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The Leucine-rich repeat kinase 2 (LRRK2) target has been identified as a promising drug target for Parkinson's disease (PD) treatment. This study focuses on optimizing the activity of LRRK2 inhibitors using alchemical relative binding free energy (RBFE) calculations. Initially, we assessed various free energy calculation methods across different LRRK2 kinase inhibitor scaffolds. The results indicate that alchemical free energy calculations are promising for prospective predictions on LRRK2 inhibitors, especially for the aminopyrimidine scaffold with an RMSE of 1.15 kcal mol−1 and Rp of 0.83. Following this, we optimized a potent LRRK2 kinase inhibitor identified from previous virtual screenings, featuring a novel scaffold. Guided by RBFE predictions using alchemical methods, this optimization led to the discovery of compound LY2023-001. This compound, with a [1,2,4]triazolo[5,6-b]indole scaffold, exhibited enhanced inhibitory activity against G2019S LRRK2 (IC50 = 12.9 nM). Molecular dynamics (MD) simulations revealed that LY2023-001 formed stable hydrogen bonds with Glu1948, and Ala1950 in the G2019S LRRK2 protein. Additionally, its phenyl substituents engage in strong electrostatic interactions with Lys1906 and van der Waals interactions with Leu1885, Phe1890, Val1893, Ile1933, Met1947, Leu1949, Leu2001, Ala2016, and Asp2017. Our findings underscore the potential of computational methods in the successful optimization of small molecules, offering important insights for the development of novel LRRK2 inhibitors. [ABSTRACT FROM AUTHOR]

Deep learning is an important branch of artificial intelligence that has been successfully applied into medicine and two-dimensional ligand design. The three-dimensional (3D) ligand generation in the 3D pocket of protein target is an interesting and challenging issue for drug design by deep learning. Here, the MolAICal software is introduced to supply a way for generating 3D drugs in the 3D pocket of protein targets by combining with merits of deep learning model and classical algorithm. The MolAICal software mainly contains two modules for 3D drug design. In the first module of MolAICal, it employs the genetic algorithm, deep learning model trained by FDA-approved drug fragments and Vinardo score fitting on the basis of PDBbind database for drug design. In the second module, it uses deep learning generative model trained by drug-like molecules of ZINC database and molecular docking invoked by Autodock Vina automatically. Besides, the Lipinski's rule of five, Pan-assay interference compounds (PAINS), synthetic accessibility (SA) and other user-defined rules are introduced for filtering out unwanted ligands in MolAICal. To show the drug design modules of MolAICal, the membrane protein glucagon receptor and non-membrane protein SARS-CoV-2 main protease are chosen as the investigative drug targets. The results show MolAICal can generate the various and novel ligands with good binding scores and appropriate XLOGP values. We believe that MolAICal can use the advantages of deep learning model and classical programming for designing 3D drugs in protein pocket. MolAICal is freely for any nonprofit purpose and accessible at https://molaical.github.io. [ABSTRACT FROM AUTHOR]