With GlaxoSmithKline unveiling a new $43 million deal in the field, in order to improve the hit-and-miss business of finding new medicines, the world's leading drug companies are turning to artificial intelligence.
Also exploring the potential of artificial intelligence (AI) to help streamline the drug discovery process are other pharmaceutical giants including Merck & Co, Johnson & Johnson and Sanofi.
In order to predict how molecules will behave and how likely they are to make a useful drug, thereby saving time and money on unnecessary tests, harness modern supercomputers and machine learning systems is the aim.
In other high-tech areas such as the development of driverless cars and facial recognition software, AI systems already play a central role.
"Many large pharma companies are starting to realize the potential of this approach and how it can help improve efficiencies," said Andrew Hopkins, chief executive of privately owned Exscientia, which announced the new tie-up with GSK.
Using roughly one-quarter of the time and at one-quarter of the cost of traditional approaches, Exscientia's AI system could deliver drug candidates, said Hopkins, who used to work at Pfizer.
One of a growing number of start-ups on both sides of the Atlantic that are applying AI to drug research is the Scotland-based company, which also signed a deal with Sanofi in May. Britain's BenevolentAI and U.S. firms Berg, Numerate, twoXAR and Atomwise, are also among other companies.
"In pharma's eyes these companies are essentially digital biotechs that they can strike partnerships with and which help feed the pipeline," said Nooman Haque, head of life sciences at Silicon Valley Bank in London.
"If this technology really proves itself, you may start to see M&A with pharma, and closer integration of these AI engines into pharma R&D."
In order to boost R&D productivity, this is not the first time drugmakers have turned to high-tech solutions.
Generating mountains of leads in the early 2000s but notably failed to solve inefficiencies in the research process was the introduction of "high throughput screening", using robots to rapidly test millions of compounds.
Since it is yet to demonstrate it can successfully bring a new molecule from computer screen to lab to clinic and finally to market and that is the common knowledge in the market, big pharma is treading cautiously when it comes to AI.
"It's still to be proven, but we definitely think we should do the experiment," said John Baldoni, GSK's head of platform technology and science.
By hiring some unexpected staff with appropriate computing and data handling experience - including astrophysicists, Baldoni is also ramping up in-house AI investment at the drugmaker.
The time taken from identifying a target for disease intervention to finding a molecule that acts against it is an average of 5.5 years today and limiting that to just one year in future is his goal
"That is a stretch. But as we've learnt more about what modern supercomputers can do, we've gained more confidence," Baldoni told Reuters. "We have an obligation to reduce the cost of drugs and reduce the time it takes to get medicines to patients."
In order to accelerate pre-clinical drug development through use of advanced computational technologies, GSK also entered a collaboration with the U.S. Department of Energy and National Cancer Institute earlier this year.
(Source:www.reuter.com)
Also exploring the potential of artificial intelligence (AI) to help streamline the drug discovery process are other pharmaceutical giants including Merck & Co, Johnson & Johnson and Sanofi.
In order to predict how molecules will behave and how likely they are to make a useful drug, thereby saving time and money on unnecessary tests, harness modern supercomputers and machine learning systems is the aim.
In other high-tech areas such as the development of driverless cars and facial recognition software, AI systems already play a central role.
"Many large pharma companies are starting to realize the potential of this approach and how it can help improve efficiencies," said Andrew Hopkins, chief executive of privately owned Exscientia, which announced the new tie-up with GSK.
Using roughly one-quarter of the time and at one-quarter of the cost of traditional approaches, Exscientia's AI system could deliver drug candidates, said Hopkins, who used to work at Pfizer.
One of a growing number of start-ups on both sides of the Atlantic that are applying AI to drug research is the Scotland-based company, which also signed a deal with Sanofi in May. Britain's BenevolentAI and U.S. firms Berg, Numerate, twoXAR and Atomwise, are also among other companies.
"In pharma's eyes these companies are essentially digital biotechs that they can strike partnerships with and which help feed the pipeline," said Nooman Haque, head of life sciences at Silicon Valley Bank in London.
"If this technology really proves itself, you may start to see M&A with pharma, and closer integration of these AI engines into pharma R&D."
In order to boost R&D productivity, this is not the first time drugmakers have turned to high-tech solutions.
Generating mountains of leads in the early 2000s but notably failed to solve inefficiencies in the research process was the introduction of "high throughput screening", using robots to rapidly test millions of compounds.
Since it is yet to demonstrate it can successfully bring a new molecule from computer screen to lab to clinic and finally to market and that is the common knowledge in the market, big pharma is treading cautiously when it comes to AI.
"It's still to be proven, but we definitely think we should do the experiment," said John Baldoni, GSK's head of platform technology and science.
By hiring some unexpected staff with appropriate computing and data handling experience - including astrophysicists, Baldoni is also ramping up in-house AI investment at the drugmaker.
The time taken from identifying a target for disease intervention to finding a molecule that acts against it is an average of 5.5 years today and limiting that to just one year in future is his goal
"That is a stretch. But as we've learnt more about what modern supercomputers can do, we've gained more confidence," Baldoni told Reuters. "We have an obligation to reduce the cost of drugs and reduce the time it takes to get medicines to patients."
In order to accelerate pre-clinical drug development through use of advanced computational technologies, GSK also entered a collaboration with the U.S. Department of Energy and National Cancer Institute earlier this year.
(Source:www.reuter.com)
