Drug Design has Changed Forever: AI and Machine Learning in Biomedical Research
Tech, AI
The drug development pipeline is increasingly integrating AI automation, presenting a potential avenue for the production of more cost effective pharmaceuticals within accelerated timelines.
Human colorectal cancer cells treated with a topoisomerase inhibitor discovered by a machine learning model- National Cancer Institute
The pharmaceutical industry is constantly evolving and in recent years, there has been a significant shift towards the integration of artificial intelligence and machine learning to accelerate drug development. In 2021, Exscientia, a leading AI driven drug discovery company, began the development process for two drugs using their unique AI platform.
Since then, numerous startups have also recognized the potential benefits of utilizing machine learning in the pharmaceutical industry and have started exploring various applications of AI to streamline drug development processes.
This article delves into the growing trend of utilizing machine learning in pharmaceuticals, examining the benefits, challenges and future prospects of this revolutionary approach to drug development.
Technology That Test Drugs, Translates Directly to Real Patients
Researchers at the Medical University of Vienna conducted a trial testing a new matchmaking technology, developed by UK based company mentioned above, which pairs individual patients with precise drugs by taking into account biological differences.
In the trial, a small tissue sample from a patient with cancer was divided into over 100 pieces and exposed to various drugs. Robotic automation and machine learning were used to identify the drugs that worked best for the patient, effectively testing dozens of treatments at the same time.
Rather than subjecting a patient to lengthy courses of chemotherapy, which normally takes months or years, researchers tested numerous treatments simultaneously. This approach could replace traditional chemotherapy and provide more efficient and personalized cancer treatment.
There is a Vision to Use AI to Expedite and Reduce the Cost of Drug Discovery
The Drug Development Process Currently Takes Over a Decade and Billions of Dollars on Average. Machine learning models can predict how potential drugs may behave in the body, allowing for the elimination of dead end compounds before they are subjected to costly and time consuming lab work. This approach has the potential to accelerate the drug discovery process and make it more cost effective.
Another example of using AI to expedite and reduce the cost of drug discovery is the partnership between BenevolentAI and AstraZeneca. BenevolentAI, a UK-based AI drug discovery company, developed a machine learning algorithm to analyze vast amounts of scientific data and identify potential drug targets.
AstraZeneca, a leading pharmaceutical company, collaborated with BenevolentAI to use their AI platform to identify new drugs for chronic kidney disease and idiopathic pulmonary fibrosis. Using AI, the team was able to rapidly identify new drug candidates and optimize their properties, which significantly reduced the time and cost of drug discovery.
For instance, they discovered a potential drug candidate for idiopathic pulmonary fibrosis, which was in preclinical development in just nine months instead of the traditional time of around two years. This significant reduction in development time can be attributed to the use of AI in the drug discovery process.
This partnership showcases the potential of AI in drug discovery to accelerate the development of new drugs and make the process more cost-effective. By eliminating the need for costly and time-consuming lab work on dead-end compounds, AI can help pharmaceutical companies streamline the drug discovery process, reduce the cost of drug development and bring new treatments to patients faster.
AI Hit the Market and is Going to Shake Pharma Industry
AI has made its mark on the pharmaceutical industry, disrupting the drug development process from initial design to final approval. The conventional drug development process involves identifying a target in the body and designing a molecule to interact with it, followed by testing the molecule in a lab and on humans to ensure safety and efficacy.
AI has the potential to revolutionise these steps by utilising advanced algorithms to expedite drug discovery, optimise molecule design and predict drug behaviour, ultimately leading to faster and more effective drug development.
The emergence of AI in the pharmaceutical industry is expected to have a significant impact on short term also on drug discovery, clinical trials and patient care. AI technologies such as machine learning and natural language processing are being used to analyse vast amounts of data and identify new drug targets, optimise drug dosing regimens and personalise treatments for patients.
Although, AI can help reduce the time and cost of drug development, ultimately making new drugs more accessible to patients. As more pharmaceutical companies invest in AI, the market is likely to experience significant growth, and the industry as a whole is expected to be transformed by this new technology. The potential of this technology to revolutionize drug development and patient care cannot be overstated, and the future of the pharma industry is set to be shaped by these advances.
The Bottom Line
In a world where traditional drug discovery methods can take years and billions of dollars, the impact of AI and machine learning in the pharmaceutical industry cannot be understated.
The adoption of these technologies has changed drug design forever, bringing an unprecedented level of efficiency and precision to the process. It's clear that the future of drug design lies at the intersection of technology and healthcare and the possibilities are truly limitless.
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