  As COVID-19 enters its fifth year, it continues to pose a significant global
health threat, with the constantly mutating SARS-CoV-2 virus challenging drug
effectiveness. A comprehensive understanding of virus-drug interactions is
essential for predicting and improving drug effectiveness, especially in
combating drug resistance during the pandemic. In response, the Path Laplacian
Transformer-based Prospective Analysis Framework (PLFormer-PAF) has been
proposed, integrating historical data analysis and predictive modeling
strategies. This dual-strategy approach utilizes path topology to transform
protein-ligand complexes into topological sequences, enabling the use of
advanced large language models for analyzing protein-ligand interactions and
enhancing its reliability with factual insights garnered from historical data.
It has shown unparalleled performance in predicting binding affinity tasks
across various benchmarks, including specific evaluations related to
SARS-CoV-2, and assesses the impact of virus mutations on drug efficacy,
offering crucial insights into potential drug resistance. The predictions align
with observed mutation patterns in SARS-CoV-2, indicating that the widespread
use of the Pfizer drug has lead to viral evolution and reduced drug efficacy.
PLFormer-PAF's capabilities extend beyond identifying drug-resistant strains,
positioning it as a key tool in drug discovery research and the development of
new therapeutic strategies against fast-mutating viruses like COVID-19.
