Method for screening of target-based drugs through numerical inversion of quantitative structure-(drug)performance relationships and molecular dynamics simulation

Patent metadata

Field	Value
Publication number	US-2020342960-A1
Application number	US-201716628976-A
Country	US
Kind code	A1
Filing date	Jul 6, 2017
Priority date	Jul 6, 2017
Publication date	Oct 29, 2020
Grant date	—

Abstract

Disclosed is a target-based drug screening method using inverse quantitative structure-(drug)performance relationships (QSPR) analysis and molecular dynamics simulation. The method includes modeling a molecular structure of a test compound group against a target molecule, obtaining a quantitative structure-(drug)performance relationships (QSPR) of the test compound group, acquiring the optimal pharmacophore of a novel target-based drug through a numerical inversion of the QSPR, and selecting drug candidates having a molecular structure similar to the optimum pharmacophore from the test compound group.

First claim

What is claimed is:

1 . A target-based drug screening method using inverse quantitative structure-performance relationships analysis and molecular dynamics simulation, the method comprising:
a molecular structure modeling step of modeling a molecular structure of a test compound group against a target molecule; a quantitative structure-performance relationships (QSPR) model creation step of obtaining a quantitative structure-performance relationships of the test compound group; an optimum pharmacophore acquisition step of acquiring an optimum pharmacophore of a novel drug through a numerical inversion of the quantitative structure-performance relationships (QSPR); and a target-based drug candidate group screening step of selecting drug candidates having a molecular structure similar to the optimum pharmacophore.

2 . The method according to claim 1 , wherein the molecular structure modeling step comprises:
a compound selection process of selecting the test compound group; a data reception process of receiving biological experimental data and chemical experimental data of the test compound group; and a molecular structure modeling process of optimizing the molecular structure of the test compound group on the basis of the experimental data through a modeling method.

3 . The method according to claim 1 , wherein the quantitative structure-performance relationships model creation step comprises:
a molecular descriptor calculation process of producing molecular descriptors from the molecular structure; and a quantitative structure-performance relationships modeling process of modeling the quantitative structure-performance relationships on the basis of the molecular descriptors.

4 . The method according to claim 3 , wherein in the quantitative structure-performance relationships (QSPR), the performance comprises one or more performances selected from among biological activity, inhibitory activity, lipophilicity, toxicity, metabolic stability and blood-brain barrier permeability.

5 . The method according to claim 3 , wherein the quantitative structure-performance modeling process selects one or more molecular descriptors from among the produced molecular descriptors by using a genetic algorithm and models the quantitative structure-performance by using the selected molecular descriptors.

6 . The method according to claim 1 , wherein the optimum pharmacophore acquisition step acquires the optimum pharmacophore of the novel drug through a numerical inversion process according to Expression 1 or Expression 2,
x *=arg max log {circumflex over (k)} w s.t log {circumflex over (k)} w =C{circumflex over (t)} {circumflex over (t)}=Px {circumflex over (t)} T S t −1 t≤c 1 ∥P{circumflex over (t)}−x∥≤c 2   Expression 1
where x is a vector of molecular descriptors of a novel drug candidate, x* is a vector of molecular descriptors of an optimal drug calculated from a mathematical programming formula of Expression 1, C is an output variable loading matrix of partial least squares (PLS), t is a score vector of input variables (being molecular descriptors x herein), P is a loading matrix of PLS, {circumflex over ( )} is a prediction value produced by a PLS model, S t is a sample covariance matrix of t, and c 1 and c 2 are appropriate constants, and

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Assignees

• Nat Univ Pukyong Ind Univ Coop Found

Inventors

• Liu Jay
• Yi Myung Gi
• Zuvela Petar

Classifications

• G16C20/64

  Screening of libraries · CPC title

• G16C20/30

  Prediction of properties of chemical compounds, compositions or mixtures · CPC title

• G16C10/00

  Computational theoretical chemistry, i.e. ICT specially adapted for theoretical aspects of quantum chemistry, molecular mechanics, molecular dynamics or the like · CPC title

• G16C20/50 Primary

  Molecular design, e.g. of drugs · CPC title

• G16C20/20

  Identification of molecular entities, parts thereof or of chemical compositions · CPC title