Kim, J., Park, S., Min, D. & Kim, W. Comprehensive survey of recent drug discovery using deep learning. Int. J. Mol. Sci. 22, 9983 (2021).
Volk, MichaelJeffrey et al. Biosystems design by machine learning. ACS Synth. Biol. 9, 1514–1533 (2020).
Mazurenko, S., Prokop, Z. & Damborsky, J. Machine learning in enzyme engineering. ACS Catal. 10, 1210–1223 (2019).
Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024).
Krishna, R. et al. Generalized biomolecular modeling and design with RoseTTAFold All-Atom. Science 384, eadl2528 (2024).
Zhou, C. et al. A comprehensive survey on pretrained foundation models: a history from BERT to ChatGPT. Int. J. Mach. Learn. Cybern. (2024).
Touvron, H. et al. Llama 2: open foundation and fine-tuned chat models. Preprint at (2023).
OpenAI. GPT-4 technical report. Preprint at (2023).
Zhang, Q. et al. Scientific large language models: a survey on biological & chemical domains. ACM Comput. Surv. 57, 161 (2025).
Weininger, D. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci. 28, 31–36 (1988).
Krenn, M., Häse, F., Nigam, AkshatKumar, Friederich, P. & Aspuru-Guzik, Alán Self-referencing embedded strings (SELFIES): a 100% robust molecular string representation. Mach. Learn. Sci. Technol. 1, 45024 (2020).
Pearson, W. R. in Computer Analysis of Sequence Data: Part I 307–331 (Humana Press, 1994).
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
Ouyang, L. et al. Training language models to follow instructions with human feedback. In Advances in Neural Information Processing Systems 27730–27744 (Curran Associates, 2022).
Edwards, C. et al. Translation between molecules and natural language. In Proc. 2022 Conference on Empirical Methods in Natural Language Processing 375–413 (Association for Computational Linguistics, 2022).
Wang, Z. et al. InstructProtein: aligning human and protein language via knowledge instruction. In Proc. 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) 1114–1136 (Association for Computational Linguistics, 2024).
Pei, Q. et al. Biot5: enriching cross-modal integration in biology with chemical knowledge and natural language associations. In 2023 Conference on Empirical Methods in Natural Language Processing 1102–1123 (Association for Computational Linguistics, 2023).
Fang, Y. et al. Mol-Instructions: a large-scale biomolecular instruction dataset for large language models. In The Twelfth International Conference on Learning Representations (ICLR, 2024).
Pei, Q. et al. Biot5+: towards generalized biological understanding with IUPAC integration and multi-task tuning. In ACL (Findings) 1216–1240 (Association for Computational Linguistics, 2024).
Luo, Y. et al. BioMedGPT: open multimodal generative pre-trained transformer for biomedicine. Preprint at (2023).
Liu, S. et al. Conversational drug editing using retrieval and domain feedback. In The Twelfth International Conference On Learning Representations (ICLR, 2024).
Kroll, A., Ranjan, S., Engqvist, MartinK. M. & Lercher, M. J. A general model to predict small molecule substrates of enzymes based on machine and deep learning. Nat. Commun. 14, 2787 (2023).
Vaswani, A. et al. Attention is all you need. In 31st Conference on Neural Information Processing Systems (NIPS 2017) (2017).
Hastings, J. et al. ChEBI in 2016: improved services and an expanding collection of metabolites. Nucleic Acids Res. 44, D1214–D1219 (2016).
Li, J. et al. Empowering molecule discovery for molecule-caption translation with large language models: a ChatGPT perspective. IEEE Trans. Knowl. Data Eng. (2024).
Zhao, Z. et al. ChemDFM: dialogue foundation model for chemistry. Preprint at (2024).
Cao, H., Liu, Z., Lu, X., Yao, Y. & Li, Y. InstructMol: multi-modal integration for building a versatile and reliable molecular assistant in drug discovery. In Proc. 31st International Conference on Computational Linguistics 354–379 (Association for Computational Linguistics, 2025).
Liu, Z. et al. Prott3: protein-to-text generation for text-based protein understanding. In Proc. 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) 5949–5966 (Association for Computational Linguistics, 2024).
