the role of bioinformatics will be boundless.


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