Google Cloud Launches AI-Powered Tools to Accelerate Drug Discovery
It takes 10-15 years of extensive research, cohesive hard work and millions of dollars to develop and engineer a new drug. With generative AI tools that are built on large language models (LLMs), the drug development process could accelerate tremendously. That’s exactly what Google Cloud promises to deliver with its new-age generative AI models such as Med PaLM2, hypercomputing and AlphaFold tool.
The healthcare industry is going through a colossal transformation due to the rise in the number of global pandemics in recent years. The last 150 years had seen many pandemics, and it is often regarded as the century of pandemics- but COVID-19 killed more people than any other pandemic in such a short time. COVID-19 was declared as a pandemic in March 2020; by September 2020, 1 million had perished due to COVID-related symptoms. During the COVID-19 pandemic, several pharma companies came under the spotlight for bringing drugs to the market faster. Considering the historical data on drug discovery and the development of re-engineered drugs, bringing out new vaccines wouldn’t have been possible without the help of artificial intelligence and machine learning. The power of cloud computing, data analytics, and artificial intelligence-enabled pharmaceutical businesses to gain real-time access to more clinical and genome data which led to the development of unique toolkits for fighting pandemics.
Google Cloud announced the launch of two new AI-powered tools – Target and Lead Identification Suite and Multiomics Suite– aimed at accelerating drug discovery and advancing precision medicine for biotech and pharmaceutical businesses.
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Target and Lead Identification Suite – AI-powered Features for Drug Discovery
Target and Lead Identification Suite is intended to assist businesses in predicting and comprehending the structure of proteins, an essential component of medication development.
With the help of the Target and Lead Identification Suite, scientists can create drugs more effectively in silico by predicting antibody structures, analyzing the structure and purpose of amino acid mutagenesis, and speeding up the creation of new proteins.
Additionally, this technology enables lead optimization, which may be applied to free energy perturbation (FEP) computations, quantitative structure-activity relationship (QSAR) research, and the discovery of new, high-quality candidates at a reasonable cost.
Benefits of Target and Lead Identification Suite
- Accurately predict target protein structures with only the input of the amino acid sequence.
- Scale HPC resources quickly as needed to characterize targets and find top-notch lead candidate compounds.
- Reimagine the entire procedure using methods that are affordable, reproducible, and create quality lead candidates.
Multiomics Suite – Features
With the help of Multiomics Suite, mass volumes of genomic data will be ingested, stored, analyzed, and shared by researchers.
The Multiomics Suite accelerates scientific advancement by converting multiomics information into insights for precision medicine care.
Streamline and speed up the processing of genomic data, create clinical genomics quickly and effectively, and analyze genomic data to make new discoveries.
Design replicable and scalable workflows to improve collaboration between scientists and data scientists, saving time on creating fresh approaches, algorithms, or techniques.
Benefits of Multiomics Suite
- Cloud computing and AI can speed up genomic analysis. Scalable, economical, and secure genomic data storage, processing, and analysis are required.
- By absorbing and analyzing multimodal datasets at scale, it is possible to hasten the discovery of novel targets for therapy and genetically stratified clinical trials.
- By standardizing genetic identification procedures, the quantity of manual intervention needed can be decreased.
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The new breakthroughs represent Google’s most recent development in the fierce AI supremacy battle where tech giants are vying for supremacy in an industry that might be worth trillions of dollars, according to analysts. Since the public release of OpenAI’s ChatGPT late last year, Google has been in a tight spot to showcase its generative artificial intelligence technology.
In February, Google made Bard, a generative chatbot, public, and more recently, Google announced various AI advancements at its annual developer conference. The two new Google Cloud suites deal with a long-standing problem in the biopharma sector: the expensive and time-consuming process of introducing a new drug to the American market.
According to a recent Deloitte analysis, pharmaceutical corporations can spend anywhere from a few hundred million dollars and over $2 billion to launch a single medicine. These efforts don’t always yield results. Their efforts aren’t always successful. Another Deloitte analysis states that the likelihood of medicine being approved in the U.S. once it has completed clinical trials is 16%. That’s not all.
The lengthy and tiresome research procedure, which normally takes between 10 and 15 years, goes hand in hand with the high cost and dismal success rate.
Drug discovery is an intricate, unpredictable (read the success of the drug) business. But thankfully, scientists, in the last few years, have suggested using AI to uncover novel pharmaceuticals to treat a variety of ailments more quickly and affordably.
Google’s newest announcement combines the science of genetics with the business of discovery and has the potential to enormously transform the landscape of precision medicine and drug discovery. These innovative solutions will be exceptionally helpful to patients who are earnestly waiting for life-saving treatment for cancer or maybe a medicine to combat headaches. This means that now healthcare providers will be better equipped to get treatments for some of the most complex and devastating diseases faster to the market while also drastically improving the lives of thousands of people.
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