Artificial intelligence in drug development
Paul, S. M. et al. How to improve R&D productivity: the pharmaceutical industry’s grand challenge. Nat. Rev. Drug Discov. 9, 203–214 (2010).
Google Scholar
Vamathevan, J. et al. Applications of machine learning in drug discovery and development. Nat. Rev. Drug Discov. 18, 463–477 (2019).
Google Scholar
DiMasi, J. A., Grabowski, H. G. & Hansen, R. W. Innovation in the pharmaceutical industry: new estimates of R&D costs. J. Health Econ. 47, 20–33 (2016).
Google Scholar
Mullard, A. The drug-maker’s guide to the galaxy. Nature 549, 445–447 (2017).
Google Scholar
Wang, H. et al. Scientific discovery in the age of artificial intelligence. Nature 620, 47–60 (2023).
Google Scholar
Van Dis, E. A., Bollen, J., Zuidema, W., Van Rooij, R. & Bockting, C. L. ChatGPT: five priorities for research. Nature 614, 224–226 (2023).
Google Scholar
Gemini Team Google. Gemini: a family of highly capable multimodal models. Preprint at (2023).
O’Callaghan, J. How OpenAI’s text-to-video tool Sora could change science—and society. Nature 627, 475–476 (2024).
Google Scholar
Sadybekov, A. V. & Katritch, V. Computational approaches streamlining drug discovery. Nature 616, 673–685 (2023).
Google Scholar
Savage, N. Tapping into the drug discovery potential of AI. Biopharm. Deal. (2021).
Dealmakers, B. Generative AI platforms drive drug discovery dealmaking. Biopharm. Deal. (2024).
Cohen, A. A. et al. A complex systems approach to aging biology. Nat. Aging 2, 580–591 (2022).
Google Scholar
Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nat. Mach. Intell. 1, 133–143 (2019).
Google Scholar
Steyaert, S. et al. Multimodal data fusion for cancer biomarker discovery with deep learning. Nat. Mach. Intell. 5, 351–362 (2023).
Google Scholar
You, Y. et al. Artificial intelligence in cancer target identification and drug discovery. Signal Transduct. Target. Ther. 7, 156 (2022).
Google Scholar
Pun, F. W., Ozerov, I. V. & Zhavoronkov, A. AI-powered therapeutic target discovery. Trends Pharmacol. Sci. 44, 561–572 (2023).
Google Scholar
Bagherian, M. et al. Machine learning approaches and databases for prediction of drug–target interaction: a survey paper. Brief. Bioinform. 22, 247–269 (2021).
Google Scholar
Zhang, Z. et al. Graph neural network approaches for drug–target interactions. Curr. Opin. Struct. Biol. 73, 102327 (2022).
Google Scholar
Catacutan, D. B., Alexander, J., Arnold, A. & Stokes, J. M. Machine learning in preclinical drug discovery. Nat. Chem. Biol. 20, 960–973 (2024).
Google Scholar
Mullowney, M. W. et al. Artificial intelligence for natural product drug discovery. Nat. Rev. Drug Discov. 22, 895–916 (2023).
Google Scholar
Al-Worafi, Y. M. Technology for Drug Safety: Current Status and Future Developments (Springer, 2023).
Subbiah, V. The next generation of evidence-based medicine. Nat. Med. 29, 49–58 (2023).
Google Scholar
Acosta, J. N., Falcone, G. J., Rajpurkar, P. & Topol, E. J. Multimodal biomedical AI. Nat. Med. 28, 1773–1784 (2022).
Google Scholar
Yang, X., Wang, Y., Byrne, R., Schneider, G. & Yang, S. Concepts of artificial intelligence for computer-assisted drug discovery. Chem. Rev. 119, 10520–10594 (2019).
Google Scholar
Schneider, P. et al. Rethinking drug design in the artificial intelligence era. Nat. Rev. Drug Discov. 19, 353–364 (2020).
Google Scholar
Chen, H. et al. Drug target prediction through deep learning functional representation of gene signatures. Nat. Commun. 15, 1853 (2024).
Google Scholar
Tasaki, S. et al. Inferring protein expression changes from mRNA in Alzheimer’s dementia using deep neural networks. Nat. Commun. 13, 655 (2022).
Google Scholar
Rodriguez, S. et al. Machine learning identifies candidates for drug repurposing in Alzheimer’s disease. Nat. Commun. 12, 1033 (2021).
Google Scholar
Schulte-Sasse, R., Budach, S., Hnisz, D. & Marsico, A. Integration of multi-omics data with graph convolutional networks to identify new cancer genes and their associated molecular mechanisms. Nat. Mach. Intell. 3, 513–526 (2021).
Google Scholar
Hong, C., Cao, Q., Zhang, Z., Tsui, S. K.-W. & Yip, K. Y. Reusability report: capturing properties of biological objects and their relationships using graph neural networks. Nat. Mach. Intell. 4, 222–226 (2022).
Google Scholar
Ratajczak, F. et al. Speos: an ensemble graph representation learning framework to predict core gene candidates for complex diseases. Nat. Commun. 14, 7206 (2023).
Google Scholar
Li, H. et al. CGMega: explainable graph neural network framework with attention mechanisms for cancer gene module dissection. Nat. Commun. 15, 5997 (2024).
Google Scholar
Youn, J., Rai, N. & Tagkopoulos, I. Knowledge integration and decision support for accelerated discovery of antibiotic resistance genes. Nat. Commun. 13, 2360 (2022).
