Machine learning (ML) and artificial intelligence (AI) have become integral to many aspects of research and product development in biotech.
The various branches of the biotechnology industry ranging from human life sciences, food, agriculture, animal biotech, and industrial applications are leveraging the advancement of machine learning techniques to speed up research outcomes, cut operations costs, reduce manual efforts, and improve accuracy. Here’s how.
Implementing the laws of machine learning in agriculture can give a significant boost to food production, at a lower cost — increasing the GDP. According to Business Insider research, global spending on agricultural technologies — including machine learning — is projected to triple in revenue by 2025. Machine learning algorithms can be used in agriculture to educate farmers about best practices, identify crop ripening time, locate and remove weeds, and ensure better seed quality.
Uses for AI and machine learning in the health sector include drug screening, virtual health assessments, diagnosis, management and analysis of clinical trial data. This technology speeds up clinical trials and enables targeted therapies..
Today, cancer treatment teams are using AI and machine learning to design personalized cancer treatments, and better identify treatment options based on outcome predictions modeled using AI. Based on blood and bone marrow samples, biotech companies are using ML algorithms to build the most effective drug combination specific to the patient. The outcome analysis can predict effectiveness for individual patients, rather than give estimates based on samples of patients who may have different health backgrounds.
In the ongoing COVID-19 pandemic, scientists were able to save millions of lives by using machine learning and AI. These algorithms and programs helped scientists develop tests to screen for antigens, accelerate the identification of how to use the virus to generate an immune response, and discover and optimize new antivirals to treat COVID patients.
During the vaccine discovery process, scientists at Stanford used machine learning models called NetMHCpan and MARIA to identify “vulnerable spots on the virus”. This led the way to the development of vaccines that have protected millions from severe disease.
Thanks to technology, any person can now track and monitor their bodily processes using their smartphones and watches. More advanced ultrasound devices are also available that enable users to connect their smartphone and display the images from the machine in real-time.
With access to medical diagnostic devices that are powered by deep learning technology but can be used on the go, clinicians can bring the latest healthcare developments anywhere. And with cloud-based healthcare records, providers can send diagnoses and recommendations to teams of clinicians from anywhere in the world.
Lab managers are constantly looking for advanced ML algorithms to fuel fast-tracked innovation, production and development of drugs, chemicals, vaccines, and other biotech products. Here are a few ways machine learning accelerates biotech R&D.
Labs rely on knowledge management and cloud-based software to collect and transfer data. The information lab managers use and record day after day needs to be accurate and easy to come back to, or share later on for future discoveries.
With the power of technology, lab scientists have access to valuable data that can be standardized and turned into insights that help solve challenges and break down research barriers. Lab managers and scientists are maintaining massive databases that are further used by biotech labs and health organizations. This data is heavily reliable as it’s free of manual errors and accurate — and it plays a crucial role in identifying risk factors, expediting drug development, building personalized treatment plans, handling the supply chain, and analyzing huge amounts of data.
Calculating the permutations and combinations of various chemicals without having to perform the actual experiment accelerates the process of drug development and saves labs time and money. Scientists are also using ML programs that take over the manual tasks of data entry, analysis, and maintenance, adding back time that could be spent toward innovation.
If it hadn’t been for AI and ML, the vaccine for COVID-19 might not have come into existence. In addition to decoding data, ML software also helps scientists share the results of their studies with the scientific community across the globe. Many ML tools assist scientists in interpreting data, identifying patterns, and discovering solutions that couldn’t be seen before. Because of these ML advancements, discovery, accuracy and cohesion between biotech researchers has never been greater.
Data and intelligence are optimizing the biotech R&D process, and reducing the time to reach research milestones. In order to take advantage of the full potential of machine learning and AI, lab teams are focusing their efforts on identifying problems and creating tailored solutions.
As data becomes more accessible, organized, and universal, it frees up the minds of scientists to work on the process of testing hypotheses and bringing ideas to life. The rich, functional capabilities of technology combined with streamlined operational processes can produce advanced healthcare solutions. With more data comes more advanced medicine and better patient outcomes — just take a look at these trends in biopharma R&D in 2022.
In the lab of the future, robots conduct research, and AI and machine learning have made that a reality. Machine learning programs can take over data input and analysis, experiment modeling, evaluation, compound design, and synthesis, without requiring labs to spend time, resources, and budget on producing drugs that won’t end up being effective.
Source: Chemical & Engineering News
With robots and machine learning programs responsible for parts of the research process, biotech labs can process exponentially more data, with precision, and find meaningful research results, faster. Rather than fearing these technologies taking over the jobs of lab staff members, labs should embrace the possibilities of agile research and automating manual research tasks.
To learn more about optimizing the research process, read 6 Tips to Running an Efficient Lab.