AI

Electron-density-informed effective and reliable de novo molecular design and optimization with ED2Mol

  • Sadybekov, A. V. & Katritch, V. Computational approaches streamlining drug discovery. Nature 616, 673–685 (2023).

    Google Scholar 

  • Hughes, J. P., Rees, S., Kalindjian, S. B. & Philpott, K. L. Principles of early drug discovery. Br. J. Pharmacol. 162, 1239–1249 (2011).

    Google Scholar 

  • He, J. et al. ASD2023: towards the integrating landscapes of allosteric knowledgebase. Nucleic Acids Res. 52, D376–D383 (2024).

    Google Scholar 

  • Irwin, J. J. et al. ZINC20—a free ultralarge-scale chemical database for ligand discovery. J. Chem. Inf. Model. 60, 6065–6073 (2020).

    Google Scholar 

  • Reymond, J. L. The chemical space project. Acc. Chem. Res. 48, 722–730 (2015).

    Google Scholar 

  • Schneider, G. & Fechner, U. Computer-based de novo design of drug-like molecules. Nat. Rev. Drug Discov. 4, 649–663 (2005).

    Google Scholar 

  • Isert, C., Atz, K. & Schneider, G. Structure-based drug design with geometric deep learning. Curr. Opin. Struct. Biol. 79, 102548 (2023).

    Google Scholar 

  • Li, M., Lan, X., Lu, X. & Zhang, J. A structure-based allosteric modulator design paradigm. Health Data Sci. 3, 0094 (2023).

    Google Scholar 

  • Harris, C. et al. PoseCheck: Generative models for 3D structure-based drug design produce unrealistic poses. In NeurIPS 2023 Generative AI and Biology (GenBio) Workshop (2023); https://openreview.net/forum?id=Nf5BxVllgq

  • Buttenschoen, M., Morris, G. M. & Deane, C. M. PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences. Chem. Sci. 15, 3130–3139 (2024).

    Google Scholar 

  • Jiang, Y. et al. PocketFlow is a data-and-knowledge-driven structure-based molecular generative model. Nat. Mach. Intell. 6, 326–337 (2024).

    Google Scholar 

  • Pan, Y. Heading toward Artificial Intelligence 2.0. Engineering 2, 409–413 (2016).

    Google Scholar 

  • Skalic, M., Sabbadin, D., Sattarov, B., Sciabola, S. & De Fabritiis, G. From target to drug: generative modeling for the multimodal structure-based ligand design. Mol. Pharm. 16, 4282–4291 (2019).

    Google Scholar 

  • Zhung, W., Kim, H. & Kim, W. Y. 3D molecular generative framework for interaction-guided drug design. Nat. Commun. 15, 2688 (2024).

    Google Scholar 

  • Zhang, Z., Min, Y., Zheng, S. & Liu, Q. Molecule generation for target protein binding with structural motifs. In Eleventh International Conference on Learning Representations (2023); https://openreview.net/forum?id=Rq13idF0F73

  • Nakatsuji, H. Electron-cloud following and preceding and the shapes of molecules. J. Am. Chem. Soc. 96, 30–37 (1974).

    Google Scholar 

  • Rico, J. F., López, R., Ema, I. & Ramírez, G. Chemical forces in terms of the electron density. Theor. Chem. Acc. 118, 709–721 (2007).

    Google Scholar 

  • Terwilliger, T. C., Klei, H., Adams, P. D., Moriarty, N. W. & Cohn, J. D. Automated ligand fitting by core-fragment fitting and extension into density. Acta Cryst. D62, 915–922 (2006).

    Google Scholar 

  • Mysinger, M. M., Carchia, M., Irwin, J. J. & Shoichet, B. K. Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking. J. Med. Chem. 55, 6582–6594 (2012).

    Google Scholar 

  • Hopkins, A. L., Keseru, G. M., Leeson, P. D., Rees, D. C. & Reynolds, C. H. The role of ligand efficiency metrics in drug discovery. Nat. Rev. Drug Discov. 13, 105–121 (2014).

    Google Scholar 

  • Ertl, P. & Schuffenhauer, A. Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. J. Cheminform. 1, 8 (2009).

    Google Scholar 

  • Bickerton, G. R., Paolini, G. V., Besnard, J., Muresan, S. & Hopkins, A. L. Quantifying the chemical beauty of drugs. Nat. Chem. 4, 90–98 (2012).

