Machine learning and molecular simulation based methods for enhancing binding and activity prediction

A machine learning model and technology for simulating data, applied in the field of machine learning, can solve problems such as futility

Pending Publication Date: 2021-01-08
THE BOARD OF TRUSTEES OF THE LELAND STANFORD JUNIOR UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Over the past century, medicinal chemists have tried in vain to synthesize opioid pain relievers without dependence problems

Method used

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  • Machine learning and molecular simulation based methods for enhancing binding and activity prediction
  • Machine learning and molecular simulation based methods for enhancing binding and activity prediction
  • Machine learning and molecular simulation based methods for enhancing binding and activity prediction

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0094] The following examples are provided to illustrate the claimed invention, but not to limit the claimed invention.

[0095]

[0096]

[0097]

[0098]

[0099]

Embodiment 2

[0101] A backbone split was defined where (1) agonist ligands with a Tanimoto score ≤0.5 compared to fentanyl were put into the training set, and (2) agonist ligands with a Tanimoto score ≥0.7 compared to fentanyl into the test set, and (3) antagonists are randomly distributed between the training and test sets.

[0102] a)

[0103] Fentanyl analog ligands (test set):

[0104] ['acetylfentanyl', 'propylenefentanyl', '3-allylfentanyl', 'alpha-methylthiofentanyl', 'aziridine', 'beta-hydroxyfentanyl' , 'β-hydroxythiofentanyl', 'butyrylfentanyl', 'carfentanyl', 'norprudin', 'dienprotine', 'fentanyl', '4-fluorobutyrylfentanyl' fentanyl', 'furylfentanyl', 'lofentanyl', '4-methoxybutyrylfentanyl', 'α-methylacetylfentanyl', '3-methylbutyrylfentanyl', 'n-methylcarbfentanyl', '3-methylfentanyl', 'β-methylfentanyl', '3-methylthiofentanyl', 'Ofentanyl', ' hydroxymethylfentanyl', 'p-flufentanyl', 'pepap', 'phenacramine', 'phenaridine', '4-phenylfentanyl', 'predidine', 'prodilidine' Ru...

Embodiment 3

[0119]The Random Forest average Gini impurity reduction ("importance") for each feature (MD state, crystal structure) was used to a) distinguish opioid agonists from antagonists, and b) distinguish opiates from μOR conjugates and nonconjugates.

[0120] a)

[0121] inactive crystals 0.063488 active crystal 0.01358 state 14 0.033463 state 12 0.013346 state 3 0.031175 state 6 0.012534 state 17 0.02995 state 2 0.012306 state 10 0.029853 state 1 0.012289 state 23 0.025154 state 20 0.01123 state 5 0.024361 state 16 0.023912 state 21 0.023884 state 4 0.021384 state 22 0.020618 state 0 0.019934 state 13 0.01972 state 18 0.017975 state 7 0.017955 state 24 0.017434 state 11 0.017295 state 9 0.01617 state 8 0.015486 state 15 0.015193 state 19 0.013673

[0122] b)

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Abstract

Systems and methods for molecular simulation in accordance with embodiments of the invention are illustrated. One embodiment includes a method for predicting a relationship between a ligand and a receptor. The method includes steps for identifying a plurality of conformations of a receptor, computing docking scores for each of the plurality of conformations and a set of one or more ligands, and predicting a relationship between the set of one or more ligands and the plurality of conformations of the receptor.

Description

[0001] Cross References to Related Applications [0002] This application is based upon 35 U.S.C § 119(e) requirements of U.S. Provisional Patent Application No. 62 / 638,805, filed March 5, 2018, entitled "Methods for Enhanced Binding and Activity Prediction Based on Machine Learning and Molecular Simulation" priority. The disclosure of US Provisional Patent Application No. 62 / 638,805 is hereby incorporated by reference in its entirety for all purposes. technical field [0003] The present invention relates generally to machine learning methods, and more particularly to the use of machine learning in molecular simulations. Background technique [0004] One class of proteins, the G-protein-coupled receptors (GPCRs), contains the targets of more than one-third of all FDA-approved drugs. One such GPCR, the μ opioid receptor (μOR), embodies the strengths and weaknesses of existing GPCR drugs. Opioid chronic pain relievers such as morphine and hydrocodone are μOR agonists that ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N33/68G01N33/74
CPCG16B15/30G16B40/20G16B40/30G06F30/20G06F2111/08G06N20/20G16C20/64G16C20/70G06N5/01G16C20/30G16B5/00G06N20/00
Inventor E·N·费恩伯格V·S·潘德
Owner THE BOARD OF TRUSTEES OF THE LELAND STANFORD JUNIOR UNIV
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