Jumper, J. et al. Highly accurate protein structure prediction with alphafold. Nature 596, 583–589 (2021).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Avsec, Ž. et al. Effective gene expression prediction from sequence by integrating long-range interactions. Nat. Methods 18, 1196–1203 (2021).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Baek, M. et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science 373, 871–876 (2021).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Gundersen, O. E. & Kjensmo, S. State of the art: reproducibility in artificial intelligence. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32, No. 1 https://ojs.aaai.org/index.php/AAAI/article/view/11503 (2018).Matschinske, J. et al. The AIMe registry for artificial intelligence in biomedical research. Nat. Methods 18, 1128–1131 (2021).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Kapoor, S. & Narayanan, A. Leakage and the reproducibility crisis in machine-learning-based science. Patterns 4, 100804 (2023). This article presents a taxonomy of common pitfalls that introduce data leakage and lead to overoptimistic results in many scientific fields. The authors also suggest model info sheets to identify and prevent those pitfalls and, ultimately, counteract the reproducibility crisis.Kaufman, S., Rosset, S., Perlich, C. & Stitelman, O. Leakage in data mining: formulation, detection, and avoidance. ACM Trans. Knowl. Discov. Data 6, 1–21 (2012). This article provides a formal definition of data leakage and suggests ways to detect and avoid it.ArticleÂ
Google ScholarÂ
Whalen, S., Schreiber, J., Noble, W. S. & Pollard, K. S. Navigating the pitfalls of applying machine learning in genomics. Nat. Rev. Genet. 23, 169–181 (2022).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Chiavegatto Filho, A., Batista, A. F. D. M. & Dos Santos, H. G. Data leakage in health outcomes prediction with machine learning. Comment on ‘prediction of incident hypertension within the next year: prospective study using statewide electronic health records and machine learning’. J. Med. Internet Res. 23, e10969 (2021).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Cheng, J. et al. Accurate proteome-wide missense variant effect prediction with alphamissense. Science 381, eadg7492 (2023).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Grimm, D. G. et al. The evaluation of tools used to predict the impact of missense variants is hindered by two types of circularity. Hum. Mutat. 36, 513–523 (2015). This article demonstrates two types of circularity that lead to overly optimistic results for deleteriousness prediction tools.ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Schaefer, M. H., Serrano, L. & Andrade-Navarro, M. A. Correcting for the study bias associated with protein–protein interaction measurements reveals differences between protein degree distributions from different cancer types. Front. Genet. 6, 137790 (2015).ArticleÂ
Google ScholarÂ
Lucchetta, M., List, M., Blumenthal, D. B. & Schaefer, M. H. Emergence of power-law distributions in protein–protein interaction networks through study bias. Preprint at bioRxiv https://doi.org/10.1101/2023.03.17.533165 (2023).Ofer, D., Brandes, N. & Linial, M. The language of proteins: Nlp, machine learning & protein sequences. Comput. Struct. Biotechnol. J. 19, 1750–1758 (2021).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Song, C. & Raghunathan, A. Information leakage in embedding models. In Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security, 377–390 (2020).Zhang, G. et al. How does a deep learning model architecture impact its privacy? a comprehensive study of privacy attacks on CNNs and transformers. Preprint at https://arxiv.org/abs/2210.11049 (2022).Rentzsch, P., Witten, D., Cooper, G. M., Shendure, J. & Kircher, M. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res. 47, D886–D894 (2019).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Notin, P. et al. ProteinGym: large-scale benchmarks for protein design and fitness prediction. In Advances in Neural Information Processing Systems 36 (NeurIPS, 2023).Ng, P. C. & Henikoff, S. SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res. 31, 3812–3814 (2003).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Adzhubei, I. A. et al. A method and server for predicting damaging missense mutations. Nat. Methods 7, 248–249 (2010).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Kircher, M. et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 46, 310–315 (2014).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Joeres, R., Blumenthal, D. B. & Kalinina, O. V. Datasail: data splitting against information leakage. Preprint at bioRxiv https://doi.org/10.1101/2023.11.15.566305 (2023).Teufel, F. et al. GraphPart: homology partitioning for biological sequence analysis. NAR Genom. Bioinform. 5, lqad088 (2023).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Weissenow, K., Heinzinger, M., Steinegger, M. & Rost, B. Ultra-fast protein structure prediction to capture effects of sequence variation in mutation movies. Preprint at bioRxiv https://doi.org/10.1101/2022.11.14.516473 (2022).Elnaggar, A. et al. ProtTrans: toward understanding the language of life through self-supervised learning. IEEE Trans. Pattern Anal. Mach. Intell. 44, 7112–7127 (2021).ArticleÂ