Google Scholar
Gogleva, A. et al. Knowledge graph-based recommendation framework identifies drivers of resistance in EGFR mutant non-small cell lung cancer. Nat. Commun. 13, 1667 (2022).
Google Scholar
Ge, Y. et al. An integrative drug repositioning framework discovered a potential therapeutic agent targeting COVID-19. Signal Transduct. Target. Ther. 6, 165 (2021).
Google Scholar
Luo, R. et al. BioGPT: generative pre-trained transformer for biomedical text generation and mining. Brief. Bioinform. 23, bbac409 (2022).
Google Scholar
Ren, F. et al. A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models. Nat. Biotechnol. (2024).
Cosentino, J. et al. Inference of chronic obstructive pulmonary disease with deep learning on raw spirograms identifies new genetic loci and improves risk models. Nat. Genet. 55, 787–795 (2023).
Google Scholar
Liu, R., Wei, L. & Zhang, P. A deep learning framework for drug repurposing via emulating clinical trials on real-world patient data. Nat. Mach. Intell. 3, 68–75 (2021).
Google Scholar
Deep learning model improves COPD risk prediction and gene discovery. Nat. Genet. 55, 738–739 (2023).
Tingle, B. I. et al. ZINC-22—a free multi-billion-scale database of tangible compounds for ligand discovery. J. Chem. Inf. Model. 63, 1166–1176 (2023).
Google Scholar
Tran-Nguyen, V.-K., Junaid, M., Simeon, S. & Ballester, P. J. A practical guide to machine-learning scoring for structure-based virtual screening. Nat. Protoc. 18, 3460–3511 (2023).
Google Scholar
Gentile, F. et al. Artificial intelligence–enabled virtual screening of ultra-large chemical libraries with deep docking. Nat. Protoc. 17, 672–697 (2022).
Google Scholar
Stärk, H., Ganea, O., Pattanaik, L., Barzilay, R. & Jaakkola, T. Equibind: geometric deep learning for drug binding structure prediction. In Proc. 39th International Conference on Machine Learning (eds Chaudhuri, K. et al.) 20503–20521 (PMLR, 2022).
Zhang, X. et al. Efficient and accurate large library ligand docking with KarmaDock. Nat. Comput. Sci. 3, 789–804 (2023).
Google Scholar
Qiao, Z., Nie, W., Vahdat, A., Miller, T. F. III. & Anandkumar, A. State-specific protein–ligand complex structure prediction with a multiscale deep generative model. Nat. Mach. Intell. 6, 195–208 (2024).
Google Scholar
Peng, X. et al. Characterizing the interaction conformation between T-cell receptors and epitopes with deep learning. Nat. Mach. Intell. 5, 395–407 (2023).
Google Scholar
Méndez-Lucio, O., Ahmad, M., Rio-Chanona, E. A. D. & Wegner, J. K. A geometric deep learning approach to predict binding conformations of bioactive molecules. Nat. Mach. Intell. 3, 1033–1039 (2021).
Google Scholar
Lu, W. et al. TANKBind: trigonometry-aware neural networks for drug-protein binding structure prediction. Adv. Neural Inf. Process. Syst. 35, 7236–7249 (2022).
Bryant, P., Kelkar, A., Guljas, A., Clementi, C. & Noé, F. Structure prediction of protein–ligand complexes from sequence information with Umol. Nat. Commun. 15, 4536 (2024).
Google Scholar
Krishna, R. et al. Generalized biomolecular modeling and design with RoseTTAFold All-Atom. Science 384, eadl2528 (2024).
Google Scholar
Tsaban, T. et al. Harnessing protein folding neural networks for peptide–protein docking. Nat. Commun. 13, 176 (2022).
Google Scholar
Lin, P., Tao, H., Li, H. & Huang, S.-Y. Protein–protein contact prediction by geometric triangle-aware protein language models. Nat. Mach. Intell. 5, 1275–1284 (2023).
Google Scholar
Yu, Y., Lu, S., Gao, Z., Zheng, H. & Ke, G. Do deep learning models really outperform traditional approaches in molecular docking? In Proc. ICLR 2023, Machine Learning for Drug Discovery workshop (ICLR, 2023).
Luo, Y., Liu, Y. & Peng, J. Calibrated geometric deep learning improves kinase–drug binding predictions. Nat. Mach. Intell. 5, 1390–1401 (2023).
Google Scholar
Renaud, N. et al. DeepRank: a deep learning framework for data mining 3D protein–protein interfaces. Nat. Commun. 12, 7068 (2021).
Google Scholar
Gainza, P. et al. Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning. Nat. Methods 17, 184–192 (2020).
Google Scholar
Stebliankin, V. et al. Evaluating protein binding interfaces with transformer networks. Nat. Mach. Intell. 5, 1042–1053 (2023).
Google Scholar
Réau, M., Renaud, N., Xue, L. C. & Bonvin, A. M. DeepRank-GNN: a graph neural network framework to learn patterns in protein–protein interfaces. Bioinformatics 39, btac759 (2023).
Google Scholar
Shen, C. et al. Boosting protein–ligand binding pose prediction and virtual screening based on residue–atom distance likelihood potential and graph transformer. J. Med. Chem. 65, 10691–10706 (2022).
Google Scholar
Shen, C. et al. A generalized protein–ligand scoring framework with balanced scoring, docking, ranking and screening powers. Chem. Sci. 14, 8129–8146 (2023).
Google Scholar
Zhang, X. et al. Planet: a multi-objective graph neural network model for protein–ligand binding affinity prediction. J. Chem. Inf. Model. 64, 2205–2220 (2023).
