Gregory Kovacs, M.D., Ph.D. (EE) - Chief Technology Officer, SRI International
Professor Emeritus, Stanford University
Person-millenia are spent each year seeking useful molecules for medicine, food, agriculture and other uses. For biomolecules, the near infinite universe of possibilities is staggering and humbling. As an example, antibodies, which make up the majority of the top-grossing medicines today, are comprised of 1,100 amino acids chosen from the twenty used by living things. The binding part (variable region) that allows the antibody to bind and recognize pathogens, is about 110 amino acids, giving rise to 10143 possible combinations. There are only about 1080 atoms in the universe, illustrating the intractability of exploring the entire space of possibility. This is just one example…
Presently, machine learning (ML), artificial intelligence (AI), quantum computing, and “big data” are often put forth as the solutions to all problems, particularly by pontificating TED presenters’ pitches dripping with hyperbole. Expecting these methods to provide intelligent de novo prediction of molecular structure and function within our lifetimes is utter rubbish. For example, a neural network trained on daily weather patterns in Palo Alto cannot develop an internal model for global weather. In a similar way, finite and reasonable molecular training sets will not magically cause a generalizable model of molecular quantum mechanics to arise within a neural network, no matter how many layers it is endowed with. Regardless of the algorithms chosen, one simply cannot yet ask a computer to “compute” a drug that cures HIV.
With that provocative preface, we turn to the notion of letting matter compute itself. Massive combinatorial libraries can now be intelligently and efficiently mined with appropriate molecular readouts (AKA “the question vector”) at ever-increasing throughputs presently surpassing 1012 unique molecules in a few hours. Once “matter-in-the-loop” exploration is embraced, AI, ML and other methods can be brought to bear usefully in closed-loop methods to follow veins of opportunity in molecular space. Several examples of mining massive molecular spaces will be presented, including drug discovery, digital pathology, and AI-guided continuous-flow chemical synthesis – all real, all working today.
- In Campus Calendar
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Yes
- Groups
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3D Systems Packaging Research Center, Georgia Electronic Design Center (GEDC), Institute for Electronics and Nanotechnology, NanoTECH, The Center for MEMS and Microsystems Technologies
- Invited Audience
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Faculty/Staff, Postdoc, Public, Graduate students, Undergraduate students
- Categories
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Seminar/Lecture/Colloquium
- Keywords
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the Institute for Electronics and Nanotechnology, machine learning, quantum computing, molecular readouts, Drug Discovery, electrical engineering, ECE, Materials Science & Engineering, microfluidics, bioengineering
- Status
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- Created By: Christa Ernst
- Workflow Status: Draft
- Created On: May 31, 2018 - 11:02am
- Last Updated: May 31, 2018 - 11:15am
