Sdfconf: A Novel, Flexible, and Robust Molecular Data Management Tool

Lätti, Sakari; Niinivehmas, Sanna; Pentikäinen, Olli T.

doi:10.1021/acs.jcim.1c01051

Cited by 3 publications

(5 citation statements)

References 18 publications

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“…The conformers were aligned in the 3D space, using the protein backbone C α atoms with VERTAA in BODIL. SDFCONF [ 37 ] was used for the handling, filtering and grouping of the docked compounds during the data analysis. The figures were prepared using BODIL, MAESTRO and VMD1.9.2 [ 32 , 38 ].…”

Section: Methodsmentioning

confidence: 99%

Ligand-Enhanced Negative Images Optimized for Docking Rescoring

Kurkinen

Lehtonen

Pentikäinen

et al. 2022

IJMS

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Despite the pivotal role of molecular docking in modern drug discovery, the default docking scoring functions often fail to recognize active ligands in virtual screening campaigns. Negative image-based rescoring improves docking enrichment by comparing the shape/electrostatic potential (ESP) of the flexible docking poses against the target protein’s inverted cavity volume. By optimizing these negative image-based (NIB) models using a greedy search, the docking rescoring yield can be improved massively and consistently. Here, a fundamental modification is implemented to this shape-focused pharmacophore modelling approach—actual ligand 3D coordinates are incorporated into the NIB models for the optimization. This hybrid approach, labelled as ligand-enhanced brute-force negative image-based optimization (LBR-NiB), takes the best from both worlds, i.e., the all-roundedness of the NIB models and the difficult to emulate atomic arrangements of actual protein-bound small-molecule ligands. Thorough benchmarking, focused on proinflammatory targets, shows that the LBR-NiB routinely improves the docking enrichment over prior iterations of the R-NiB methodology. This boost can be massive, if the added ligand information provides truly essential binding information that was lacking or completely missing from the cavity-based NIB model. On a practical level, the results indicate that the LBR-NiB typically works well when the added ligand 3D data originates from a high-quality source, such as X-ray crystallography, and, yet, the NIB model compositions can also sometimes be improved by fusing into them, for example, with flexibly docked solvent molecules. In short, the study demonstrates that the protein-bound ligands can be used to improve the shape/ESP features of the negative images for effective docking rescoring use in virtual screening.

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Section: Methodsmentioning

confidence: 99%

Ligand-Enhanced Negative Images Optimized for Docking Rescoring

Kurkinen

Lehtonen

Pentikäinen

et al. 2022

IJMS

Self Cite

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“…The PHA filtering, performed with SDFCONF [49], was fine-tuned by adjusting the optimal radii for the PHA points, which would discard as many decoy compounds as possible with a relatively low number of active compounds being discarded. Eventually, PHA filtering radii were determined to be 4.0 Å for region 1, 4.5 Å for region 2, 5.2 Å for region 3, 4.0 Å for region 4, and 4.0 Å for region 5.…”

Section: Pharmacophore Filtering Focuses Compound Selection To Invers...mentioning

confidence: 99%

“…First, the top 1% of the best-ranked SPECS compounds from BR-NiB (Models Ib, c and IIb, c; Figure 2) were selected for further consideration. Next, the molecules were screened with the PHA points defined from the RORγt ligand binding site using SDFCONF [49] (Figure 4). Third, the compounds were filtered by logP (< 5.5), and to avoid unspecific binding, the potential PAINS compounds were filtered out using Canvas.…”

Section: Selecting Compounds For Experimental Testingmentioning

confidence: 99%

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Virtual Screening Strategy to Identify Retinoic Acid-Related Orphan Receptor γt Modulators

et al. 2023

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Molecular docking is a key method used in virtual screening (VS) campaigns to identify small-molecule ligands for drug discovery targets. While docking provides a tangible way to understand and predict the protein-ligand complex formation, the docking algorithms are often unable to separate active ligands from inactive molecules in practical VS usage. Here, a novel docking and shape-focused pharmacophore VS protocol is demonstrated for facilitating effective hit discovery using retinoic acid receptor-related orphan receptor gamma t (RORγt) as a case study. RORγt is a prospective target for treating inflammatory diseases such as psoriasis and multiple sclerosis. First, a commercial molecular database was flexibly docked. Second, the alternative docking poses were rescored against the shape/electrostatic potential of negative image-based (NIB) models that mirror the target’s binding cavity. The compositions of the NIB models were optimized via iterative trimming and benchmarking using a greedy search-driven algorithm or brute force NIB optimization. Third, a pharmacophore point-based filtering was performed to focus the hit identification on the known RORγt activity hotspots. Fourth, free energy binding affinity evaluation was performed on the remaining molecules. Finally, twenty-eight compounds were selected for in vitro testing and eight compounds were determined to be low μM range RORγt inhibitors, thereby showing that the introduced VS protocol generated an effective hit rate of ~29%.

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“…The SDF (Structure Data File) [33] of all the final compounds synthetized in this work were submitted to the Open Innovation Drug Discovery program at Eli Lilly, [34] where they were in‐silico screened for novelty, drug‐like properties and selected for a screening campaign against the human epigenetic target Nicotinamide N‐Methyltransferase (hNNMT) which at that time was available and uprunning from the Eli Lilly OIDD screening platform. The hNNMT is a metabolic enzyme which catalyzes the S‐adenosyl‐L‐methionine (SAM)‐dependent N‐methylation of nicotinamide.…”

Section: Biology: Hnnmt Profilingmentioning

confidence: 99%

Design and Synthesis of Fsp3‐Enriched Spirocyclic‐Based Biological Screening Compound Arrays via DOS Strategies and Their NNMT Inhibition Profiling

et al. 2022

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Medicinal chemists are keen to explore tridimensional compounds, especially when it comes to small molecules. It has already been stressed that the majority of known drugs tend to be flat, whereas natural products tend to be more tridimensional and represent a good source of active compounds. 3D metrics have been implemented and computational descriptors are available to evaluate and prioritize compounds based on their 3D geometry. This is usually done by comparing the saturated carbon atoms in a molecule with the total number of its non‐hydrogen atoms (the Fsp3 value). While this aspect is clear, still there are not enough synthetic tools that support the realization of novel chemotypes that conform to these criteria. Herein we describe a diversity oriented synthesis (DOS) synthetic cascade technology that starts from two simple reagents, and generates highly enriched Fsp3 novel and diverse spiro‐scaffolds with pragmatic synthetic handles (points of diversity). The spiro nature of these scaffolds not only ensures high Fsp3 values but renders the compounds more rigid and therefore more effective in forming precise stereo‐interactions with their potential biological targets. These compounds were also profiled for their drug‐like properties and as potential modulators of the NNMT enzyme.

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Sdfconf: A Novel, Flexible, and Robust Molecular Data Management Tool

Cited by 3 publications

References 18 publications

Ligand-Enhanced Negative Images Optimized for Docking Rescoring

Ligand-Enhanced Negative Images Optimized for Docking Rescoring

Virtual Screening Strategy to Identify Retinoic Acid-Related Orphan Receptor γt Modulators

Design and Synthesis of Fsp3‐Enriched Spirocyclic‐Based Biological Screening Compound Arrays via DOS Strategies and Their NNMT Inhibition Profiling

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