Google Scholar
Yu, J. et al. Computing the relative binding affinity of ligands based on a pairwise binding comparison network. Nat. Comput. Sci. 3, 860–872 (2023).
Google Scholar
Sverrisson, F., Feydy, J., Correia, B. E. & Bronstein, M. M. Fast end-to-end learning on protein surfaces. In Proc. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition 15267–15276 (IEEE, 2021).
Chen, L. et al. Sequence-based drug design as a concept in computational drug design. Nat. Commun. 14, 4217 (2023).
Google Scholar
Stokes, J. M. et al. A deep learning approach to antibiotic discovery. Cell 180, 688–702 (2020).
Google Scholar
Liu, G. et al. Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii. Nat. Chem. Biol. 19, 1342–1350 (2023).
Google Scholar
Wong, F. et al. Discovery of a structural class of antibiotics with explainable deep learning. Nature 626, 177–185 (2024).
Google Scholar
Wong, F., Omori, S., Donghia, N. M., Zheng, E. J. & Collins, J. J. Discovering small-molecule senolytics with deep neural networks. Nat. Aging 3, 734–750 (2023).
Google Scholar
Smer-Barreto, V. et al. Discovery of senolytics using machine learning. Nat. Commun. 14, 3445 (2023).
Google Scholar
Tong, X. et al. Deep representation learning of chemical-induced transcriptional profile for phenotype-based drug discovery. Nat. Commun. 15, 5378 (2024).
Google Scholar
Zhu, J. et al. Prediction of drug efficacy from transcriptional profiles with deep learning. Nat. Biotechnol. 39, 1444–1452 (2021).
Google Scholar
Duran, I. et al. Detection of senescence using machine learning algorithms based on nuclear features. Nat. Commun. 15, 1041 (2024).
Google Scholar
Tilborg, D. V., Alenicheva, A. & Grisoni, F. Exposing the limitations of molecular machine learning with activity cliffs. J. Chem. Inf. Model. 62, 5938–5951 (2022).
Google Scholar
Moshkov, N. et al. Predicting compound activity from phenotypic profiles and chemical structures. Nat. Commun. 14, 1967 (2023).
Google Scholar
Scantlebury, J. et al. A small step toward generalizability: training a machine learning scoring function for structure-based virtual screening. J. Chem. Inf. Model. 63, 2960–2974 (2023).
Google Scholar
Yu, L., He, X., Fang, X., Liu, L. & Liu, J. Deep learning with geometry-enhanced molecular representation for augmentation of large-scale docking-based virtual screening. J. Chem. Inf. Model. 63, 6501–6514 (2023).
Google Scholar
Lam, H. Y. I. et al. Application of variational graph encoders as an effective generalist algorithm in computer-aided drug design. Nat. Mach. Intell. 5, 754–764 (2023).
Google Scholar
Moon, S., Zhung, W., Yang, S., Lim, J. & Kim, W. Y. PIGNet: a physics-informed deep learning model toward generalized drug–target interaction predictions. Chem. Sci. 13, 3661–3673 (2022).
Google Scholar
Moon, S., Hwang, S.-Y., Lim, J. & Kim, W. Y. PIGNet2: a versatile deep learning-based protein–ligand interaction prediction model for binding affinity scoring and virtual screening. Digit. Discov. 3, 287–299 (2024).
Google Scholar
Lyu, J., Irwin, J. J. & Shoichet, B. K. Modeling the expansion of virtual screening libraries. Nat. Chem. Biol. 19, 712–718 (2023).
Google Scholar
Cao, Z., Sciabola, S. & Wang, Y. Large-scale pretraining improves sample efficiency of active learning-based virtual screening. J. Chem. Inf. Model. 64, 1882–1891 (2024).
Google Scholar
Lilienfeld, O. A. V., Müller, K.-R. & Tkatchenko, A. Exploring chemical compound space with quantum-based machine learning. Nat. Rev. Chem. 4, 347–358 (2020).
Google Scholar
Korlepara, D. B. et al. PLAS-20k: extended dataset of protein–ligand affinities from md simulations for machine learning applications. Sci. Data 11, 180 (2024).
Google Scholar
Moret, M. et al. Leveraging molecular structure and bioactivity with chemical language models for de novo drug design. Nat. Commun. 14, 114 (2023).
Google Scholar
Li, Y. et al. Generative deep learning enables the discovery of a potent and selective RIPK1 inhibitor. Nat. Commun. 13, 6891 (2022).
Google Scholar
Chenthamarakshan, V. et al. Accelerating drug target inhibitor discovery with a deep generative foundation model. Sci. Adv. 9, eadg7865 (2023).
Google Scholar
Zheng, S. et al. Accelerated rational PROTAC design via deep learning and molecular simulations. Nat. Mach. Intell. 4, 739–748 (2022).
Google Scholar
Chen, S. et al. Design of target specific peptide inhibitors using generative deep learning and molecular dynamics simulations. Nat. Commun. 15, 1611 (2024).
Google Scholar
Pandi, A. et al. Cell-free biosynthesis combined with deep learning accelerates de novo-development of antimicrobial peptides. Nat. Commun. 14, 7197 (2023).
Google Scholar
Szymczak, P. et al. Discovering highly potent antimicrobial peptides with deep generative model HydrAMP. Nat. Commun. 14, 1453 (2023).
Google Scholar
Notin, P., Rollins, N., Gal, Y., Sander, C. & Marks, D. Machine learning for functional protein design. Nat. Biotechnol. 42, 216–228 (2024).
