FastGrow: on-the-fly growing and its application to DYRK1A

Abstract

Fragment-based drug design is an established routine approach in both experimental and computational spheres. Growing fragment hits into viable ligands has increasingly shifted into the spotlight. FastGrow is an application based on a shape search algorithm that addresses this challenge at high speeds of a few milliseconds per fragment. It further features a pharmacophoric interaction description, ensemble flexibility, as well as geometry optimization to become a fully fledged structure-based modeling tool. All features were evaluated in detail on a previously reported collection of fragment growing scenarios extracted from crystallographic data. FastGrow was also shown to perform competitively versus established docking software. A case study on the DYRK1A kinase, using recently reported new chemotypes, illustrates FastGrow’s features in practice and its ability to identify active fragments. FastGrow is freely available to the public as a web server at https://fastgrow.plus/ and is part of the SeeSAR 3D software package.

Three-Pronged Approach to Curb Cancer Metastasis

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Protein Structure-Based Organic Chemistry-Driven Ligand Design from Ultralarge Chemical Spaces

“…Once a seed fragment has been identified, scaffold-focused two-dimensional (2D) libraries, exploring the corresponding chemical space via a set of organic chemistry reactions, can be enumerated, converted in three-dimensional (3D) atomic coordinates and physically docked to propose novel hits. This approach has been applied with success to a few targets ,,, but still requires hardware settings enabling docking a significant subset (a few million) of the entire chemical space. Last, fast machine learning approaches may be first trained on a set of representative ligand-annotated docking poses to simply predict docking scores ,,,, and next be applied to predict docking scores for the remaining space.…”

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“…56 Methods like FastGrow emphasize identifying fragment growth points rather than the specifics of fragment expansion, often comparing to other structural docking tools. 57 In the realm of docking, ultralargescale docking techniques, such as those by Lyu et al, identify potential molecules based on docking scores, with a breadth possibly surpassing human intuition. 58 On a similar note, Allen et al present iterative fragment growth relying on docking scores, yet distinctively using prescreening information to navigate their search.…”

“…The conventional approach to computational FBDD involves either computationally fragmentizing a compound (ligand) library or self-generating fragments using computational techniques, followed by computationally docking target fragments to a protein binding pocket and computationally “growing” or synthesizing a candidate ligand by modifying the fragment within that pocket . Methods like FastGrow emphasize identifying fragment growth points rather than the specifics of fragment expansion, often comparing to other structural docking tools . In the realm of docking, ultralarge-scale docking techniques, such as those by Lyu et al, identify potential molecules based on docking scores, with a breadth possibly surpassing human intuition .…”