Google ScholarÂ
Haselbeck, F. et al. Superior protein thermophilicity prediction with protein language model embeddings. NAR Genom. Bioinform. 5, lqad087 (2023).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Teufel, F. et al. Signalp 6.0 predicts all five types of signal peptides using protein language models. Nat. Biotechnol. 40, 1023–1025 (2022).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Wu, R. et al. High-resolution de novo structure prediction from primary sequence. Preprint at bioRxiv https://doi.org/10.1101/2022.07.21.500999 (2022).Charoenkwan, P. et al. SAPPHIRE: a stacking-based ensemble learning framework for accurate prediction of thermophilic proteins. Comput. Biol. Med. 146, 105704 (2022).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Lin, H. & Chen, W. Prediction of thermophilic proteins using feature selection technique. J. Microbiol. Methods 84, 67–70 (2011).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Ahmed, Z. et al. iThermo: a sequence-based model for identifying thermophilic proteins using a multi-feature fusion strategy. Front. Microbiol. 13, 790063 (2022).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Pei, H. et al. Identification of thermophilic proteins based on sequence-based bidirectional representations from transformer-embedding features. Appl. Sci. 13, 2858 (2023).ArticleÂ
CASÂ
Google ScholarÂ
PudžiuvelytÄ—, I. et al. TemStaPro: protein thermostability prediction using sequence representations from protein language models. Bioinformatics 40, btae157 (2024).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Pucci, F., Bernaerts, K. V., Kwasigroch, J. M. & Rooman, M. Quantification of biases in predictions of protein stability changes upon mutations. Bioinformatics 34, 3659–3665 (2018). This article analyzes biases in protein stability prediction tools and shows that most predictors favor destabilizing mutations. The authors also propose a new method addressing this issue by imposing physical symmetries under inverse mutations.ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Usmanova, D. R. et al. Self-consistency test reveals systematic bias in programs for prediction change of stability upon mutation. Bioinformatics 34, 3653–3658 (2018).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Fang, J. The role of data imbalance bias in the prediction of protein stability change upon mutation. PLoS ONE 18, e0283727 (2023).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Sanavia, T. et al. Limitations and challenges in protein stability prediction upon genome variations: towards future applications in precision medicine. Comput. Struct. Biotechnol. J. 18, 1968–1979 (2020).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Stourac, J. et al. Fireprotdb: database of manually curated protein stability data. Nucleic Acids Res. 49, D319–D324 (2021).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Rodrigues, C. H., Pires, D. E. & Ascher, D. B. Dynamut2: Assessing changes in stability and flexibility upon single and multiple point missense mutations. Protein Sci. 30, 60–69 (2021).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Fang, J. A critical review of five machine learning-based algorithms for predicting protein stability changes upon mutation. Brief. Bioinform. 21, 1285–1292 (2019).ArticleÂ
PubMed CentralÂ
Google ScholarÂ
Menche, J. et al. Uncovering disease-disease relationships through the incomplete interactome. Science 347, 1257601 (2015).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Batra, R. et al. On the performance of de novo pathway enrichment. NPJ Syst. Biol. Appl. 3, 6 (2017).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Bernett, J., Blumenthal, D. B. & List, M. Cracking the black box of deep sequence-based protein–protein interaction prediction. Brief. Bioinform. 25, bbae076 (2024). This article shows that reported performances of numerous deep learning-based protein–protein interaction prediction models are massively inflated due to data leakage. The authors also provide a leakage-free gold-standard dataset to foster the development of better protein–protein interaction predictors in the future.ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Park, Y. & Marcotte, E. M. Flaws in evaluation schemes for pair-input computational predictions. Nat. Methods 9, 1134–1136 (2012).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Dunham, B. & Ganapathiraju, M. K. Benchmark evaluation of protein–protein interaction prediction algorithms. Molecules 27, 41 (2021).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Hamp, T. & Rost, B. Evolutionary profiles improve protein–protein interaction prediction from sequence. Bioinformatics 31, 1945–1950 (2015).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Blohm, P. et al. Negatome 2.0: a database of non-interacting proteins derived by literature mining, manual annotation and protein structure analysis. Nucleic Acids Res. 42, D396–D400 (2014).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Ben-Hur, A. & Noble, W. S. Choosing negative examples for the prediction of protein–protein interactions. BMC Bioinformatics 7, S2 (2006).