Google Scholar
Torres, S. V. et al. De novo design of high-affinity binders of bioactive helical peptides. Nature 626, 435–442 (2024).
Google Scholar
Wang, J. et al. Multi-constraint molecular generation based on conditional transformer, knowledge distillation and reinforcement learning. Nat. Mach. Intell. 3, 914–922 (2021).
Google Scholar
Born, J. & Manica, M. Regression transformer enables concurrent sequence regression and generation for molecular language modelling. Nat. Mach. Intell. 5, 432–444 (2023).
Google Scholar
Flam-Shepherd, D., Zhu, K. & Aspuru-Guzik, A. Language models can learn complex molecular distributions. Nat. Commun. 13, 3293 (2022).
Google Scholar
Skinnider, M. A., Stacey, R. G., Wishart, D. S. & Foster, L. J. Chemical language models enable navigation in sparsely populated chemical space. Nat. Mach. Intell. 3, 759–770 (2021).
Google Scholar
Skinnider, M. A. Invalid SMILES are beneficial rather than detrimental to chemical language models. Nat. Mach. Intell. 6, 437–448 (2024).
Google Scholar
Özçelik, R., de Ruiter, S., Criscuolo, E. & Grisoni, F. Chemical language modeling with structured state space sequence models. Nat. Commun. 15, 6176 (2024).
Google Scholar
Zhang, O. et al. Learning on topological surface and geometric structure for 3D molecular generation. Nat. Comput. Sci. 3, 849–859 (2023).
Google Scholar
Gebauer, N. W., Gastegger, M., Hessmann, S. S., Müller, K.-R. & Schütt, K. T. Inverse design of 3D molecular structures with conditional generative neural networks. Nat. Commun. 13, 973 (2022).
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
Godinez, W. J. et al. Design of potent antimalarials with generative chemistry. Nat. Mach. Intell. 4, 180–186 (2022).
Google Scholar
Hoogeboom, E., Satorras, V. G., Vignac, C. & Welling, M. Equivariant diffusion for molecule generation in 3D. In Proc. 39th International Conference on Machine Learning (eds Chaudhuri, K. et al.) 8867–8887 (PMLR, 2022).
Renz, P., Rompaey, D. V., Wegner, J. K., Hochreiter, S. & Klambauer, G. On failure modes in molecule generation and optimization. Drug Discov. Today Technol. 32, 55–63 (2019).
Google Scholar
Dodds, M. et al. Sample efficient reinforcement learning with active learning for molecular design. Chem. Sci. 15, 4146–4160 (2024).
Google Scholar
Guo, J. et al. Improving de novo molecular design with curriculum learning. Nat. Mach. Intell. 4, 555–563 (2022).
Google Scholar
Mokaya, M. et al. Testing the limits of SMILES-based de novo molecular generation with curriculum and deep reinforcement learning. Nat. Mach. Intell. 5, 386–394 (2023).
Google Scholar
Méndez-Lucio, O., Baillif, B., Clevert, D.-A., Rouquié, D. & Wichard, J. De novo generation of hit-like molecules from gene expression signatures using artificial intelligence. Nat. Commun. 11, 10 (2020).
Google Scholar
Zhu, H., Zhou, R., Cao, D., Tang, J. & Li, M. A pharmacophore-guided deep learning approach for bioactive molecular generation. Nat. Commun. 14, 6234 (2023).
Google Scholar
Li, Y., Pei, J. & Lai, L. Structure-based de novo drug design using 3D deep generative models. Chem. Sci. 12, 13664–13675 (2021).
Google Scholar
Feng, W. et al. Generation of 3D molecules in pockets via a language model. Nat. Mach. Intell. 6, 62–73 (2024).
Google Scholar
Huang, L. et al. A dual diffusion model enables 3D molecule generation and lead optimization based on target pockets. Nat. Commun. 15, 2657 (2024).
Google Scholar
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
Hoffman, S. C., Chenthamarakshan, V., Wadhawan, K., Chen, P.-Y. & Das, P. Optimizing molecules using efficient queries from property evaluations. Nat. Mach. Intell. 4, 21–31 (2022).
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
Atz, K. et al. Prospective de novo drug design with deep interactome learning. Nat. Commun. 15, 3408 (2024).
Google Scholar
Chen, Z., Min, M. R., Parthasarathy, S. & Ning, X. A deep generative model for molecule optimization via one fragment modification. Nat. Mach. Intell. 3, 1040–1049 (2021).
Google Scholar
Bhhatarai, B., Walters, W. P., Hop, C. E., Lanza, G. & Ekins, S. Opportunities and challenges using artificial intelligence in ADME/Tox. Nat. Mater. 18, 418–422 (2019).
Google Scholar
Göller, A. H. et al. Bayer’s in silico ADMET platform: a journey of machine learning over the past two decades. Drug Discov. Today 25, 1702–1709 (2020).
Google Scholar
Li, Z., Jiang, M., Wang, S. & Zhang, S. Deep learning methods for molecular representation and property prediction. Drug Discov. Today 27, 103373 (2022).
Google Scholar
Atz, K., Grisoni, F. & Schneider, G. Geometric deep learning on molecular representations. Nat. Mach. Intell. 3, 1023–1032 (2021).
Google Scholar
Boulougouri, M., Vandergheynst, P. & Probst, D. Molecular set representation learning. Nat. Mach. Intell. 6, 754–763 (2024).
Google Scholar
Qu, S., Huang, S., Pan, X., Yang, L. & Mei, H. Constructing interconsistent, reasonable, and predictive models for both the kinetic and thermodynamic properties of HIV-1 protease inhibitors. J. Chem. Inf. Model. 56, 2061–2068 (2016).
