title: Automatic tailoring and transplanting: a practical method that makes virtual screening more useful.
abstract: Docking-based virtual screening of large compound libraries has been widely applied to lead discovery in structure-based drug design. However, subsequent lead optimizations often rely on other types of computational methods, such as de novo design methods. We have developed an automatic method, namely automatic tailoring and transplanting (AutoT&T), which can effectively utilize the outcomes of virtual screening in lead optimization. This method detects suitable fragments on virtual screening hits and then transplants them onto a lead compound to generate new ligand molecules. Binding affinities, synthetic feasibilities, and drug-likeness properties are considered in the selection of final designs. In this study, our AutoT&T program was tested on three different target proteins, including p38 MAP kinase, PPAR-α, and Mcl-1. In the first two cases, AutoT&T was able to produce molecules identical or similar to known inhibitors with better potency than the given lead compound. In the third case, we demonstrated how to apply AutoT&T to design novel ligand molecules from scratch. Compared to the solutions generated by other two de novo design methods, i.e., LUDI and EA-Inventor, the solutions generated by AutoT&T were structurally more diverse and more promising in terms of binding scores in all three cases. AutoT&T also completed the assigned jobs more efficiently than LUDI and EA-Inventor by several folds. Our AutoT&T method has certain technical advantages over de novo design methods. Importantly, it expands the application of virtual screening from lead discovery to lead optimization and thus may serve as a valuable tool for many researchers.
authors.affiliation: State Key Laboratory of Bioorganic Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, People's Republic of China.
authors.forename: Yan
authors.initials: Y
authors.lastname: Li
authors.forename: Yuan
authors.initials: Y
authors.lastname: Zhao
authors.forename: Zhihai
authors.initials: Z
authors.lastname: Liu
authors.forename: Renxiao
authors.initials: R
authors.lastname: Wang
mesh_terms: D001331:Automation; D015195:Drug Design; D008958:Models, Molecular; D064549:Myeloid Cell Leukemia Sequence 1 Protein; D047492:Peroxisome Proliferator-Activated Receptors / Q000037:antagonists & inhibitors / Q000737:chemistry; D011487:Protein Conformation; D047428:Protein Kinase Inhibitors / Q000737:chemistry / Q000494:pharmacology; D019253:Proto-Oncogene Proteins c-bcl-2 / Q000037:antagonists & inhibitors / Q000737:chemistry; D054852:Small Molecule Libraries / Q000737:chemistry / Q000494:pharmacology; D013816:Thermodynamics; D014584:User-Computer Interface; D048051:p38 Mitogen-Activated Protein Kinases / Q000037:antagonists & inhibitors / Q000737:chemistry
chemical_list: D064549:Myeloid Cell Leukemia Sequence 1 Protein; D047492:Peroxisome Proliferator-Activated Receptors; D047428:Protein Kinase Inhibitors; D019253:Proto-Oncogene Proteins c-bcl-2; D054852:Small Molecule Libraries; D048051:p38 Mitogen-Activated Protein Kinases