SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules

A Line-Formula Chemical Notation.

Moore, A. M. (1955) · Journal of the American Chemical Society · doi:10.1021/ja01612a112 · Cited by 68 · see citing works

Proposed minimum reporting standards for chemical analysis: Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI)

Sumner, L. W., Amberg, A., Barrett, D., Beale, M. H., Beger, R., Daykin, C. A., Fan, T. W.-M., Fiehn, O., Goodacre, R., Griffin, J. L., et al. (2007) · Metabolomics · doi:10.1007/s11306-007-0082-2 · Cited by 5,368 · see citing works

PubChem Substance and Compound databases

Kim, S., Thiessen, P. A., Bolton, E. E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B. A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv951 · Cited by 5,436 · see citing works

ZINC − A Free Database of Commercially Available Compounds for Virtual Screening

Irwin, J. J., & Shoichet, B. K. (2005) · Journal of Chemical Information and Modeling · doi:10.1021/ci049714+ · Cited by 4,058 · see citing works

PubChem in 2021: new data content and improved web interfaces

Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S., Li, Q., Shoemaker, B. A., Thiessen, P. A., Yu, B., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa971 · Cited by 3,953 · see citing works

PubChem 2023 update

Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S., Li, Q., Shoemaker, B. A., Thiessen, P. A., Yu, B., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkac956 · Cited by 3,443 · see citing works

Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules

Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., & Aspuru-Guzik, A. (2018) · ACS Central Science · doi:10.1021/acscentsci.7b00572 · Cited by 3,106 · see citing works

MoleculeNet: a benchmark for molecular machine learning

Wu, Z., Ramsundar, B., Feinberg, E. N., Gomes, J., Geniesse, C., Pappu, A. S., Leswing, K., & Pande, V. (2018) · Chemical Science · doi:10.1039/c7sc02664a · Cited by 3,107 · see citing works

MassBank: a public repository for sharing mass spectral data for life sciences

Horai, H., Arita, M., Kanaya, S., Nihei, Y., Ikeda, T., Suwa, K., Ojima, Y., Tanaka, K., Tanaka, S., Aoshima, K., et al. (2010) · Journal of Mass Spectrometry · doi:10.1002/jms.1777 · Cited by 2,529 · see citing works

Quantum chemistry structures and properties of 134 kilo molecules

Ramakrishnan, R., Dral, P. O., Rupp, M., & von Lilienfeld, O. A. (2014) · Scientific Data · doi:10.1038/sdata.2014.22 · Cited by 1,966 · see citing works

Analyzing Learned Molecular Representations for Property Prediction

Yang, K., Swanson, K., Jin, W., Coley, C., Eiden, P., Gao, H., Guzman-Perez, A., Hopper, T., Kelley, B., Mathea, M., et al. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00237 · Cited by 1,923 · see citing works

Scientific discovery in the age of artificial intelligence.

Wang, H., Fu, T., Du, Y., Gao, W., Huang, K., Liu, Z., Chandak, P., Liu, S., Van Katwyk, P., Deac, A., et al. (2023) · Nature · doi:10.1038/s41586-023-06221-2 · Cited by 1,823 · see citing works

A protocol for generating a high-quality genome-scale metabolic reconstruction

Thiele, I., & Palsson, B. Ø. (2010) · Nature Protocols · doi:10.1038/nprot.2009.203 · Cited by 1,789 · see citing works

Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks

Segler, M. H. S., Kogej, T., Tyrchan, C., & Waller, M. P. (2018) · ACS Central Science · doi:10.1021/acscentsci.7b00512 · Cited by 1,537 · see citing works

Drug discovery with explainable artificial intelligence

Jiménez-Luna, J., Grisoni, F., & Schneider, G. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-00236-4 · Cited by 1,063 · see citing works

BIOPEP-UWM Database of Bioactive Peptides: Current Opportunities

Minkiewicz, P., Iwaniak, A., & Darewicz, M. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20235978 · Cited by 958 · see citing works

Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach

Ramakrishnan, R., Dral, P. O., Rupp, M., & von Lilienfeld, O. A. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00099 · Cited by 911 · see citing works

Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models

Polykovskiy, D., Zhebrak, A., Sanchez-Lengeling, B., Golovanov, S., Tatanov, O., Belyaev, S., Kurbanov, R., Artamonov, A., Aladinskiy, V., Veselov, M., et al. (2020) · Frontiers in Pharmacology · doi:10.3389/fphar.2020.565644 · Cited by 849 · see citing works

GuacaMol: Benchmarking Models for de Novo Molecular Design

Brown, N., Fiscato, M., Segler, M. H. S., & Vaucher, A. C. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00839 · Cited by 895 · see citing works

The Chemical Space Project

Reymond, J.-L. (2015) · Accounts of Chemical Research · doi:10.1021/ar500432k · Cited by 720 · see citing works

