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Related Concept Videos

Drug Discovery: Overview01:26

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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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Drug elimination refers to drug removal from the body, either through urine or bile, by the kidneys or liver, respectively. A pharmacokinetic parameter, drug clearance, measures the efficiency of drug removal from the bloodstream within a specific time frame. It is calculated as the rate at which a drug is eliminated from plasma divided by the drug's concentration in plasma.
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Insights from incorporating quantum computing into drug design workflows.

Bayo Lau1, Prashant S Emani2,3, Jackson Chapman2,3

  • 1HypaHealth, HypaHub Inc., San Jose, CA 95128, USA.

Bioinformatics (Oxford, England)
|December 8, 2022
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Summary
This summary is machine-generated.

HypaCADD integrates quantum machine learning (QML) into classical drug design workflows. This hybrid approach effectively predicts mutation impacts, demonstrating quantum computing

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Area of Science:

  • Computational chemistry
  • Quantum computing applications
  • Drug discovery

Background:

  • Quantum computing (QC) offers theoretical advantages but faces hardware limitations.
  • Near-term QC in computer-aided drug design (CADD) necessitates hybrid classical-quantum approaches.
  • Integrating QC requires careful partitioning of computational tasks.

Purpose of the Study:

  • To develop HypaCADD, a hybrid workflow for protein-ligand binding prediction considering genetic mutations.
  • To identify and implement QC-amenable modules within a CADD pipeline.
  • To assess the performance of quantum machine learning (QML) for mutation impact prediction.

Main Methods:

  • Developed HypaCADD, a hybrid classical-quantum workflow.
  • Combined classical docking and molecular dynamics with QML for mutation impact inference.
  • Mapped a classical neural network module to QC using qubit-rotation gates.
  • Implemented and tested the workflow using simulations and two commercial quantum computers.

Main Results:

  • Identified mutation-impact prediction as a key module for QC integration.
  • QML models achieved performance comparable to or exceeding classical baselines.
  • Demonstrated a case study using SARS-CoV-2 protease and its mutants.
  • Successfully implemented the hybrid workflow on quantum hardware.

Conclusions:

  • HypaCADD presents a viable strategy for leveraging QC in CADD.
  • Hybrid approaches are effective for utilizing near-term quantum hardware.
  • QML shows promise for accelerating drug discovery and personalized medicine.