Leveraging Artificial Intelligence for Efficient Drug Discovery and Development

The global biopharmaceutical industry faces ever-increasing business hazards associated with research costs, troublesome patenting (changing standards, vague definitions, and else), significant competition, and more. The main focus area of strategic-thinking biotechnology players is to allocate resources (time, cost) more wisely to reduce those risks and, naturally, increase revenue.

Artificial intelligence and its various subfields (machine and deep learning, as well as artificial neural networks) are being actively adopted to streamline pharma research, which includes drug discovery and development. Computational technologies are implemented for cost-efficient drug discovery and repurposing along with clinical trials.

AI technology is radically changing conventional approaches

The objective of research and development in the biopharma domain is to generate quality drug candidates. But unfortunately, that process in the biotech sector has historically always been a stepped, pass-fail process – quite an inefficient process if considering the number of compounds being tested.

To generate drug candidates and produce efficient medication, pharma scientists are searching for molecules that can organically attach to proteins causing disease and change their structure to provoke positive changes. But oftentimes, pharma researchers find themselves experimentally resolving complex puzzles, as molecules can fold in thousands of ways.

Over the past years, we’ve seen digital transformation drastically revolutionizing pharmaceutical research. Despite this, digital innovation comes with considerable challenges in acquiring and applying new approaches to solve pharma problems.

That’s where artificial intelligence and its various subdisciplines (machine and deep learning) get into the game. The integration of today’s state-of-the-art approaches helps bring life-saving medication to the global market simultaneously minimizing human labor.

AI technology already delivering significant value: The numbers

Biopharma organizations are usually sluggish when speaking about incorporating advanced technology. But with the recent COVID-19 pandemic, biotech companies began prioritizing modern-day technology as an influential investment.

As stated by Deloitte:

• In 2021, pharma organizations have been actively adopting digital technology in their daily operations – 38% implemented artificial intelligence
• 77% of all respondents say they are viewing digital innovation as a competitive differentiator
• Most leaders agree that they must proactively address fundamental issues to scale digital innovation:
  • Funding (59%)
  • Strategy (49%)
  • Talent (47%)
• The leaders see multiple business benefits in applying digital tools:

  • Increased research productivity (95%)

  • Reduced research costs (76%)

  • Improved pipeline diversity (67%)

    
    

According to Precedence Research, artificial intelligence in the healthcare and pharma markets was estimated at over $11 billion in 2021 and should surpass around $188 billion in 2030.

According to Morgan Stanley, modest improvements in early drug discovery and development success rates enabled by artificial intelligence could generate 50 additional novel therapies over about 10 years bringing a $50 billion business opportunity.

The roadblocks

Years ago, target identification, lead optimization, and further drug discovery and development were manual and required significant resources.

At the very moment, scientific research has gone quite far, but challenges still remain:

• The length, complexity, uncertainty, and cost of research

• The lack of validated diagnostic/therapeutic biomarkers

• The unknown disorder pathophysiology

• The patient population heterogeneity

  
  

Most issues emerging during drug discovery and manufacturing commonly comprise adverse absorption, distribution, metabolism, excretion, and toxicity properties, creating constraints on the way to market launch. These issues can be efficiently resolved by facilitating early-stage prediction of potential therapeutic effects prior to clinical trials.

The opportunities

With the ever-increasing implementation of AI, the global biopharmaceutical industry notably evolved.

The application of AI is targeted to streamline:

• Drug discovery

• Drug screening

• Molecular design

• Drug development

• Quality control

• Clinical trials

• Market prediction and analysis

• Strategic pricing

  
  

In 2019, the World Economic Forum publicly stated that adopting artificial intelligence along with Big Data might start the fourth industrial revolution radically transforming the practice of customary biotech research. Healthcare and pharma organizations, like most mature companies across industries, start adopting Big Data, ML, DL, and ANN to overcome the challenges associated with biotech design.

Traditional approaches are being actively modernized to automate sophisticated processes, in particular:

• Prediction of drug-target interactions
• Prediction of protein structures
• Discovery of novel compounds
• Prediction of pharmacokinetic properties

And more!

Some platforms and toolkits have already been recognized:

• AlphaFold
• DeepChem
• Cyclica
• ODDT

And are being utilized by the scientific community, so there’s definitely more to come.

Final words

Advanced technology, in particular artificial intelligence, is revolutionizing drug discovery and development, providing cost-efficiency, minimized hazard, operational automation, data-driven decision-making, and more. Machine and deep learning along with other disciplines might contribute to establishing the quality and safety of the drug product at every single stage, from initial target identification to launch.

Given that enormous potential, biopharmaceutical organizations should re-consider accustomed approaches and implement artificial intelligence in their day-to-day processes, including even supportive documentation. Biotech companies that prioritize business flexibility and scalability, are already taking advantage of complex ML and DL techniques, respectively boosting business profitability.