The fragments decomposed from small-molecule drugs are recombined under predefined retrosynthetic rules, offering improved drug-likeness and synthesizability, overcoming the inherent limitations of atom-based approaches. Distinct from prevailing atom-centric methods, 3D-MCTS employs a fragment-based molecular editing strategy. To overcome these challenges, we present a novel search-based framework, 3D-MCTS, for structure-based de novo drug design. Additionally, the dependence of deep learning models on large-scale structural data has hindered their adaptability across different targets. Despite the introduction of deep generative models for molecular generation, the atom-wise generation paradigm that partially contradicts chemical intuition limits the validity and synthetic accessibility of the generated molecules. Contemporary structure-based molecular generative methods have demonstrated their potential to model the geometric and energetic complementarity between ligands and receptors, thereby facilitating the design of molecules with favorable binding affinity and target specificity.
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