US2018238869A1PendingUtilityA1
Uses of systems with degrees of freedom poised between fully quantum and fully classical states
Est. expiryJul 26, 2030(~4 yrs left)· nominal 20-yr term from priority
G01N 33/54366C40B 30/02G06F 19/706G06N 99/002G01N 2500/20G01N 2500/04B82Y 10/00G06N 10/70G06N 10/20G16C 20/64G16C 20/50G16B 35/00G16C 20/60
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Claims
Abstract
Disclosed herein are systems and uses of systems operating between fully quantum coherent and fully classical states. Such systems operate in what is termed the “Poised realm” and exhibit unique behaviors that can be applied to a number of useful applications. Non-limiting examples include drug discovery, computers, and artificial intelligence
Claims
exact text as granted — not AI-modifiedWhat is claimed is:
1 . A method of drug discovery, comprising:
selecting a biological target; screening a library of candidate molecules by conducting an in vitro binding assay to identify a first subset of candidate molecules that bind to the biological target; determining the energy level spacing distribution of a quantum degree of freedom in each of the candidate molecules in the first subset by evaluating their mean energy level spacing as a function of energy; comparing the energy level spacing distribution to at least one pre-determined reference function to determine a degree of similarity of the energy level spacing distribution and the reference function; and selecting a second subset of molecules from the first subset as drug candidates for drug development or medicinal chemistry based on the comparison, wherein the second subset of molecules are more likely to contain biologically active molecules than the first subset of molecules.
2 . The method of claim 1 , wherein the biological target is an enzyme.
3 . The method of claim 1 , wherein the biological target is a receptor.
4 . The method of claim 3 , wherein the receptor is a cell-surface receptor.
5 . The method of claim 1 , wherein determining the energy level spacing distribution comprises spectroscopically determining the energy level spacings of each candidate molecule in the first subset.
6 . The method of claim 5 , wherein the energy level spacings are determined using ultraviolet/visible spectroscopy.
7 . The method of claim 5 , wherein the energy level spacings are determined using infrared spectroscopy.
8 . The method of claim 5 , wherein the energy level spacings are determined using X-ray spectroscopy.
9 . The method of claim 5 , wherein the energy level spacings are determined using nuclear magnetic resonance spectroscopy.
10 . The method of claim 5 , wherein the energy level spacings are determined using electron paramagnetic resonance spectroscopy.
11 . The method of claim 1 , wherein determining the energy level spacing distribution comprises computationally modeling the energy levels.
12 . The method of claim 1 , wherein the one or more reference functions include a function having the form:
p ( s )=4 s exp(−2 s )
where s is the energy level spacing and p(s) is the energy level spacing distribution.
13 . The method of claim 1 , wherein the one or more reference functions include a function having the form:
p
(
s
)
=
π
s
2
exp
(
-
π
s
2
/
4
)
where s is the energy level spacing and p(s) is the energy level spacing distribution.
14 . The method of claim 1 , wherein the one or more reference functions include a function having the form:
p ( s )=exp(− s )
where s is the energy level spacing and p(s) is the energy level spacing distribution.
15 . The method of claim 1 , wherein comparing the energy level spacing distribution to at least one pre-determined reference function comprises determining the quantity:
x
=
A
-
A
p
A
w
-
A
p
wherein
A
p
=
∫
2
∞
p
p
(
s
)
,
A
w
=
∫
2
∞
p
w
(
s
)
,
and
A
=
∫
2
∞
p
(
s
)
,
where
:
p
p
(
s
)
=
exp
(
-
s
)
and
p
w
(
s
)
=
π
s
2
exp
(
-
π
s
2
/
4
)
,
wherein s is energy level spacing and p(s) is the determined energy level spacing distribution.
16 . The method of claim 15 , wherein selecting a second subset of molecules from the first subset as drug candidates comprises selecting those molecules having an x value within a predetermined distance from a predetermined value.
