incorporate protein flexibility during binding has been attempted, but they incorporate receptor flexibility only to a limited extent, focusing on sampling only most
plausible/relevant portion of the conformational space, e.g., through side chain
flexibility, conformational relaxation, and multiple structure docking, as already
discussed in protein flexibility section. However, newer techniques, e.g., supervised
molecular dynamics (SuMD) can be useful to incorporate receptor flexibility,
because they allow receptor to experience thermal fluctuation and supervision of
ligand toward binding site from unbound state might allow receptor to adopt
induced conformational changes sensing the ligand in vicinity of binding site under
influence of its interaction field [225].
6 Binding Ability and Free Energy Calculation
The binding free energy of ligand to receptor is the thermodynamic signature of the
interaction affinity. Therefore, accurate prediction of binding free energy has been
attempted from long times. The free energy calculation methods can be grouped
into relative binding free energy calculation methods and absolute binding free
energy methods [226]. Relative binding free energy methods aim to calculate
Table 11 A brief summary of major programs for small-molecule conformation generation
Program
Type
Algorithm
Cost/license References
Balloon_GA Stochastic Genetic algorithm
Free/
proprietary
[216]
CAESAR
Systematic Incremental search of torsion angles
combined with distance geometry
Commercial [217]
Confgen
Stochastic Random walk on energy surface
Commercial [218]
Confab
Systematic Torsion driving approach
Open
source
[219]
Corina
Systematic Knowledge-based rules derived from
CSD
Commercial [220]
ETKDG
Stochastic Distance geometry and knowledge
base
Open
source
[221]
Frog2
Stochastic Monte Carlo
Open
source
[222]
MS-Dock
Systematic Brute force, anchor, and grow
Open
source
[223]
MOE
Stochastic Random perturbations of rotatable
bonds in increments biased around
30°
Commercial [108]
OMEGA
Systematic Knowledge-based, complete
enumeration
Commercial [212]
RDKit
Stochastic Distance geometry
Open
source
[224]
In Silico Structure-Based Prediction of Receptor–Ligand Binding …
151
plausible/relevant portion of the conformational space, e.g., through side chain
flexibility, conformational relaxation, and multiple structure docking, as already
discussed in protein flexibility section. However, newer techniques, e.g., supervised
molecular dynamics (SuMD) can be useful to incorporate receptor flexibility,
because they allow receptor to experience thermal fluctuation and supervision of
ligand toward binding site from unbound state might allow receptor to adopt
induced conformational changes sensing the ligand in vicinity of binding site under
influence of its interaction field [225].
6 Binding Ability and Free Energy Calculation
The binding free energy of ligand to receptor is the thermodynamic signature of the
interaction affinity. Therefore, accurate prediction of binding free energy has been
attempted from long times. The free energy calculation methods can be grouped
into relative binding free energy calculation methods and absolute binding free
energy methods [226]. Relative binding free energy methods aim to calculate
Table 11 A brief summary of major programs for small-molecule conformation generation
Program
Type
Algorithm
Cost/license References
Balloon_GA Stochastic Genetic algorithm
Free/
proprietary
[216]
CAESAR
Systematic Incremental search of torsion angles
combined with distance geometry
Commercial [217]
Confgen
Stochastic Random walk on energy surface
Commercial [218]
Confab
Systematic Torsion driving approach
Open
source
[219]
Corina
Systematic Knowledge-based rules derived from
CSD
Commercial [220]
ETKDG
Stochastic Distance geometry and knowledge
base
Open
source
[221]
Frog2
Stochastic Monte Carlo
Open
source
[222]
MS-Dock
Systematic Brute force, anchor, and grow
Open
source
[223]
MOE
Stochastic Random perturbations of rotatable
bonds in increments biased around
30°
Commercial [108]
OMEGA
Systematic Knowledge-based, complete
enumeration
Commercial [212]
RDKit
Stochastic Distance geometry
Open
source
[224]
In Silico Structure-Based Prediction of Receptor–Ligand Binding …
151