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Tabar, M. S. et al. Illuminating the dark protein–protein interactome. Cell Rep. Methods 2, 100275 (2022).Aloy, P., Ceulemans, H., Stark, A. & Russell, R. B. The relationship between sequence and interaction divergence in proteins. J. Mol. Biol. 332, 989–998 (2003).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Marsh, J. A. & Teichmann, S. A. Structure, dynamics, assembly, and evolution of protein complexes. Annu. Rev. Biochem. 84, 551–575 (2015).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Lin, Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379, 1123–1130 (2023).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Madani, A. et al. Large language models generate functional protein sequences across diverse families. Nat. Biotechnol. 41, 1099–1106 (2023).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Yao, Y., Du, X., Diao, Y. & Zhu, H. An integration of deep learning with feature embedding for protein–protein interaction prediction. PeerJ 7, e7126 (2019).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Chen, M. et al. Multifaceted protein–protein interaction prediction based on Siamese residual RCNN. Bioinformatics 35, i305–i314 (2019).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Davis, M. I. et al. Comprehensive analysis of kinase inhibitor selectivity. Nat. Biotechnol. 29, 1046–1051 (2011).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Tang, J. et al. Making sense of large-scale kinase inhibitor bioactivity data sets: a comparative and integrative analysis. J. Chem. Inf. Model. 54, 735–743 (2014).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Liu, Z. et al. PDB-wide collection of binding data: current status of the PDBbind database. Bioinformatics 31, 405–412 (2015).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Liu, T., Lin, Y., Wen, X., Jorissen, R. N. & Gilson, M. K. BindingDB: a web-accessible database of experimentally determined protein–ligand binding affinities. Nucleic Acids Res. 35, D198–D201 (2007).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Chatterjee, A. et al. Improving the generalizability of protein–ligand binding predictions with AI-Bind. Nat. Commun. 14, 1989 (2023). This article shows how deep learning models for drug–target interaction prediction learn shortcuts from the topology of the training network instead of hidden mechanisms and, hence, generalize poorly. The authors further propose a new method designed to overcome these shortcomings.ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Bai, P. et al. Hierarchical clustering split for low-bias evaluation of drug–target interaction prediction. In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 641–644 (IEEE, 2021).Torrisi, M., de la Vega de León, A., Climent, G., Loos, R. & Panjkovich, A. Improving the assessment of deep learning models in the context of drug–target interaction prediction. Preprint at bioRxiv https://doi.org/10.1101/2022.04.20.488898 (2022).Chan, W. K. et al. GLASS: a comprehensive database for experimentally validated GPCR–ligand associations. Bioinformatics 31, 3035–3042 (2015).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Ramsundar, B. Molecular machine learning with DeepChem. Ph.D. thesis, Stanford University (2018).Huang, K. et al. Artificial intelligence foundation for therapeutic science. Nat. Chem. Biol. 18, 1033–1036 (2022).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Steshin, S. Lo-Hi: practical Ml drug discovery benchmark. In Advances in Neural Information Processing Systems 36 (NeurIPS, 2023).Elnaggar, A. et al. Ankh: optimized protein language model unlocks general-purpose modelling. Preprint at https://arxiv.org/abs/2301.06568 (2023).Chithrananda, S., Grand, G. & Ramsundar, B. ChemBERTa: large-scale self-supervised pretraining for molecular property prediction. Preprint at https://arxiv.org/abs/2010.09885 (2020).Kim, S. et al. Pubchem 2019 update: improved access to chemical data. Nucleic Acids Res. 47, D1102–D1109 (2019).ArticleÂ
PubMedÂ
Google ScholarÂ
Paszke, A. et al. PyTorch: an imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems 32 (NeurIPS, 2019).Abadi, M. et al. TensorFlow: large-scale machine learning on heterogeneous systems. In 12th USENIX Symposium on Operating Systems Design and Implementation (USENIX, 2016).Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
Google ScholarÂ
Hastie, T., Tibshirani, R. & Friedman, J. H. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, vol. 2 (Springer, 2009).Goodfellow, I., Bengio, Y. & Courville, A. Deep Learning (MIT Press, 2016); http://www.deeplearningbook.org/Ching, T. et al. Opportunities and obstacles for deep learning in biology and medicine. J. R. Soc. Interface 15, 20170387 (2018).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Goodman, S. N., Fanelli, D. & Ioannidis, J. P. A. What does research reproducibility mean? Sci. Transl. Med. 8, 341ps12 (2016). This article provides a subdivision of the term ‘reproducibility’ into ‘methods reproducibility’, ‘results reproducibility’ and ‘inferential reproducibility’. Data leakage is one important source of lack of inferential reproducibility.ArticleÂ
PubMedÂ
Google ScholarÂ