Liu, S. et al. A text-guided protein design framework. Nat. Mach. Intell. 7, 580–591 (2025).
Anderson, A. C. The process of structure-based drug design. Chem. Biol. 10, 787–797 (2003).
Peng, X. et al. Pocket2Mol: efficient molecular sampling based on 3D protein pockets. In Proc. Machine Learning Research 17644–17655 (PMLR, 2022).
Luo, S., Guan, J., Ma, J. & Peng, J. A 3D generative model for structure-based drug design. In Advances in Neural Information Processing Systems 6229–6239 (Curran Associates, 2021).
Guan, J. et al. 3D equivariant diffusion for target-aware molecule generation and affinity prediction. In The Eleventh International Conference on Learning Representations (ICLR, 2023).
Li, Y. et al. DrugGPT: a GPT-based strategy for designing potential ligands targeting specific proteins. Preprint at bioRxiv (2023).
Bar-Even, A. et al. The moderately efficient enzyme: evolutionary and physicochemical trends shaping enzyme parameters. Biochemistry 50, 4402–4410 (2011).
Hu, E. J. et al. LoRA: low-rank adaptation of large language models. In International Conference on Learning Representations (ICLR, 2022).
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O. & Dahl, G. E. Neural message passing for quantum chemistry. In Proc. Machine Learning Research 1263–1272 (PMLR, 2017).
Zhou, G. et al. Uni-Mol: a universal 3D molecular representation learning framework. In The Eleventh International Conference on Learning Representations (ICLR, 2023).
Lin, Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379, 1123–1130 (2023).
Google Scholar
Xu, K., Hu, W., Leskovec, J. & Jegelka, S. How powerful are graph neural networks? In International Conference on Learning Representations (ICLR, 2019).
Hu, W. et al. Strategies for pre-training graph neural networks. In International Conference on Learning Representations (2020).
Wang, Y. et al. Geometric transformer with interatomic positional encoding. In Advances in Neural Information Processing Systems 55981–55994 (Curran Associates, 2023).
Su, J. et al. SaProt: protein language modeling with structure-aware vocabulary. In The Twelfth International Conference on Learning Representations (ICLR, 2024).
Zhang, Z., Liu, Q., Wang, H., Lu, C. & Lee, C. K. Motif-based graph self-supervised learning for molecular property prediction. In Advances in Neural Information Processing Systems 15870–15882 (Curran Associates, 2021).
Li, H. et al. A knowledge-guided pre-training framework for improving molecular representation learning. Nat. Commun. 14, 7568 (2023).
Grant, C. E., Bailey, T. L. & Noble, WilliamStafford FIMO: scanning for occurrences of a given motif. Bioinformatics 27, 1017–1018 (2011).
Rogers, D. & Hahn, M. Extended-connectivity fingerprints. J. Chem. Inf. Model. 50, 742–754 (2010).
Boeckmann, B. et al. The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003. Nucleic Acids Res. 31, 365–370 (2003).
Radford, A. et al. Improving language understanding by generative pre-training. OpenAI (2018).
Kim, S. Pubchem substance and compound databases. Nucleic Acids Res. 44, D1202–D1213 (2016).
Suzek, B. E., Huang, H., McGarvey, P., Mazumder, R. & Wu, C. H. Uniref: comprehensive and non-redundant uniprot reference clusters. Bioinformatics 23, 1282–1288 (2007).
White, J. PubMed 2.0. Med. Ref. Serv. Q. 39, 382–387 (2020).
Sever, R. et al. bioRxiv: the preprint server for biology. Preprint at bioRxiv (2019).
Mudrak, B. et al. Five years of ChemRxiv: where we are and where we go from here. Angew. Chem. Int. Ed. 62, e202215847 (2023).
McNaught, A. D. et al. Compendium of Chemical Terminology Vol. 1669 (Blackwell Science Oxford, 1997).
The UniProt Consortium. UniProt: the universal protein knowledgebase. Nucleic Acids Res. 46, 2699–2699 (2018).
