Estimates of ligand-binding affinities supported by quantum mechanical methods

Area of Science:

• Computational Chemistry
• Molecular Modeling
• Drug Discovery

Background:

• Accurate prediction of drug-receptor binding affinity is crucial for drug discovery.
• Traditional molecular mechanics methods have limitations in capturing complex interactions.
• Quantum mechanical (QM) methods offer higher accuracy but are computationally expensive.

Purpose of the Study:

• To review efforts in using QM methods to improve free-energy estimates of drug-receptor binding.
• To evaluate the impact of solvation models, conformational changes, and electrostatic interactions on binding affinity predictions.
• To develop and assess advanced QM-based methods for more accurate binding energy calculations.

Main Methods:

• Testing various implicit solvation models for ligand-binding affinity predictions.
• Studying the conformational dependence of QM charges and their influence on binding.
• Estimating electrostatic interactions beyond point-charge models.
• Developing QM-based molecular mechanics potentials.
• Introducing the PMISP (polarised multipole interaction with supermolecular pairs) method.

Main Results:

• Implicit solvation models show comparable accuracy (2-5 kJ/mol) for relative energies within similar molecules.
• Conformational dependence of QM charges is largely offset by solvation energies.
• Multipoles and anisotropic polarisabilities significantly influence electrostatic interactions within 10-15 Å.
• A new QM-based potential struggled to reduce errors below 2-3 kJ/mol per interaction.
• The PMISP method achieved an error of 5-10 kJ/mol for protein-ligand complexes, surpassing current molecular mechanics.

Conclusions:

• QM methods significantly contribute to improving binding free-energy estimates.
• The PMISP method represents a substantial advancement in accurately calculating protein-ligand interaction energies.
• Further research is needed to address remaining challenges and achieve greater accuracy in predicting binding affinities.