Google Scholar
Turon, G. et al. First fully-automated AI/ML virtual screening cascade implemented at a drug discovery centre in Africa. Nat. Commun. 14, 5736 (2023).
Google Scholar
Yoshikai, Y., Mizuno, T., Nemoto, S. & Kusuhara, H. Difficulty in chirality recognition for Transformer architectures learning chemical structures from string representations. Nat. Commun. 15, 1197 (2024).
Google Scholar
Chen, D. et al. Algebraic graph-assisted bidirectional transformers for molecular property prediction. Nat. Commun. 12, 3521 (2021).
Google Scholar
Zeng, X. et al. Accurate prediction of molecular properties and drug targets using a self-supervised image representation learning framework. Nat. Mach. Intell. 4, 1004–1016 (2022).
Google Scholar
Fang, X. et al. Geometry-enhanced molecular representation learning for property prediction. Nat. Mach. Intell. 4, 127–134 (2022).
Google Scholar
Ross, J. et al. Large-scale chemical language representations capture molecular structure and properties. Nat. Mach. Intell. 4, 1256–1264 (2022).
Google Scholar
Li, H. et al. A knowledge-guided pre-training framework for improving molecular representation learning. Nat. Commun. 14, 7568 (2023).
Google Scholar
Chang, J. & Ye, J. C. Bidirectional generation of structure and properties through a single molecular foundation model. Nat. Commun. 15, 2323 (2024).
Google Scholar
Kundu, S. AI in medicine must be explainable. Nat. Med. 27, 1328 (2021).
Google Scholar
Jiménez-Luna, J., Grisoni, F. & Schneider, G. Drug discovery with explainable artificial intelligence. Nat. Mach. Intell. 2, 573–584 (2020).
Google Scholar
Zhong, Y. et al. Learning motif-based graphs for drug–drug interaction prediction via local–global self-attention. Nat. Mach. Intell. 6, 1094–1105 (2024).
Google Scholar
Fang, Y. et al. Knowledge graph-enhanced molecular contrastive learning with functional prompt. Nat. Mach. Intell. 5, 542–553 (2023).
Google Scholar
Shen, Y. et al. Automation and computer-assisted planning for chemical synthesis. Nat. Rev. Methods Prim. 1, 23 (2021).
Google Scholar
Abolhasani, M. & Kumacheva, E. The rise of self-driving labs in chemical and materials sciences. Nat. Synth. 2, 483–492 (2023).
Google Scholar
Bai, J. et al. A dynamic knowledge graph approach to distributed self-driving laboratories. Nat. Commun. 15, 462 (2024).
Google Scholar
Vaucher, A. C. et al. Automated extraction of chemical synthesis actions from experimental procedures. Nat. Commun. 11, 3601 (2020).
Google Scholar
Vaucher, A. C. et al. Inferring experimental procedures from text-based representations of chemical reactions. Nat. Commun. 12, 2573 (2021).
Google Scholar
Tu, X. et al. Artificial intelligence-enabled discovery of a RIPK3 inhibitor with neuroprotective effects in an acute glaucoma mouse model. Chin. Med. J. (2024).
Wipke, W., Braun, H., Smith, G., Choplin, F. & Sieber, W. SECS—Simulation and Evaluation of Chemical Synthesis: Strategy and Planning (ACS Publications, 1977).
Huang, Q., Li, L.-L. & Yang, S.-Y. RASA: a rapid retrosynthesis-based scoring method for the assessment of synthetic accessibility of drug-like molecules. J. Chem. Inf. Model. 51, 2768–2777 (2011).
Google Scholar
Coley, C. W. et al. A robotic platform for flow synthesis of organic compounds informed by AI planning. Science 365, eaax1566 (2019).
Google Scholar
Button, A., Merk, D., Hiss, J. A. & Schneider, G. Automated de novo molecular design by hybrid machine intelligence and rule-driven chemical synthesis. Nat. Mach. Intell. 1, 307–315 (2019).
Google Scholar
Grisoni, F. et al. Combining generative artificial intelligence and on-chip synthesis for de novo drug design. Sci. Adv. 7, eabg3338 (2021).
Google Scholar
Mikulak-Klucznik, B. et al. Computational planning of the synthesis of complex natural products. Nature 588, 83–88 (2020).
Google Scholar
Zhang, P. et al. A neural network model informs the total synthesis of clovane sesquiterpenoids. Nat. Synth. 2, 527–534 (2023).
Google Scholar
Tetko, I. V., Karpov, P., Van Deursen, R. & Godin, G. State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis. Nat. Commun. 11, 5575 (2020).
Google Scholar
Fang, L., Li, J., Zhao, M., Tan, L. & Lou, J.-G. Single-step retrosynthesis prediction by leveraging commonly preserved substructures. Nat. Commun. 14, 2446 (2023).
Google Scholar
Pesciullesi, G., Schwaller, P., Laino, T. & Reymond, J.-L. Transfer learning enables the molecular transformer to predict regio-and stereoselective reactions on carbohydrates. Nat. Commun. 11, 4874 (2020).
Google Scholar
Schwaller, P. et al. Mapping the space of chemical reactions using attention-based neural networks. Nat. Mach. Intell. 3, 144–152 (2021).
Google Scholar
Wang, Y. et al. Retrosynthesis prediction with an interpretable deep-learning framework based on molecular assembly tasks. Nat. Commun. 14, 6155 (2023).
