Artificial Intelligence And Drug Discovery

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Artificial technologies promise to speed up the process of Drug discovery and development and make it cost effective. New drugs are approved through human clinical trials - year long rigorous procedures starting in animal trials and gradually moving to patients. They cost billions and take many years to complete. Also, patients in trials are exposed to side effects that cannot be predicted. And, if the trial is successful, it has to go through a regulatory approval - it may or may not receive nod from the Regulatory agency.
While looking at all technologies, Artificial Intelligence is the most advanced and multifaceted. The technology helps companies aggregate and synthesize information needed for clinical trials, shortening the Drug Development Process. It also helps to understand the mechanisms of disease, establish Biomarkers, Design or Redesign drugs, generate data, models or novel drug candidates, run preclinical experiments, run clinical trials and even analyse the real world experience. The number reflects manifold usage of technology increasing day by day. Of these, following are the seven methods of trials mostly in use-

1)  Turbine AI - The technology models cell biology on the molecular level. It can identify the best
     drug to target a specific Tumor with, moreover it identifies complex biomarkers and design
     combination therapies by performing millions of simulated experiments every day. The
     technology is already in use with Bayer, Cambridge University and top Hungarian groups to
     find new Cancer cures and save the lives of Patients suffering from currently incurable forms of
     lethal disease. The key to Turbine's uniqueness is its molecular model of Cancer biology guided
     by A.I to identify the biomarkers that signal sensitivity to treatment.

2) Cambridge Cancer Genomics (CCG) - This develops Precision Oncology solutions to transform
     the way cancer patients are treated. Using simple Blood draws, CCG reduces the time required
     to know whether a treatment is working, allowing changes in used therapy and reducing
     unnecessary side effects. It can also identify relapse in an average of 7 months earlier than
     the standard practice.

3)  Antidote - U.S and U,K based Antidote is focusing on matching patients and Researchers in
     clinical trials so that they could work easily. It is basically a effective online platform for
    enhancing access to clinical trials. By combining proprietary technologies, Data, and well
    established business models, the company is transforming the way Patients and Researchers
    connect so that breakthroughs can happen faster.

4) Atomwise - The company in drug discovery aims to reduce costs of development of medicine
     by using Supercomputers to predict from Database of Molecular structures in advance which
     potential medicines will work and which won't. Their deep convolutional Neural network,
    Atomnet, screens more than 100 million compounds each day.
    Atomwise launched in 2015, a virtual search for safe existing medicine that could be redesigned
    to treat Ebola Virus. They found two Drug candidates to reduce the Ebola infectivity significantly
    in one day which would have taken months or years to complete.

5) Row Analytics - The platform combines A.I methods and Data Analytics to look at multiple
    genetic variants in combination across a range of diseases. This enables rapid identification of
    new drug candidates and drugs to repurpose. Able to complete the process in weeks instead of
    months even for large disease population.

6) Deep Genomics (Excellence in SILICO Genomics) - It promises to solve the biggest puzzle in
    Genetics - to get to know exact information the Genome can provide for Patients, Professionals
    and Researchers. For this, Deep genomics is upgrading its computational system to help decode
    the meaning of Genome. Its computational system is used to develop Database which provides
    predictions for more than 300 Genetic variations which could effect the Genetic code. Their
    findings are used for Genome- based therapeutic development, molecular diagnostics, targeting
    Biomarker discovery and assessing risk pf genetic disorders.

7) Insilico Medicine - This aims to cover entire process of Drug discovery, Clinical trial analysis
    and Digital medicine. It is pursuing Internal Drug discovery in Cancer, Fibrosis, Parkinson's, and
    Dermatological disease, Diabetes, Aging, Alzheimer's, ALS and Sarcopenia disease. The company
   while working with Researchers at University of Toronto announced that the process of
   development of a new drug candidate lasted 46 days with the help of a smart Algorithm. It took 21
   days to create 30,000 designs for Molecules that target a Protein linked with Fibrosis (Tissue
   Scarring). They synthesized six of these Molecules in the Lab and then tested in the cells; the most
   promising one was tested on Mice. Thy concluded it was potent against the protein and showed
   " Drug-like" qualities.


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