17 . The method of claim 1 , wherein comparing the energy level spacing distribution to at least one pre-determined reference function comprises fitting the determined energy level spacing distributions to a pre-determined function.
18 . The method of claim 17 , wherein selecting a second subset of molecules from the first subset as drug candidates comprises selecting those molecules whose energy level spacing distribution fits the pre-determined function.
19 . The method of claim 1 , further comprising conducting an in vitro or in vivo assay on each drug candidate to test for biological activity.
20 . A method of drug discovery, comprising:
selecting a biological target; screening a library of candidate molecules by conducting an in vitro binding assay to identify a first subset of candidate molecules that bind to the biological target; determining the energy level spacing distribution of a quantum degree of freedom in each of the candidate molecules in the first subset by evaluating their mean energy level spacing as a function of energy; conducting an in vitro or in vivo assay for biological activity on each of the candidate molecules in the first subset; correlating the energy level spacing distribution with activity determined from the in vitro or in vivo assay; determining the energy level spacing distributions of a quantum degree of freedom in a new set of candidate molecules by evaluating their mean energy level spacing as a function of energy; comparing the energy level spacing distributions of the new set of candidate molecules with energy level spacing distributions that correlate with biological activity to determine their degree of similarity; and selecting as drug candidates from the new set of candidate molecules for drug development or medicinal chemistry those molecules whose energy level spacing distributions exhibit a pre-determined level of similarity to the energy level spacing distributions that correlate with biological activity, wherein the selected drug candidates are more likely to contain biologically active molecules than non-selected molecules from the new set of candidate molecules.
21 . The method of claim 20 , wherein comparing the energy level spacing distributions of the new set of candidate molecules with energy level spacing distributions that correlate with biological activity comprises using a computational fitting algorithm.
22 . The method of claim 21 , where the fitting algorithm comprises least squares analysis.
23 . A method of drug discovery, comprising:
selecting a biological target; screening a library of candidate molecules by conducting an in vitro binding assay to identify a first subset of candidate molecules that bind to the biological target; measuring decoherence decay of a quantum degree of freedom in each of the candidate molecules in the first subset; comparing the decoherence decay to at least one pre-determined reference function to determine a degree of similarity of the decoherence decay and the reference function; and selecting a second subset of molecules from the first subset as drug candidates for drug development or medicinal chemistry based on the comparison, wherein the second subset of molecules are more likely to contain biologically active molecules than the first subset of molecules.
24 . The method of claim 23 , wherein the reference function has the form:
S ( T H )˜exp(− T H /T D )/ T H α
wherein S(T H ) is a coherence signal as a function of time T H and T D and α are fitting parameters.
25 . The method of claim 24 , wherein selecting the second subset of molecules comprises selecting those molecules having the lowest T D value.
26 . The method of claim 23 , wherein measuring decoherence decay comprises performing a spin echo experiment.
27 . The method of claim 23 , wherein measuring decoherence decay comprises performing a photon echo experiment.
28 . A method of drug discovery, comprising:
selecting a biological target; screening a library of candidate molecules by conducting an in vitro binding assay to identify a first subset of candidate molecules that bind to the biological target; measuring decoherence decay of a quantum degree of freedom in each of the candidate molecules in the first subset; conducting an in vitro or in vivo assay for biological activity on each of the candidate molecules in the first subset; correlating the decoherence decay with activity determined from the in vitro or in vivo assay; measuring decoherence decay of a quantum degree of freedom in a new set of candidate molecules; comparing the decoherence decay of the new set of candidate molecules with the decoherence decay that correlate with biological activity to determine a degree of similarity of the decoherence decay of the new set of candidate molecules and the decoherence decay that correlate with biological activity; and selecting as drug candidates from the new set of candidate molecules for drug development or medicinal chemistry those molecules whose decoherence decay exhibit a pre-determined level of similarity to the decoherence decay that correlate with biological activity, wherein the selected drug candidates are more likely to contain biologically active molecules than non-selected molecules from the new set of candidate molecules.Cited by (0)
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