Gilson, M. K. et al. BindingDB in 2015: a public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res. 44, D1045–D1053 (2016).
Uludoğan, G., Ozkirimli, E., Ulgen, K. O., Karalí, N. & Özgür, A. Exploiting pretrained biochemical language models for targeted drug design. Bioinformatics 38, ii155–ii161 (2022).
Bansal, P. et al. Rhea, the reaction knowledgebase in 2022. Nucleic Acids Res. 50, D693–D700 (2022).
Landrum, G. RDKit: a software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 5281 (2013).
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).
Halgren, T. A. Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94. J. Comput. Chem. 17, 490–519 (1996).
Varadi, M. AlphaFold protein structure database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res. 50, D439–D444 (2022).
Paszke, A. et al. PyTorch: an imperative style, high-performance deep learning library. In 33rd Conference on Neural Information Processing Systems (NeurIPS 2019) (NeurIPS, 2019).
Rajbhandari, S., Rasley, J., Ruwase, O. & He, Y. ZeRO: memory optimizations toward training trillion parameter models. In SC20: International Conference for High Performance Computing, Networking, Storage and Analysis 1–16 (IEEE, 2020).
Papineni, K., Roukos, S., Ward, T. & Zhu, W.J. Bleu: a method for automatic evaluation of machine translation. In Proc. 40th Annual Meeting of the Association for Computational Linguistics 311–318 (ACL, 2002).
Lin, C. Y. in Text Summarization Branches Out 74–81 (2004).
Banerjee, S. & Lavie, A. METEOR: an automatic metric for MT evaluation with improved correlation with human judgments. In Proc. ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translationand/or Summarization 65–72 (Association for Computational Linguistics, 2005).
Miller, F. P., Vandome, A. F. & McBrewster, J. Levenshtein Distance: Information Theory, Computer Science, String (Computer Science), String Metric, Damerau? Levenshtein Distance, Spell Checker, Hamming Distance (Alpha Press, 2009).
Durant, J. L., Leland, B. A., Henry, D. R. & Nourse, J. G. Reoptimization of MDL keys for use in drug discovery. J. Chem. Inf. Comput. Sci. 42, 1273–1280 (2002).
Schneider, N., Sayle, R. A. & Landrum, G. A. Get your atoms in order an open-source implementation of a novel and robust molecular canonicalization algorithm. J. Chem. Inf. Model. 55, 2111–2120 (2015).
Bajusz, D., Rácz, A. & Héberger, K. Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations? J. Cheminform. 7, 20 (2015).
Preuer, K., Renz, P., Unterthiner, T., Hochreiter, S. & Klambauer, G. Fréchet ChemNet distance: a metric for generative models for molecules in drug discovery. J. Chem. Inf. Model. 58, 1736–1741 (2018).
Smith, T. F. Identification of common molecular subsequences. J. Mol. Biol. 147, 195–197 (1981).
Henikoff, S. & Henikoff, J. G. Amino acid substitution matrices from protein blocks. Proc. Natl Acad. Sci. USA 89, 10915–10919 (1992).
Qu, Y. et al. MolCRAFT: structure-based drug design in continuous parameter space. In Proc. Forty-first International Conference on Machine Learning (2024).
Corso, G. et al. Deep confident steps to new pockets: strategies for docking generalization. In International Conference on Learning Representations (ICLR, 2024).
Alhossary, A., Handoko, S. D., Mu, Y. & Kwoh, C.-K. Fast, accurate, and reliable molecular docking with QuickVina 2. Bioinformatics 31, 2214–2216 (2015).
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).
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).
Zhuang, X. Dataset for the paper ‘advancing biomolecule understanding and design following human instructions’. Zenodo (2025).
Zhuang, X. HICAI-ZJU/InstructBioMol: version 1.0.0. Zenodo (2025).
Probst, D. & Reymond, Jean-Louis SmilesDrawer: parsing and drawing smiles-encoded molecular structures using client-side Javascript. J. Chem. Inf. Model. 58, 1–7 (2018).
Schrödinger, LLC. The PyMOL molecular graphics system, version 3.0. (2024).
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