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Small molecule docking in YASARA

YASARA Structure provides everything you need to dock ligands with proteins, nucleic acids and other structures, these are the main features:

AutoDock
Figure 1: Docking of an inhibitor built around a cyclic urea core against HIV protease (PDB ID 1HVR)
Docking Result Player
Figure 2: YASARA's interactive docking result player, which allows to easily visualize different interactions (H-bonds, hydrophobic, ionic, pi-pi and cation-pi) and docking clusters.
  • Docking at the touch of a button: select ligand, receptor and go.
  • Possibility to interactively place the simulation cell around the active site to focus docking on the most important region.
  • Possibility to interactively fix certain internal degrees of freedom of the ligand to perform anything from rigid to flexible docking (AutoDock and VINA).
  • Automatic typing of ligands, assignment of pH dependent bond orders and hydrogen atoms.
  • Automatic ligand structure analysis to determine the core fragment and its flexible attachments (AutoDock and VINA).
  • Consideration of receptor flexibility via automatic generation of a receptor ensemble with alternative high-scoring solutions of the side-chain rotamer network.
  • Keep selected active-site residues flexible during docking.
  • Parallel docking: make full use of today's multi core CPUs by docking on all your cores in parallel (in Windows maximally 32 cores).
  • Virtual screening: Dock libraries with thousands of ligands automatically.
  • Covalent docking: if the ligand forms a covalent bond with a known receptor atom, this is handled automatically using AutoDock's flexible side-chain approach.
  • Interruptible docking: run on your notebook, exit YASARA and continue docking next day.
  • Easy result analysis: concise docking report, all ligand conformers superposed and sorted by binding energy, interactive docking result visualization.
AutoDock
Figure 1: Docking of an inhibitor built around a cyclic urea core against HIV protease (PDB ID 1HVR)
For the actual docking procedure, YASARA currently includes four engines:

Approach 1: Autodock

AutoDock is a highly cited docking program developed at the Scripps Research Institute by Dr. Garrett M. Morris et al. [1]. YASARA employs semi-empirical QM calculations to assign high-quality AutoSMILES charges, which are further tuned for maximum compatibility with the AutoDock scoring function.

Approach 2: AutoDockGPU

A rewrite of AutoDock from the Forli lab at Scripps Research Institute[2], that leverages the power of today's GPU and makes docking lightning fast, which is especially helpful for virtual screening.

Approach 3: VINA

VINA (Vina Is Not Autodock) has also been developed at the Scripps Research Institute, by different authors, Dr. Oleg Trott and Dr. Arthur J. Olson [3]. It is tightly related to the original AutoDock, so everything written above also applies to VINA, and additionally it can dock multiple ligands together.

Approach 4: Boltz-2

Boltz-2, being a remake of AlphaFold 3 developed at the MIT[4], this AI model combines docking with structure prediction. The receptor structure is thus predicted as part of the docking run, it is no longer needed to either dock against a rigid receptor or select a few side-chains to be kept flexible. The binding energies are also predicted with an AI model.


Docking Result Player
Figure 2: YASARA's interactive docking result player, which allows to easily visualize different interactions (H-bonds, hydrophobic, ionic, pi-pi and cation-pi) and docking clusters.

R E F E R E N C E S

[1] Automated Docking Using a Lamarckian Genetic Algorithm and and Empirical Binding Free Energy Function
Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK and Olson AJ (1998), J.Comput.Chem. 19,1639-1662
[2] Accelerating AutoDock4 with GPUs and gradient-based local search
Santos-Martins D, Solis-Vasquez L, Tillack AF, Sanner MF, Koch A, Forli S (2021), J.Chem.Theory Comput. 17,1060–1073
[3] AutoDock VINA: improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading
Trott O, Olson AJ (2010), J.Comput.Chem. 31, 455-461
[4] Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction
Passaro S, Corso G, Wohlwend J, Reveiz M, Thaler S, Somnath VR, Getz N, Portnoi T, Roy J, Stark H, Kwabi-Addo D, Beaini D, Jaakkola T, Barzilay R (2025) bioRxiv 2025.06.14.659707v1