Insights from incorporating quantum computing into drug design workflows.

ABSTRACT:

Motivation

While many quantum computing (QC) methods promise theoretical advantages over classical counterparts, quantum hardware remains limited. Exploiting near-term QC in computer-aided drug design (CADD) thus requires judicious partitioning between classical and quantum calculations.

Results

We present HypaCADD, a hybrid classical-quantum workflow for finding ligands binding to proteins, while accounting for genetic mutations. We explicitly identify modules of our drug-design workflow currently amenable to replacement by QC: non-intuitively, we identify the mutation-impact predictor as the best candidate. HypaCADD thus combines classical docking and molecular dynamics with quantum machine learning (QML) to infer the impact of mutations. We present a case study with the c

SUBMITTER: Lau B 

PROVIDER: S-EPMC9825754 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature