Artificial Intelligence (AI) has a major role to play in preparing for and responding to future pandemics, a team of top Qatar-based researchers said.
“The data generated in the past two years can prove invaluable in terms of tackling the next pandemic,” stated Dr Sanjay Chawla, research director of Qatar Centre for Artificial Intelligence (QCAI) at Qatar Computing Research Institute (QCRI), part of Hamad Bin Khalifa University.
QCRI, conducts innovative, multidisciplinary applied computing research that addresses national priorities and one of its main strengths is its expertise in AI.
Dr Chawla explained: “Since the outbreak of Covid-19, large amounts of data have been generated, in addition to statistics on transmission and infection patterns. Many predictive and forecasting models have been developed since the start of the pandemic and AI models can learn from all generated data to obtain a better prediction of a future outbreak. This will enable decision-makers to prepare and intervene early in the hope of limiting the impact of future viruses and pandemics.”
Covid, noted Dr Chawla, has generated a massive digital footprint and to prepare for the next pandemic AI researchers can extract useful predictive and prescriptive patterns of response from this.
He said one area where AI can influence policies is lockdowns, and whether there is a need for a full lockdown or which regions to implement restrictions. Because of the availability of specific data, AI has the potential of designing more calibrated lockdowns.
“QCRI’s research has shown that integrating traffic information into epidemiological models results in a better understanding of how the disease is likely to spread. As a specific example, when a virus-infected patient lands at Hamad International Airport, QCRI’s work shows that different parts of Qatar will be impacted differently based on traffic mobility,” continued the researcher.
According to another scientist, AI can also assist in predicting what drugs could have the potential to tackle the pandemic.
Dr Mohamad Saad, a research scientist in QCAI, stated that AI and digital tools can be used to analyse drugs at the molecular level and can offer advantages at different stages of drug development, such as drug screening and drug designing.
“These tools can be used to predict physiochemical properties, bioactivities, toxicity, the structure of the target protein, and interactions with the target proteins of drug molecules to name a few. These tools have shown a lot of potential to predict drug behaviour by providing a better profile analysis, faster elimination of candidate compounds, and selection of potential lead compounds along with estimating absorption, distribution, metabolism, excretion, and toxicity drug candidates,” he described.
Dr Saad observed that AI can also assist in developing a treatment for future pandemics. “Compared to a traditional drug candidate discovery process that used to take four to five years, AI can reduce this to less than a year. In the case of future pandemics, drug repurposing has a major role to play in identifying cures and treatments for the disease in a short time,” he pointed out.
The researcher also highlighted that AI can help correlate disparate pieces of information.
“Machine learning techniques can help find connecting patterns between the chemical structure of a drug and the protein structure of a virus. This is possible because there is a large repository of existing drugs and known viruses available,” said Dr Saad.
Dr Ehsan Ullah from QCAI who led the project on drug repurposing noted: “The framework developed by QCRI independently tested a number of drugs for treating Covid-19, many of which were in the pipeline for FDA approval. Two potential drugs found by QCRI's drug repurposing framework to be effective against Covid-19 - Brilacidin1 and Ritonavir2 received approval for treating Covid-19.”
“In my opinion, machine learning and AI have huge potential in the field of drug repurposing, not only for pandemics but for personalised medicine as well as existing diseases such as cancer,” he added.
“The data generated in the past two years can prove invaluable in terms of tackling the next pandemic,” stated Dr Sanjay Chawla, research director of Qatar Centre for Artificial Intelligence (QCAI) at Qatar Computing Research Institute (QCRI), part of Hamad Bin Khalifa University.
QCRI, conducts innovative, multidisciplinary applied computing research that addresses national priorities and one of its main strengths is its expertise in AI.
Dr Chawla explained: “Since the outbreak of Covid-19, large amounts of data have been generated, in addition to statistics on transmission and infection patterns. Many predictive and forecasting models have been developed since the start of the pandemic and AI models can learn from all generated data to obtain a better prediction of a future outbreak. This will enable decision-makers to prepare and intervene early in the hope of limiting the impact of future viruses and pandemics.”
Covid, noted Dr Chawla, has generated a massive digital footprint and to prepare for the next pandemic AI researchers can extract useful predictive and prescriptive patterns of response from this.
He said one area where AI can influence policies is lockdowns, and whether there is a need for a full lockdown or which regions to implement restrictions. Because of the availability of specific data, AI has the potential of designing more calibrated lockdowns.
“QCRI’s research has shown that integrating traffic information into epidemiological models results in a better understanding of how the disease is likely to spread. As a specific example, when a virus-infected patient lands at Hamad International Airport, QCRI’s work shows that different parts of Qatar will be impacted differently based on traffic mobility,” continued the researcher.
According to another scientist, AI can also assist in predicting what drugs could have the potential to tackle the pandemic.
Dr Mohamad Saad, a research scientist in QCAI, stated that AI and digital tools can be used to analyse drugs at the molecular level and can offer advantages at different stages of drug development, such as drug screening and drug designing.
“These tools can be used to predict physiochemical properties, bioactivities, toxicity, the structure of the target protein, and interactions with the target proteins of drug molecules to name a few. These tools have shown a lot of potential to predict drug behaviour by providing a better profile analysis, faster elimination of candidate compounds, and selection of potential lead compounds along with estimating absorption, distribution, metabolism, excretion, and toxicity drug candidates,” he described.
Dr Saad observed that AI can also assist in developing a treatment for future pandemics. “Compared to a traditional drug candidate discovery process that used to take four to five years, AI can reduce this to less than a year. In the case of future pandemics, drug repurposing has a major role to play in identifying cures and treatments for the disease in a short time,” he pointed out.
The researcher also highlighted that AI can help correlate disparate pieces of information.
“Machine learning techniques can help find connecting patterns between the chemical structure of a drug and the protein structure of a virus. This is possible because there is a large repository of existing drugs and known viruses available,” said Dr Saad.
Dr Ehsan Ullah from QCAI who led the project on drug repurposing noted: “The framework developed by QCRI independently tested a number of drugs for treating Covid-19, many of which were in the pipeline for FDA approval. Two potential drugs found by QCRI's drug repurposing framework to be effective against Covid-19 - Brilacidin1 and Ritonavir2 received approval for treating Covid-19.”
“In my opinion, machine learning and AI have huge potential in the field of drug repurposing, not only for pandemics but for personalised medicine as well as existing diseases such as cancer,” he added.