    Google Scholar 

  • Schneider, G. & Clark, D. E. Automated de novo drug design: are we nearly there yet? Angew. Chem. Int. Ed. 58, 10792–10803 (2019).

    Google Scholar 

  • Mozaffari-Jovin, S. et al. Inhibition of RNA helicase Brr2 by the C-terminal tail of the spliceosomal protein Prp8. Science 341, 80–84 (2013).

    Google Scholar 

  • Iwatani-Yoshihara, M. et al. Discovery of allosteric inhibitors targeting the spliceosomal RNA helicase Brr2. J. Med. Chem. 60, 5759–5771 (2017).

    Google Scholar 

  • Choi, S.-S., Park, J. & Choi, J. H. Revisiting PPARγ as a target for the treatment of metabolic disorders. BMB Rep. 47, 599–608 (2014).

    Google Scholar 

  • Artis, D. R. et al. Scaffold-based discovery of indeglitazar, a PPAR pan-active anti-diabetic agent. Proc. Natl Acad. Sci. USA 106, 262–267 (2009).

    Google Scholar 

  • L’Hote, C. G. & Knowles, M. A. Cell responses to FGFR3 signalling: growth, differentiation and apoptosis. Exp. Cell Res. 304, 417–431 (2005).

    Google Scholar 

  • Krook, M. A. et al. Fibroblast growth factor receptors in cancer: genetic alterations, diagnostics, therapeutic targets and mechanisms of resistance. Br. J. Cancer 124, 880–892 (2021).

    Google Scholar 

  • Huang, Z. et al. Structural mimicry of a-loop tyrosine phosphorylation by a pathogenic FGF receptor 3 mutation. Structure 21, 1889–1896 (2013).

    Google Scholar 

  • Li, M., Rehman, A. U., Liu, Y., Chen, K. & Lu, S. Dual roles of ATP-binding site in protein kinases: orthosteric inhibition and allosteric regulation. Adv. Protein Chem. Struct. Biol. 124, 87–119 (2021).

    Google Scholar 

  • Attwood, M. M., Fabbro, D., Sokolov, A. V., Knapp, S. & Schioth, H. B. Trends in kinase drug discovery: targets, indications and inhibitor design. Nat. Rev. Drug Discov. 20, 839–861 (2021).

    Google Scholar 

  • Chu, W. T., Chu, X. & Wang, J. Binding mechanism and dynamic conformational change of C subunit of PKA with different pathways. Proc. Natl Acad. Sci. USA 114, E7959–E7968 (2017).

    Google Scholar 

  • Melendez, J., Grogg, M. & Zheng, Y. Signaling role of Cdc42 in regulating mammalian physiology. J. Biol. Chem. 286, 2375–2381 (2011).

    Google Scholar 

  • Maldonado, M. D. M. & Dharmawardhane, S. Targeting Rac and Cdc42 GTPases in cancer. Cancer Res. 78, 3101–3111 (2018).

    Google Scholar 

  • Jahid, S. et al. Structure-based design of CDC42 effector interaction inhibitors for the treatment of cancer. Cell Rep. 39, 110760 (2022).

    Google Scholar 

  • Matschinsky, F. M., Glaser, B. & Magnuson, M. A. Pancreatic beta-cell glucokinase: closing the gap between theoretical concepts and experimental realities. Diabetes 47, 307–315 (1998).

    Google Scholar 

  • Grimsby, J. et al. Allosteric activators of glucokinase: potential role in diabetes therapy. Science 301, 370–373 (2003).

    Google Scholar 

  • Nishimura, T. et al. Identification of novel and potent 2-amino benzamide derivatives as allosteric glucokinase activators. Bioorg. Med. Chem. Lett. 19, 1357–1360 (2009).

    Google Scholar 

  • Mitsuya, M. et al. Discovery of novel 3,6-disubstituted 2-pyridinecarboxamide derivatives as GK activators. Bioorg. Med. Chem. Lett. 19, 2718–2721 (2009).

    Google Scholar 

  • Jiang, X. et al. GPRC5A: an emerging biomarker in human cancer. Biomed. Res. Int. 2018, 1823726 (2018).

    Google Scholar 

  • Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024).

    Google Scholar 

  • Huang, W. K. et al. AlloSite: a method for predicting allosteric sites. Bioinformatics 29, 2357–2359 (2013).

    Google Scholar 

  • Atz, K. et al. Prospective de novo drug design with deep interactome learning. Nat. Commun. 15, 3408 (2024).

