Combining de novo molecular design with semiempirical protein-ligand binding free energy calculation.

Autor:	Iff M; ETH Zurich, Department of Chemistry and Applied Biosciences Vladimir-Prelog-Weg 4 8093 Zurich Switzerland gisbert@ethz.ch., Atz K; ETH Zurich, Department of Chemistry and Applied Biosciences Vladimir-Prelog-Weg 4 8093 Zurich Switzerland gisbert@ethz.ch., Isert C; ETH Zurich, Department of Chemistry and Applied Biosciences Vladimir-Prelog-Weg 4 8093 Zurich Switzerland gisbert@ethz.ch., Pachon-Angona I; ETH Zurich, Department of Chemistry and Applied Biosciences Vladimir-Prelog-Weg 4 8093 Zurich Switzerland gisbert@ethz.ch., Cotos L; ETH Zurich, Department of Chemistry and Applied Biosciences Vladimir-Prelog-Weg 4 8093 Zurich Switzerland gisbert@ethz.ch., Hilleke M; ETH Zurich, Department of Chemistry and Applied Biosciences Vladimir-Prelog-Weg 4 8093 Zurich Switzerland gisbert@ethz.ch., Hiss JA; ETH Zurich, Department of Chemistry and Applied Biosciences Vladimir-Prelog-Weg 4 8093 Zurich Switzerland gisbert@ethz.ch., Schneider G; ETH Zurich, Department of Chemistry and Applied Biosciences Vladimir-Prelog-Weg 4 8093 Zurich Switzerland gisbert@ethz.ch.; ETH Zurich, Department of Biosystems Science and Engineering Klingelbergstrasse 48 4056 Basel Switzerland.
Jazyk:	angličtina
Zdroj:	RSC advances [RSC Adv] 2024 Nov 20; Vol. 14 (50), pp. 37035-37044. Date of Electronic Publication: 2024 Nov 20 (Print Publication: 2024).
DOI:	10.1039/d4ra05422a
Abstrakt:	Semi-empirical quantum chemistry methods estimate the binding free energies of protein-ligand complexes. We present an integrated approach combining the GFN2- x TB method with de novo design for the generation and evaluation of potential inhibitors of acetylcholinesterase (AChE). We employed chemical language model-based molecule generation to explore the synthetically accessible chemical space around the natural product Huperzine A, a potent AChE inhibitor. Four distinct molecular libraries were created using structure- and ligand-based molecular de novo design with SMILES and SELFIES representations, respectively. These libraries were computationally evaluated for synthesizability, novelty, and predicted biological activity. The candidate molecules were subjected to molecular docking to identify hypothetical binding poses, which were further refined using Gibbs free energy calculations. The structurally novel top-ranked molecule was chemically synthesized and biologically tested, demonstrating moderate micromolar activity against AChE. Our findings highlight the potential and certain limitations of integrating deep learning-based molecular generation with semi-empirical quantum chemistry-based activity prediction for structure-based drug design. Competing Interests: G. S. is a co-founder of inSili.com LLC, Zurich, and Xanadys LLC, Zurich, and a consultant to the pharmaceutical industry. (This journal is © The Royal Society of Chemistry.)
Databáze:	MEDLINE
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