For years, the pharmaceutical industry has struggled with the sluggish and costly processes of bringing new medicines to markets. Research teams would spend years and millions sifting through chemical compounds, running lab tests, and hoping something stuck.
Today, artificial intelligence (AI) is quietly transforming this traditional model, making drug discovery more efficient. While associated with larger pharmaceutical companies, there’s no stopping healthcare startups from accessing the platform and reshaping the future of medicine.
The Main Challenges in Drug Discovery and Development
According to a 2022 study, approximately 90% of drug development processes fail due to both clinical and non-clinical reasons. About half of the drug candidates can’t produce their intended therapeutic effects, while a third generate unpredictable and potentially severe adverse effects. (1)
Around 10% have ‘poor drug-like properties’ that restrict them from becoming safe and effective medicines. Surprisingly, the fourth reason involves poor strategic planning and a lack of commercial needs. There are many ways to address these issues, but the integration of artificial intelligence (AI) is transforming the process in numerous ways. (1)
AI solutions for drug discovery can help make the drug discovery process and its subsequent stages more efficient. The platform’s machine learning and other advanced capabilities enhance the likelihood of developing safe and effective drugs in the shortest possible time.
How AI-driven Drug Discovery Breaks Bottlenecks
A successful clinical trial and drug development process hinges on proper planning and accurate predictions. With AI use, research organizations can forecast clinical outcomes instead of relying on costly trial-and-error.
Analyzing Volumes of Data
AI models thrive in complexity. Instead of studying one compound at a time, these platforms can study molecular structures within a few hours or days. High-throughput screening can accelerate traditional pharmaceutical research.
Generative AI, for instance, can develop new medications by creating biomolecular structures that possess the desired properties specified by clinicians. It can likewise identify how it behaves inside the human body.
Through deep learning, the platform can forecast drug-target interactions and flag potential toxicity based on molecular interactions, genomic data, and other information sources.
The technology’s next-generation problem-solving capabilities can suggest modifications to maximize drug efficacy and patient outcomes, helping research teams start their clinical studies on the right track.
Better Patient-Trial Matching
Recruiting participants for clinical studies can be a slow and expensive process. Researchers must scan through large volumes of clinical notes and pathology reports to identify potential treatment targets.
Natural language processing, along with its subset, large language models, automates and transforms complex and unstructured clinical text into structured eligibility data. Once patient data and trial criteria are set into a structure format, the algorithm can easily compare and match them.
Drug Repurposing
With artificial intelligence in drug development, scientists don’t have to start from scratch each time. AI can identify hidden potential in existing drug compounds in a matter of days, rather than years, unlike manual drug discovery processes. This approach is more cost-effective because extensive safety and performance data on the existing drug targets are available. As such, teams can start immediately with the clinical trials.
Peptide Design
AI relies on powerful computational methods to discover peptide sequences that could take years to do using traditional processes. Unsurprisingly, about 25% of the global drug products are peptide- or protein-based, earning more than USD$500 billion. Unlike small molecule medications, the human body breaks down these products into non-toxic amino acids, resulting in fewer adverse effects and reduced toxicity. (2)
Even so, it’s not enough for companies to have platforms for molecular simulation and toxicity prediction. AI performance is largely dependent on the quality of data you feed it. Mastering biological processes and data sciences is crucial in the current setting.
But how can AI-driven drug discovery help shape the future of health tech startups?
Speed and Capital Efficiency
In the US, the entire drug development process lasts a decade or so and costs research groups around USD$879.3 million, according to a 2024 conservative government estimate. Clinical trials account for the largest expenditure, averaging USD$117.4 million, which is approximately 68% of the total research and development expenditures. (3)
AI-designed drugs enable startups to complete the early-stage drug discovery process in months rather than years. As such, they can achieve crucial milestones that make them highly attractive to health research funders and investors.
With this capability, startups don’t have to secure millions before launching. They can instead focus on gathering high-quality datasets and training AI models in line with their business goals.
Precision Medicine and Diagnostics
A small company can use AI to link molecular modeling from the drug discovery phase with individual health care data to create personalized treatment plans for certain chronic or autoimmune diseases.
Pharmaceutical sciences have relied on AI-run diagnostic tools to analyze medical images and genomic data to predict disease progression. For example, a former health tech startup, which has now grown into a Series C company, initially used AI to examine tissue samples for cancer diagnosis. The company trained their model well before offering extensive digital pathology platforms.
Digital Therapeutics (DTx)
Apart from personalized treatments, the future also promises a complete health care package through digital therapeutics (DTx). This advanced healthcare approach taps digital healthcare monitoring and software-based interventions to manage or treat certain diseases.
Unlike general wellness apps, it caters to specific conditions, such as diabetes and substance use disorders. There’s a startup company that uses FDA-approved game-based therapy to improve the attention spans of children with attention deficit hyperactivity disorder (ADHD).
As AI is a pillar of DTx, it’s backed by clinical evidence, with some products requiring a doctor’s prescription. Apart from oral medications, interventions cover other types of treatment, including cognitive behavioral therapy (CBT).
Final Thoughts
Tapping AI for drug development addresses many existing and emerging health needs. Apart from shrinking timelines and drug development costs, it levels the playing field among many healthcare startups and pharmaceutical giants.
The key to success for small companies lies in ensuring data quality and target identification, while complying with the requirements set by regulatory bodies. Even so, the ultimate goal should always be to provide people with better and more accessible treatments, faster.
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