Google Scholar
Koscher, B. A. et al. Autonomous, multiproperty-driven molecular discovery: from predictions to measurements and back. Science 382, eadi1407 (2023).
Google Scholar
Steiner, S. et al. Organic synthesis in a modular robotic system driven by a chemical programming language. Science 363, eaav2211 (2019).
Google Scholar
Liu, C. et al. Automated synthesis of prexasertib and derivatives enabled by continuous-flow solid-phase synthesis. Nat. Chem. 13, 451–457 (2021).
Google Scholar
Schneider, G. Automating drug discovery. Nat. Rev. Drug Discov. 17, 97–113 (2018).
Google Scholar
Boiko, D. A., MacKnight, R., Kline, B. & Gomes, G. Autonomous chemical research with large language models. Nature 624, 570–578 (2023).
Google Scholar
Bran, A. M. et al. Augmenting large language models with chemistry tools. Nat. Mach. Intell. 6, 525–535 (2024).
Li, X. et al. Ultrasensitive sensors reveal the spatiotemporal landscape of lactate metabolism in physiology and disease. Cell Metab. 35, 200–211 (2023).
Google Scholar
Simm, J. et al. Repurposing high-throughput image assays enables biological activity prediction for drug discovery. Cell Chem. Biol. 25, 611–618 (2018).
Google Scholar
Yu, M. et al. Deep learning large-scale drug discovery and repurposing. Nat. Comput. Sci. 4, 600–614 (2024).
Google Scholar
Tang, Q. et al. Morphological profiling for drug discovery in the era of deep learning. Brief. Bioinform. 25, bbae284 (2024).
Google Scholar
Claudio Quiros, A. et al. Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unannotated pathology slides. Nat. Commun. 15, 4596 (2024).
Google Scholar
Huang, Z. et al. A pathologist—AI collaboration framework for enhancing diagnostic accuracies and efficiencies. Nat. Biomed. Eng. (2024).
Sung, M. et al. Three-dimensional label-free morphology of CD8+ T cells as a sepsis biomarker. Light Sci. Appl. 12, 265 (2023).
Google Scholar
Heckenbach, I. et al. Nuclear morphology is a deep learning biomarker of cellular senescence. Nat. Aging 2, 742–755 (2022).
Google Scholar
Niu, L. et al. Noninvasive proteomic biomarkers for alcohol-related liver disease. Nat. Med. 28, 1277–1287 (2022).
Google Scholar
Skrede, O.-J. et al. Deep learning for prediction of colorectal cancer outcome: a discovery and validation study. Lancet 395, 350–360 (2020).
Google Scholar
Lee, Y. et al. Derivation of prognostic contextual histopathological features from whole-slide images of tumours via graph deep learning. Nat. Biomed. Eng. (2022).
Google Scholar
Hoang, D.-T. et al. A deep-learning framework to predict cancer treatment response from histopathology images through imputed transcriptomics. Nat. Cancer 5, 1305–1317 (2024).
Google Scholar
Wang, S. et al. Machine learning-based extrachromosomal DNA identification in large-scale cohorts reveals its clinical implications in cancer. Nat. Commun. 15, 1515 (2024).
Google Scholar
Kather, J. N. et al. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat. Med. 25, 1054–1056 (2019).
Google Scholar
Echle, A. et al. Clinical-grade detection of microsatellite instability in colorectal tumors by deep learning. Gastroenterology 159, 1406–1416 (2020).
Google Scholar
Yamashita, R. et al. Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study. Lancet Oncol. 22, 132–141 (2021).
Google Scholar
Echle, A. et al. Deep learning for the detection of microsatellite instability from histology images in colorectal cancer: a systematic literature review. ImmunoInformatics 3, 100008 (2021).
Google Scholar
Bilal, M. et al. Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: a retrospective study. Lancet Digit. 3, e763–e772 (2021).
Google Scholar
Muti, H. S. et al. Development and validation of deep learning classifiers to detect Epstein-Barr virus and microsatellite instability status in gastric cancer: a retrospective multicentre cohort study. Lancet Digit. 3, e654–e664 (2021).
Google Scholar
Hong, R., Liu, W., DeLair, D., Razavian, N. & Fenyö, D. Predicting endometrial cancer subtypes and molecular features from histopathology images using multi-resolution deep learning models. Cell Rep. Med. 2, 100400 (2021).
Google Scholar
Wang, X. et al. Spatial interplay patterns of cancer nuclei and tumor-infiltrating lymphocytes (TILs) predict clinical benefit for immune checkpoint inhibitors. Sci. Adv. 8, eabn3966 (2022).
Google Scholar
Farahmand, S. et al. Deep learning trained on hematoxylin and eosin tumor region of Interest predicts HER2 status and trastuzumab treatment response in HER2+ breast cancer. Mod. Pathol. 35, 44–51 (2022).
Google Scholar
Li, F. et al. Deep learning-based predictive biomarker of pathological complete response to neoadjuvant chemotherapy from histological images in breast cancer. J. Transl. Med. 19, 348 (2021).
Google Scholar
Kong, J. et al. Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients. Nat. Commun. 11, 5485 (2020).
Google Scholar
Pai, S. et al. Foundation model for cancer imaging biomarkers. Nat. Mach. Intell. 6, 354–367 (2024).
Google Scholar
Fu, Y. et al. Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis. Nat. Cancer 1, 800–810 (2020).
Google Scholar
Avram, R. et al. A digital biomarker of diabetes from smartphone-based vascular signals. Nat. Med. 26, 1576–1582 (2020).
Google Scholar
Klonoff, D. C. Diagnosing diabetes mellitus from smartphone-based vascular signals. Nat. Rev. Endocrinol. 16, 681–682 (2020).
