Computational Chemistry for Drug Design

About our group

We develop efficient computational chemistry methods and apply them to the study of biomolecules. By combining semiempirical quantum chemical calculations and machine learning techniques, we are able to compute large systems containing thousands of atoms with unprecedented accuracy. This work is also supported by accurate benchmark calculations and the study of the fundamental mechanisms of non-covalent interactions.

We focus on the calculation of the binding affinity of protein-ligand complexes, a quantity crucial for computer-aided drug design. Our methodology combines the unique advantages and accuracy of quantum mechanical calculations with the efficiency required for applications to real-world problems.

The group collaborates with biochemists and medicinal chemists at IOCB as well as with leading pharmaceutical companies on practical applications of our methodology.

SQM2.20: Semiempirical quantum-mechanical scoring function yields DFT-quality protein–ligand binding affinity predictions in minutes

A. Pecina J. Fanfrlík M. Lepšík J. Řezáč

Nature Communications 15: 1127 (2024)

Accurate estimation of protein–ligand binding affinity is the cornerstone of computer-aided drug design. We present a universal physics-based scoring function, named SQM2.20, addressing key terms of binding free energy using semiempirical quantum-mechanical computational methods. SQM2.20 incorporates the latest methodological advances while remaining computationally efficient even for systems with thousands of atoms. To validate it rigorously, we have compiled and made available the PL-REX benchmark dataset consisting of high-resolution crystal structures and reliable experimental affinities for ten diverse protein targets. Comparative assessments demonstrate that SQM2.20 outperforms other scoring methods and reaches a level of accuracy similar to much more expensive DFT calculations. In the PL-REX dataset, it achieves excellent correlation with experimental data (average R2 = 0.69) and exhibits consistent performance across all targets. In contrast to DFT, SQM2.20 provides affinity…

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