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• W4322761566 endingPage "102559" @default.
• W4322761566 startingPage "102559" @default.
• W4322761566 abstract "Generative molecular design for drug discovery and development has seen a recent resurgence promising to improve the efficiency of the design-make-test-analyse cycle; by computationally exploring much larger chemical spaces than traditional virtual screening techniques. However, most generative models thus far have only utilized small-molecule information to train and condition de novo molecule generators. Here, we instead focus on recent approaches that incorporate protein structure into de novo molecule optimization in an attempt to maximize the predicted on-target binding affinity of generated molecules. We summarize these structure integration principles into either distribution learning or goal-directed optimization and for each case whether the approach is protein structure-explicit or implicit with respect to the generative model. We discuss recent approaches in the context of this categorization and provide our perspective on the future direction of the field." @default.
• W4322761566 created "2023-03-03" @default.
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• W4322761566 date "2023-04-01" @default.
• W4322761566 modified "2023-09-30" @default.
• W4322761566 title "Integrating structure-based approaches in generative molecular design" @default.
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• W4322761566 doi "https://doi.org/10.1016/j.sbi.2023.102559" @default.
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• W4322761566 hasPublicationYear "2023" @default.
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