    Google Scholar 

  • Hert, J., Irwin, J. J., Laggner, C., Keiser, M. J. & Shoichet, B. K. Quantifying biogenic bias in screening libraries. Nat. Chem. Biol. 5, 479–483 (2009).

    Google Scholar 

  • Mnih, V. et al. Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015).

    Google Scholar 

  • Cremer, J., Le, T., Noe, F., Clevert, D. A. & Schutt, K. T. PILOT: equivariant diffusion for pocket-conditioned de novo ligand generation with multi-objective guidance via importance sampling. Chem. Sci. 15, 14954–14967 (2024).

    Google Scholar 

  • Grosse-Kunstleve, R. W., Sauter, N. K., Moriarty, N. W. & Adams, P. D. The Computational Crystallography Toolbox: crystallographic algorithms in a reusable software framework. J. Appl. Crystallogr. 35, 126–136 (2002).

    Google Scholar 

  • Ganea, O. et al. GeoMol: torsional geometric generation of molecular 3D conformer ensembles. Adv. Neural Inf. Process. Syst. 34, 13757–13769 (2021).

    Google Scholar 

  • Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006).

    Google Scholar 

  • Gao, M. & Skolnick, J. APoc: large-scale identification of similar protein pockets. Bioinformatics 29, 597–604 (2013).

    Google Scholar 

  • Riniker, S. & Landrum, G. A. Better informed distance geometry: using what we know to improve conformation generation. J. Chem. Inf. Model. 55, 2562–2574 (2015).

    Google Scholar 

  • Francoeur, P. G. et al. Three-dimensional convolutional neural networks and a cross-docked data set for structure-based drug design. J. Chem. Inf. Model. 60, 4200–4215 (2020).

    Google Scholar 

  • Zha, J., Li, M., Kong, R., Lu, S. & Zhang, J. Explaining and predicting allostery with allosteric database and modern analytical techniques. J. Mol. Biol. 434, 167481 (2022).

    Google Scholar 

  • Akbar, R. & Helms, V. ALLO: a tool to discriminate and prioritize allosteric pockets. Chem. Biol. Drug Des. 91, 845–853 (2018).

    Google Scholar 

  • Huang, W. et al. ASBench: benchmarking sets for allosteric discovery. Bioinformatics 31, 2598–2600 (2015).

    Google Scholar 

  • Peng, X. et al. Pocket2Mol: efficient molecular sampling based on 3D protein pockets. In Proc. 39th International Conference on Machine Learning 17644–17655 (PMLR, 2022).

  • Liu, M., Luo, Y., Uchino, K., Maruhashi, K. & Ji, S. Generating 3D molecules for target protein binding. In Proc. 39th International Conference on Machine Learning 13912–13924 (PMLR, 2022).

  • Zhang, O. et al. ResGen is a pocket-aware 3D molecular generation model based on parallel multiscale modelling. Nat. Mach. Intell. 5, 1020–1030 (2023).

    Google Scholar 

  • Guan, J. et al. 3D Equivariant diffusion for target-aware molecule generation and affinity prediction. In Eleventh International Conference on Learning Representations (2023); https://openreview.net/forum?id=kJqXEPXMsE0

  • Xie, J., Chen, S., Lei, J. & Yang, Y. DiffDec: structure-aware scaffold decoration with an end-to-end diffusion model. J. Chem. Inf. Model. 64, 2554–2564 (2024).

    Google Scholar 

  • Zhang, Y. et al. FragGrow: a web server for structure-based drug design by fragment growing within constraints. J. Chem. Inf. Model. 64, 3970–3976 (2024).

    Google Scholar 

  • Kingma, D. P. Adam: a method for stochastic optimization. Preprint at https://arxiv.org/abs/1412.6980 (2014).

  • Li, M. Electron-density informed effective and reliable de novo molecular design and optimization with ED2Mol. Zenodo https://doi.org/10.5281/zenodo.16736826 (2025).

  • Li, M. ED2Mol: Electron-density-informed effective and reliable de novo molecular design and optimization with ED2Mol. Github https://github.com/pineappleK/ED2Mol (2025).

  • Li, M. ED2Mol: electron-density informed effective and reliable de novo molecular design and optimization. Code Ocean https://doi.org/10.24433/CO.7635060.v2 (2025).

  • Don’t miss more hot News like this! Click here to discover the latest in AI news!

    2025-08-20 00:00:00

    Related Articles

    Back to top button