Nursing

Virtual screening

Biology

Homology modeling

Biochemistry

Data mining

Computer science

Protein structure

Docking (animal)

Computational biology

Drug discovery

Bioinformatics

Protein–ligand docking

Enzyme

Artificial intelligence

Protein structure prediction

Medicine

Chemistry

Machine learning

iScience

A crucial component in structure-based drug discovery is the availability of high-quality three-dimensional structures of the protein target. Whenever experimental structures were not available, homology modeling has been, so far, the method of choice. Recently, AlphaFold (AF), an artificial-intelligence-based protein structure prediction method, has shown impressive results in terms of model accuracy. This outstanding success prompted us to evaluate how accurate AF models are from the perspective of docking-based drug discovery. We compared the high-throughput docking (HTD) performance of AF models with their corresponding experimental PDB structures using a benchmark set of 22 targets. The AF models showed consistently worse performance using four docking programs and two consensus techniques. Although AlphaFold shows a remarkable ability to predict protein architecture, this might not be enough to guarantee that AF models can be reliably used for HTD, and post-modeling refinement strategies might be key to increase the chances of success.

How good are AlphaFold models for docking-based virtual screening?