CSearch: chemical space search via virtual synthesis and global optimization
Abstract The two key components of computational molecular design are virtually generating molecules and predicting the properties of these generated molecules. This study focuses on an effective method for molecular generation through virtual synthesis and global optimization of a given objective f...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2024-12-01
|
| Series: | Journal of Cheminformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13321-024-00936-8 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850137002230415360 |
|---|---|
| author | Hakjean Kim Seongok Ryu Nuri Jung Jinsol Yang Chaok Seok |
| author_facet | Hakjean Kim Seongok Ryu Nuri Jung Jinsol Yang Chaok Seok |
| author_sort | Hakjean Kim |
| collection | DOAJ |
| description | Abstract The two key components of computational molecular design are virtually generating molecules and predicting the properties of these generated molecules. This study focuses on an effective method for molecular generation through virtual synthesis and global optimization of a given objective function. Using a pre-trained graph neural network (GNN) objective function to approximate the docking energies of compounds for four target receptors, we generated highly optimized compounds with 300–400 times less computational effort compared to virtual compound library screening. These optimized compounds exhibit similar synthesizability and diversity to known binders with high potency and are notably novel compared to library chemicals or known ligands. This method, called CSearch, can be effectively utilized to generate chemicals optimized for a given objective function. With the GNN function approximating docking energies, CSearch generated molecules with predicted binding poses to the target receptors similar to known inhibitors, demonstrating its effectiveness in producing drug-like binders. Scientific Contribution We have developed a method for effectively exploring the chemical space of drug-like molecules using a global optimization algorithm with fragment-based virtual synthesis. The compounds generated using this method optimize the given objective function efficiently and are synthesizable like commercial library compounds. Furthermore, they are diverse, novel drug-like molecules with properties similar to known inhibitors for target receptors. |
| format | Article |
| id | doaj-art-815d8210539b4efe8a2c68e680d91059 |
| institution | OA Journals |
| issn | 1758-2946 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Cheminformatics |
| spelling | doaj-art-815d8210539b4efe8a2c68e680d910592025-08-20T02:30:59ZengBMCJournal of Cheminformatics1758-29462024-12-0116111310.1186/s13321-024-00936-8CSearch: chemical space search via virtual synthesis and global optimizationHakjean Kim0Seongok Ryu1Nuri Jung2Jinsol Yang3Chaok Seok4Department of Chemistry, Seoul National UniversityGalux IncDepartment of Chemistry, Seoul National UniversityGalux IncDepartment of Chemistry, Seoul National UniversityAbstract The two key components of computational molecular design are virtually generating molecules and predicting the properties of these generated molecules. This study focuses on an effective method for molecular generation through virtual synthesis and global optimization of a given objective function. Using a pre-trained graph neural network (GNN) objective function to approximate the docking energies of compounds for four target receptors, we generated highly optimized compounds with 300–400 times less computational effort compared to virtual compound library screening. These optimized compounds exhibit similar synthesizability and diversity to known binders with high potency and are notably novel compared to library chemicals or known ligands. This method, called CSearch, can be effectively utilized to generate chemicals optimized for a given objective function. With the GNN function approximating docking energies, CSearch generated molecules with predicted binding poses to the target receptors similar to known inhibitors, demonstrating its effectiveness in producing drug-like binders. Scientific Contribution We have developed a method for effectively exploring the chemical space of drug-like molecules using a global optimization algorithm with fragment-based virtual synthesis. The compounds generated using this method optimize the given objective function efficiently and are synthesizable like commercial library compounds. Furthermore, they are diverse, novel drug-like molecules with properties similar to known inhibitors for target receptors.https://doi.org/10.1186/s13321-024-00936-8Chemical space searchComputer-aided drug designGlobal optimizationVirtual synthesis |
| spellingShingle | Hakjean Kim Seongok Ryu Nuri Jung Jinsol Yang Chaok Seok CSearch: chemical space search via virtual synthesis and global optimization Journal of Cheminformatics Chemical space search Computer-aided drug design Global optimization Virtual synthesis |
| title | CSearch: chemical space search via virtual synthesis and global optimization |
| title_full | CSearch: chemical space search via virtual synthesis and global optimization |
| title_fullStr | CSearch: chemical space search via virtual synthesis and global optimization |
| title_full_unstemmed | CSearch: chemical space search via virtual synthesis and global optimization |
| title_short | CSearch: chemical space search via virtual synthesis and global optimization |
| title_sort | csearch chemical space search via virtual synthesis and global optimization |
| topic | Chemical space search Computer-aided drug design Global optimization Virtual synthesis |
| url | https://doi.org/10.1186/s13321-024-00936-8 |
| work_keys_str_mv | AT hakjeankim csearchchemicalspacesearchviavirtualsynthesisandglobaloptimization AT seongokryu csearchchemicalspacesearchviavirtualsynthesisandglobaloptimization AT nurijung csearchchemicalspacesearchviavirtualsynthesisandglobaloptimization AT jinsolyang csearchchemicalspacesearchviavirtualsynthesisandglobaloptimization AT chaokseok csearchchemicalspacesearchviavirtualsynthesisandglobaloptimization |