It usually takes a drug around ten years to
reach the market since its initial discovery. The initial clinical trials take
six to seven years on an average. The average cost of drug discovery is around
$2.6 billion. These figures leave nothing for me to explain regarding the
difficulties of drug discovery. However, the situation is changing and we are
entering an era when these hardships could be history.
A Hongkong based pharmaceutical company called
Insilico Medicine worked with the researchers from Toronto university to design
30,000 designs for molecules that can challenge a protein related to fibrosis
in September of 2019. It took them 21 days. Then they isolated six molecular
structures and then picked two from those. The most potent molecule was tested
on a rat with favorable results. The most exciting part of this story is that
the whole process took no more than 46 days. Yes, we are talking about the
conduction of six to seven years worth of research and development in 46 days.
This miraculous feat was realized with the help of an AI driven system called
Generative Tensorial Reinforcement Learning or GTRL.
Integrating AI in the drug discovery process
The kind of research that was referred to in
the last section is still not second nature to the pharmaceutical industry.
Things in this industry are more complicated than just inventing a new tool and
solving the problem forever. Nevertheless, innovative steps are being taken
quite consistently. Various technologies used in the process of drug design are
being infused with artificial intelligence.
Augmenting the information engine
The information engine is a system used by
researchers. This gathers and stores data from different sources, data that is
relevant to the efforts of drug discovery and pharmaceutical research. The data
that is collected includes everything from research works done in different
universities to patents owned by other big companies. The information
aggregated by the information engine can be sorted and processed faster with
the help of AI.
Speeding up research
AI based systems can work directly with the
molecular structures of drug candidates. Finding the appropriate target (that
is the disease which can be cured with the drug), locating pathways for
repurpusing (using an already existing compound for a different outcome) an
existing drug, all of these can be brought to speed.
Fighting the Corona crisis with AI
Google deepmind has already used its AI
systems to identify six protein structures that may have constructed the
capsule outside the COVID-19 virus. Although this information is hypothetical
still it can be crucial in finding a cure. Insilico Medicine has found
molecular structures that can break the protein on the COVID-19. Germany based
startup Innoplexus has developed a molecule that has binding quality against a
protein on the virus. Other startups are trying to find ways of repurpusing
already existing drugs to fight Corona. This is a real test for the AI based drug
discovery startups, which have been heavily funded in recent times and employ
more than 10,000 personnel world wide. It is also an awakening for the AI
enthusiasts to undergo an applied artificial
intelligence course and join the cohorts of this
burgeoning industry.
0 Comments