Google Scholar
Boehm, K. M., Khosravi, P., Vanguri, R., Gao, J. & Shah, S. P. Harnessing multimodal data integration to advance precision oncology. Nat. Rev. Cancer 22, 114–126 (2022).
Google Scholar
Jaume, G., Song, A. H. & Mahmood, F. Integrating context for superior cancer prognosis. Nat. Biomed. Eng. 6, 1323–1325 (2022).
Google Scholar
Saldanha, O. L. et al. Swarm learning for decentralized artificial intelligence in cancer histopathology. Nat. Med. 28, 1232–1239 (2022).
Google Scholar
Ávila, C. G. et al. DISCOVER-EEG: an open, fully automated EEG pipeline for biomarker discovery in clinical neuroscience. Sci. Data 10, 613 (2023).
Google Scholar
AbdulJabbar, K. et al. Geospatial immune variability illuminates differential evolution of lung adenocarcinoma. Nat. Med. 26, 1054–1062 (2020).
Google Scholar
Dent, A. et al. HAVOC: small-scale histomic mapping of cancer biodiversity across large tissue distances using deep neural networks. Sci. Adv. 9, eadg1894 (2023).
Google Scholar
Zhao, S. et al. Single-cell morphological and topological atlas reveals the ecosystem diversity of human breast cancer. Nat. Commun. 14, 6796 (2023).
Google Scholar
Saillard, C. et al. Pacpaint: a histology-based deep learning model uncovers the extensive intratumor molecular heterogeneity of pancreatic adenocarcinoma. Nat. Commun. 14, 3459 (2023).
Google Scholar
Liang, J. et al. Deep learning supported discovery of biomarkers for clinical prognosis of liver cancer. Nat. Mach. Intell. 5, 408–420 (2023).
Google Scholar
Hartman, E. et al. Interpreting biologically informed neural networks for enhanced proteomic biomarker discovery and pathway analysis. Nat. Commun. 14, 5359 (2023).
Google Scholar
Bretthauer, M., Løberg, M., Holme, Ø., Adami, H.-O. & Kalager, M. Deep learning and cancer biomarkers: recognising lead-time bias. Lancet 397, 194 (2021).
Google Scholar
Specogna, A. V. & Sinicrope, F. A. Defining colon cancer biomarkers by using deep learning. Lancet 395, 314–316 (2020).
Google Scholar
Howard, F. M. et al. The impact of site-specific digital histology signatures on deep learning model accuracy and bias. Nat. Commun. 12, 4423 (2021).
Google Scholar
Kleppe, A. et al. Designing deep learning studies in cancer diagnostics. Nat. Rev. Cancer 21, 199–211 (2021).
Google Scholar
Pavlović, M. et al. Improving generalization of machine learning-identified biomarkers using causal modelling with examples from immune receptor diagnostics. Nat. Mach. Intell. 6, 15–24 (2024).
Google Scholar
Terrail, J. O. D. et al. Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer. Nat. Med. 29, 135–146 (2023).
Google Scholar
Gong, X. et al. Decoding kinase-adverse event associations for small molecule kinase inhibitors. Nat. Commun. 13, 4349 (2022).
Google Scholar
Allesøe, R. L. et al. Discovery of drug–omics associations in type 2 diabetes with generative deep-learning models. Nat. Biotechnol. 41, 399–408 (2023).
Google Scholar
ValizadehAslani, T. et al. PharmBERT: a domain-specific BERT model for drug labels. Brief. Bioinform. 24, bbad226 (2023).
Google Scholar
Qiang, B. et al. Bridging the gap between chemical reaction pretraining and conditional molecule generation with a unified model. Nat. Mach. Intell. 5, 1476–1485 (2023).
Google Scholar
Zhou, Y., Wang, F., Tang, J., Nussinov, R. & Cheng, F. Artificial intelligence in COVID-19 drug repurposing. Lancet Digit. 2, e667–e676 (2020).
Google Scholar
Aliper, A. et al. Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data. Mol. Pharm. 13, 2524–2530 (2016).
Google Scholar
Janizek, J. D. et al. Uncovering expression signatures of synergistic drug responses via ensembles of explainable machine-learning models. Nat. Biomed. Eng. 7, 811–829 (2023).
Google Scholar
Artemov, A. V. et al. Integrated deep learned transcriptomic and structure-based predictor of clinical trials outcomes. Preprint at bioRxiv (2016).
Liu, R. et al. Evaluating eligibility criteria of oncology trials using real-world data and AI. Nature 592, 629–633 (2021).
Google Scholar
Chebanov, D. K. & Misyurin, V. A. Predictive modeling of clinical trial outcomes for novel drugs using digital twin patient cohorts and generative AI. Preprint at medRxiv (2023).
Google Scholar
Zhang, K. et al. Concepts and applications of digital twins in healthcare and medicine. Patterns 5, 101028 (2024).
Google Scholar
He, J. et al. The practical implementation of artificial intelligence technologies in medicine. Nat. Med. 25, 30–36 (2019).
Google Scholar
Connor, S. et al. Adaptability of AI for safety evaluation in regulatory science: a case study of drug-induced liver injury. Front. Artif. Intell. 5, 1034631 (2022).
Google Scholar
Bender, A. & Cortes-Ciriano, I. Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 2: a discussion of chemical and biological data. Drug Discov. Today 26, 1040–1052 (2021).
Google Scholar
Schrader, M. L., Schäfer, F. R., Schäfers, F. & Glorius, F. Bridging the information gap in organic chemical reactions. Nat. Chem. 16, 491–498 (2024).
