This survey provides a comprehensive review of over 100 methods in 3D structure-based drug design (SBDD) and molecular optimisation, and proposes a unified taxonomy covering four primary generative paradigms: autoregressive models, diffusion models, flow matching, and Bayesian Flow Networks.
A state-aware functional classifier (SAFC) is developed that integrates molecular dynamics derived receptor ensembles, ensemble docking and protein ligand interaction graphs that provides dynamics-aware functional activity rankings for generated molecules that were partly complementary to docking, drug-likeness and syn...
H. Kumar, Zheng-Xiao Yang, Yankai Yu et al.· bioRxiv· 0 citations
Deep neural network (DNN)‐based in silico models show great promise in predicting the properties and bioactivities of novel compounds, including small molecules. Among traditional approaches, structure‐based drug design (SBDD) remains a fundamental approach for drug discovery using molecular docking, scoring functions,...
A. Srivastav, Unnati Modi, Rahul Kumar et al.· Molecular Informatics· 1 citation
Current structure-based drug design generative models often struggle to faithfully recapitulate genuine ligand-protein binding interactions. Instead, under the coupling of implicit learning architectures and biased training data, they tend to learn spurious statistical correlations. To address this, we propose EIP-Diff...
Huabin Du, Mingyang Wang, M. Luo et al.· Journal of the American Chem...· 0 citations
A clear pattern is revealed in LLM spatial capabilities: while they still lag behind state-of-the-art approaches, they are promising and can handle multiple spatial constraints simultaneously, enabling scaling to heterogeneous setups.
Thomas MacDougall, Maksim Kuznetsov, Roman Schutski et al.· arXiv.org· 1 citation
Structure-based drug design (SBDD) models are central to modern pharmaceutical research, enabling the rational exploration of protein-ligand interactions at atomic resolution. However, most existing approaches frame molecular generation as an isolated optimization or a one-to-one matching task, overlooking the shared b...
Dong Xu, Zhang-Fan Yang, Junchuang Cai et al.· IEEE transactions on computa...· 1 citation
This review elucidates the paradigm shift in drug discovery from serendipitous exploration to rational, structure-driven polypharmacological molecular engineering, thereby providing a clear, structured guide for navigating the complexities of next-generation therapeutics.
Tianming Han, Zhijie Pan, Wenchi Ge et al.· 0 citations
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