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  • biocomp 1.0.3

    From Edward Montague@21:1/5 to All on Thu Jan 2 06:56:48 2020
    At this stage the most productive approach to using machine learning or artificial intelligence is to determine what drug candidates are being investigated, how, and by who. Then attempt to learn from their discoveries.

    Most of the computational effort may already be done, maybe use PyMol,
    with a plugin to investigate further.

    If anyone here is already using M.L or A.I, perhaps you have some recommendations.

    --- SoupGate-Win32 v1.05
    * Origin: fsxNet Usenet Gateway (21:1/5)
  • From Edward Montague@21:1/5 to Edward Montague on Wed Jan 15 16:24:16 2020
    On Friday, January 3, 2020 at 3:56:49 AM UTC+13, Edward Montague wrote:
    At this stage the most productive approach to using machine learning or artificial intelligence is to determine what drug candidates are being investigated, how, and by who. Then attempt to learn from their discoveries.

    Most of the computational effort may already be done, maybe use PyMol,
    with a plugin to investigate further.

    If anyone here is already using M.L or A.I, perhaps you have some recommendations.



    Just been to the Schrodinger.com website, they're partnering with the pharmaceutical company Bayer, to introduce Machine Learning to the Pymol software. Don't know yet if this will be included in a public
    release of Pymol, very interesting if this was the instance.I'd most likely require an upgrade to my P.C system and to look to cloud computing.


    Schrodinger have Pymol release 4 for 2020 available, you may need to register to obtain this.

    There's other software available, from elsewhere, including Seaview.

    --- SoupGate-Win32 v1.05
    * Origin: fsxNet Usenet Gateway (21:1/5)
  • From Edward Montague@21:1/5 to Edward Montague on Fri Jan 17 15:18:18 2020
    On Friday, January 3, 2020 at 3:56:49 AM UTC+13, Edward Montague wrote:
    At this stage the most productive approach to using machine learning or artificial intelligence is to determine what drug candidates are being investigated, how, and by who. Then attempt to learn from their discoveries.

    Most of the computational effort may already be done, maybe use PyMol,
    with a plugin to investigate further.

    If anyone here is already using M.L or A.I, perhaps you have some recommendations.

    The Broad Institute recently completed a comprehensive cancer
    genome atlas. This may narrow the search for treatments.

    --- SoupGate-Win32 v1.05
    * Origin: fsxNet Usenet Gateway (21:1/5)
  • From Edward Montague@21:1/5 to Edward Montague on Sun Mar 8 05:36:51 2020
    On Friday, January 3, 2020 at 3:56:49 AM UTC+13, Edward Montague wrote:
    At this stage the most productive approach to using machine learning or artificial intelligence is to determine what drug candidates are being investigated, how, and by who. Then attempt to learn from their discoveries.

    Most of the computational effort may already be done, maybe use PyMol,
    with a plugin to investigate further.

    If anyone here is already using M.L or A.I, perhaps you have some recommendations.

    How might we ensure that Super Computers are used for life extension and the results are made available to a more general
    audience, without the requirement of a subscription.
    Drug discovery might be the first application.


    In the recent publication of the magazine Science, mention is
    made of a billion fold speedup of a weather simulation via the
    use of Artificial Intelligence; with good precision.
    I wonder if this might ever be possible for bio computations.

    I often visit Science Daily, Nature. There the article titles
    quite often contain the words 'may' and 'could', I must avoid
    this practice and research topics in more detail.

    --- SoupGate-Win32 v1.05
    * Origin: fsxNet Usenet Gateway (21:1/5)
  • From Edward Montague@21:1/5 to Edward Montague on Mon Mar 16 02:22:01 2020
    On Friday, January 3, 2020 at 3:56:49 AM UTC+13, Edward Montague wrote:
    At this stage the most productive approach to using machine learning or artificial intelligence is to determine what drug candidates are being investigated, how, and by who. Then attempt to learn from their discoveries.

    Most of the computational effort may already be done, maybe use PyMol,
    with a plugin to investigate further.

    If anyone here is already using M.L or A.I, perhaps you have some recommendations.

    I've been reviewing the public announcements of Dr.Howard.M.
    Temin, nobel recipient.
    After consulting with his co recipients, he said that a person
    was more likely to get cancer from smoking or radiation.
    The the smokers in the audience, which included the royals,
    promptly extinguished their cigarettes.

    Prior to this, his article in the Jan 1972 edition of Scientific American portrayed a different possibility, this is what I took note.
    Eventually Temin and Baltimore acknowledged the validity of
    this original interpretation.

    A very basic interpretation then, some viruses cause disruptions to the genome, these are passed down through the
    generations; to be triggered by some internal or external factor leading to cancers.

    --- SoupGate-Win32 v1.05
    * Origin: fsxNet Usenet Gateway (21:1/5)
  • From Edward Montague@21:1/5 to Edward Montague on Mon Apr 6 01:07:25 2020
    On Friday, January 3, 2020 at 3:56:49 AM UTC+13, Edward Montague wrote:
    At this stage the most productive approach to using machine learning or artificial intelligence is to determine what drug candidates are being investigated, how, and by who. Then attempt to learn from their discoveries.

    Most of the computational effort may already be done, maybe use PyMol,
    with a plugin to investigate further.

    If anyone here is already using M.L or A.I, perhaps you have some recommendations.

    Dr Howard.Temin may never of published any material, prior to
    his Nobel prize; hence one may well conclude that he was receiving the prize for his work on viruses.
    A recent article has appeared in Scientific American highlighting the ongoing challenges in understanding the mechanisms of cancer; stating that another 100 years maybe required.
    Yet the vaccine against the Human Papilloma Virus is quite
    effective at reducing the incidence of the associated cancer.

    --- SoupGate-Win32 v1.05
    * Origin: fsxNet Usenet Gateway (21:1/5)