Google Scholar
Luukkonen, S., Maagdenberg, H. W. V. D., Emmerich, M. T. & Westen, G. J. V. Artificial intelligence in multi-objective drug design. Curr. Opin. Struct. Biol. 79, 102537 (2023).
Google Scholar
Fromer, J. C. & Coley, C. W. Computer-aided multi-objective optimization in small molecule discovery. Patterns 4, 100678 (2023).
Google Scholar
Coley, C. W., Eyke, N. S. & Jensen, K. F. Autonomous discovery in the chemical sciences part II: outlook. Angew. Chem. Int. Ed. 59, 23414–23436 (2020).
Google Scholar
Vanhaelen, Q., Lin, Y.-C. & Zhavoronkov, A. The advent of generative chemistry. ACS Med. Chem. Lett. 11, 1496–1505 (2020).
Google Scholar
Polykovskiy, D. et al. Molecular sets (MOSES): a benchmarking platform for molecular generation models. Front. Pharmacol. 11, 565644 (2020).
Google Scholar
Brown, N., Fiscato, M., Segler, M. H. & Vaucher, A. C. GuacaMol: benchmarking models for de novo molecular design. J. Chem. Inf. Model. 59, 1096–1108 (2019).
Google Scholar
Orlando, G. et al. PyUUL provides an interface between biological structures and deep learning algorithms. Nat. Commun. 13, 961 (2022).
Google Scholar
Wang, Y., Wang, J., Cao, Z. & Farimani, A. B. Molecular contrastive learning of representations via graph neural networks. Nat. Mach. Intell. 4, 279–287 (2022).
Google Scholar
Zang, X., Zhao, X. & Tang, B. Hierarchical molecular graph self-supervised learning for property prediction. Commun. Chem. 6, 34 (2023).
Google Scholar
Chan, L., Kumar, R., Verdonk, M. & Poelking, C. A multilevel generative framework with hierarchical self-contrasting for bias control and transparency in structure-based ligand design. Nat. Mach. Intell. 4, 1130–1142 (2022).
Google Scholar
Alon, U. & Yahav, E. On the bottleneck of graph neural networks and its practical implications. In Proc. International Conference on Learning Representations 2021 (ICLR, 2021).
Corso, G., Stark, H., Jegelka, S., Jaakkola, T. & Barzilay, R. Graph neural networks. Nat. Rev. Methods Prim. 4, 17 (2024).
Google Scholar
Gomes, B. & Ashley, E. A. Artificial intelligence in molecular medicine. N. Engl. J. Med. 388, 2456–2465 (2023).
Google Scholar
Aliper, A. et al. Prediction of clinical trials outcomes based on target choice and clinical trial design with multi‐modal artificial intelligence. Clin. Pharmacol. Ther. 114, 972–980 (2023).
Google Scholar
Miller, E. B. et al. Enabling structure-based drug discovery utilizing predicted models. Cell 187, 521–525 (2024).
Google Scholar
Liu, S. et al. Multi-modal molecule structure–text model for text-based retrieval and editing. Nat. Mach. Intell. 5, 1447–1457 (2023).
Google Scholar
Banerjee, J. et al. Machine learning in rare disease. Nat. Methods 20, 803–814 (2023).
Google Scholar
Hocky, G. M. Connecting molecular properties with plain language. Nat. Mach. Intell. 6, 249–250 (2024).
Google Scholar
Jablonka, K. M., Schwaller, P., Ortega-Guerrero, A. & Smit, B. Leveraging large language models for predictive chemistry. Nat. Mach. Intell. 6, 161–169 (2024).
Google Scholar
Dias, A. L. & Rodrigues, T. Large language models direct automated chemistry laboratory. Nature 624, 530–531 (2023).
Google Scholar
Izard, S. G., Juanes Méndez, J. A. & Palomera, P. R. Virtual reality educational tool for human anatomy. J. Med. Syst. 41, 76 (2017).
Google Scholar
Chan, H. S., Shan, H., Dahoun, T., Vogel, H. & Yuan, S. Advancing drug discovery via artificial intelligence. Trends Pharmacol. Sci. 40, 592–604 (2019).
Google Scholar
Miller, D. D. & Brown, E. W. How cognitive machines can augment medical imaging. Am. J. Roentgenol. 212, 9–14 (2019).
Google Scholar
Pasquiers, B., Benamara, S., Felices, M., Nguyen, L. & Declèves, X. Review of the existing translational pharmacokinetics modeling approaches specific to monoclonal antibodies (mAbs) to support the First-In-Human (FIH) dose selection. Int. J. Mol. Sci. 23, 12754 (2022).
Google Scholar
Liang, J., He, T., Li, H., Guo, X. & Zhang, Z. Improve individual treatment by comparing treatment benefits: cancer artificial intelligence survival analysis system for cervical carcinoma. J. Transl. Med. 20, 293 (2022).
Google Scholar
Antel, R., Abbasgholizadeh-Rahimi, S., Guadagno, E., Harley, J. M. & Poenaru, D. The use of artificial intelligence and virtual reality in doctor-patient risk communication: a scoping review. Patient Educ. Couns. 105, 3038–3050 (2022).
Google Scholar
Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024).
Google Scholar
Pettersen, E. F. et al. UCSF ChimeraX: structure visualization for researchers, educators, and developers. Protein Sci. 30, 70–82 (2021).
Google Scholar
Szklarczyk, D. et al. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res 43, D447–D452 (2015).
Google Scholar
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