Challenges and opportunities for applying quantum computers to drug design

ORAL

Abstract

The current limitations of classical computing methods in accurately describing quantum systems hinder the application of quantum chemistry to drug design. More precise computations could replace many labor-intensive experiments. Quantum computations could offer key insights into chemical systems, justifying high computational costs in an industrial setting. However, to significantly impact the pharmaceutical industry, quantum computers must address a broader set of problems, including those involving large protein structures. Significant advancements in hardware and quantum algorithms have reduced computational costs over the years, sparking optimism for the future use of quantum computing in quantum chemistry. However, harnessing the full potential of quantum computing in the pharmaceutical industry requires further improvements in hardware and novel algorithms. We will discuss these challenges and discuss several routes to achieve these goals and progress these challenges. Open research integrating academia and industry will help make quantum computing an essential tool for designing better drugs faster.

The current limitations of classical computing methods in accurately describing quantum systems hinder the application of quantum chemistry to drug design. More precise computations could replace many labor-intensive experiments, provided the computational cost is lower. Quantum computations could offer key insights into chemical systems, justifying high computational costs in an industrial setting. However, to significantly impact the pharmaceutical industry, quantum computers must address a broader set of problems, including those involving large protein structures. New methods that balance accuracy and time on quantum computers could be beneficial. Significant advancements in hardware and quantum algorithms have reduced computational costs over the years, sparking optimism for the future use of quantum computing in quantum chemistry. However, harnessing the full potential of quantum computing in the pharmaceutical industry requires further improvements in hardware and novel algorithms. We will discuss these challenges and explore several potential routes to achieve these goals.

Publication: Santagati, R. et al. Drug design on quantum computers. Arxiv (2023) doi:10.48550/arxiv.2301.04114 https://arxiv.org/abs/2301.04114

Presenters

  • Raffaele Santagati

    • Boehringer-Ingelheim Quantum Lab
    • Boehringer Ingelheim

Authors

  • Raffaele Santagati

    • Boehringer-Ingelheim Quantum Lab
    • Boehringer Ingelheim
  • Alán Aspuru-Guzik

    • University of Toronto
  • Ryan Babbush

    • Google LLC
    • Google
    • Google Quantum AI
  • Matthias Degroote

    • Boehringer Ingelheim Pharm Inc
  • Leticia Gonzalez

    • University of Vienna
  • Elica Kyoseva

    • Boehringer-Ingelheim
  • Nikolaj Moll

    • Boehringer Ingelheim
  • Markus Oppel

    • University of Vienna
  • Robert M Parrish

    • QC WARE
    • QC Ware Cooperation
    • QC Ware
    • QC Ware Corporation
  • Nicholas C Rubin

    • Google
    • Google Quantum AI
  • Michael Streif

    • Boehringer Ingelheim
    • Boehringer Ingelheim Quantum Lab
  • Christofer Tautermann

    • Boehringer Ingelheim
  • Horst Weiss

    • BASF
  • Horst Weiss

    • BASF
  • Nathan Wiebe

    • University of Toronto
  • Clemens Utschig-Utschig

    • Boehringer Ingelheim