Machine Learning in Computer-Aided Synthesis Planning

Coley, C. W., Green, W. H., & Jensen, K. F. (2018) · Accounts of Chemical Research · doi:10.1021/acs.accounts.8b00087 · Cited by 704 · see citing works

Self-Driving Laboratories for Chemistry and Materials Science

Tom, G., Schmid, S. P., Baird, S. G., Cao, Y., Darvish, K., Hao, H., Lo, S., Pablo-García, S., Rajaonson, E. M., Skreta, M., et al. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00055 · Cited by 707 · see citing works

Pathway Tools version 13.0: integrated software for pathway/genome informatics and systems biology

Karp, P. D., Paley, S. M., Krummenacker, M., Latendresse, M., Dale, J. M., Lee, T. J., Kaipa, P., Gilham, F., Spaulding, A., Popescu, L., et al. (2010) · Briefings in Bioinformatics · doi:10.1093/bib/bbp043 · Cited by 694 · see citing works

Deep learning for molecular design—a review of the state of the art

Elton, D. C., Boukouvalas, Z., Fuge, M. D., & Chung, P. W. (2019) · Molecular Systems Design & Engineering · doi:10.1039/c9me00039a · Cited by 683 · see citing works

A graph-convolutional neural network model for the prediction of chemical reactivity

Coley, C. W., Jin, W., Rogers, L., Jamison, T. F., Jaakkola, T. S., Green, W. H., Barzilay, R., & Jensen, K. F. (2019) · Chemical Science · doi:10.1039/c8sc04228d · Cited by 679 · see citing works

The Pathway Tools software

Karp, P. D., Paley, S., & Romero, P. (2002) · Bioinformatics · doi:10.1093/bioinformatics/18.suppl_1.s225 · Cited by 661 · see citing works

Computer‐Assisted Synthetic Planning: The End of the Beginning

Szymkuć, S., Gajewska, E. P., Klucznik, T., Molga, K., Dittwald, P., Startek, M., Bajczyk, M., & Grzybowski, B. A. (2016) · Angewandte Chemie International Edition · doi:10.1002/anie.201506101 · Cited by 611 · see citing works

A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology

Herrgård, M. J., Swainston, N., Dobson, P., Dunn, W. B., Arga, K. Y., Arvas, M., Blüthgen, N., Borger, S., Costenoble, R., Heinemann, M., et al. (2008) · Nature Biotechnology · doi:10.1038/nbt1492 · Cited by 588 · see citing works

A Critical Review of Machine Learning of Energy Materials

Chen, C., Zuo, Y., Ye, W., Li, X., Deng, Z., & Ong, S. P. (2020) · Advanced Energy Materials · doi:10.1002/aenm.201903242 · Cited by 609 · see citing works

Large-scale chemical language representations capture molecular structure and properties

Ross, J., Belgodere, B., Chenthamarakshan, V., Padhi, I., Mroueh, Y., & Das, P. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00580-7 · Cited by 590 · see citing works

Large-scale comparison of machine learning methods for drug target prediction on ChEMBL

Mayr, A., Klambauer, G., Unterthiner, T., Steijaert, M., Wegner, J. K., Ceulemans, H., Clevert, D.-A., & Hochreiter, S. (2018) · Chemical Science · doi:10.1039/c8sc00148k · Cited by 584 · see citing works

Molecular representations in AI-driven drug discovery: a review and practical guide

David, L., Thakkar, A., Mercado, R., & Engkvist, O. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-00460-5 · Cited by 587 · see citing works

Deep Learning in Chemistry

Mater, A. C., & Coote, M. L. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00266 · Cited by 565 · see citing works

Iron behaving badly: inappropriate iron chelation as a major contributor to the aetiology of vascular and other progressive inflammatory and degenerative diseases

Kell, D. B. (2009) · BMC Medical Genomics · doi:10.1186/1755-8794-2-2 · Cited by 554 · see citing works

Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models

Liu, B., Ramsundar, B., Kawthekar, P., Shi, J., Gomes, J., Luu Nguyen, Q., Ho, S., Sloane, J., Wender, P., & Pande, V. (2017) · ACS Central Science · doi:10.1021/acscentsci.7b00303 · Cited by 536 · see citing works

REINVENT 2.0: An AI Tool for De Novo Drug Design

Blaschke, T., Arús-Pous, J., Chen, H., Margreitter, C., Tyrchan, C., Engkvist, O., Papadopoulos, K., & Patronov, A. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00915 · Cited by 496 · see citing works

Perfluoroalkyl sulfonic and carboxylic acids: A critical review of physicochemical properties, levels and patterns in waters and wastewaters, and treatment methods

Rayne, S., & Forest, K. (2009) · Journal of Environmental Science and Health, Part A · doi:10.1080/10934520903139811 · Cited by 475 · see citing works

Convolutional embedding of attributed molecular graphs for physical property prediction

Coley, C. W., Barzilay, R., Green, W. H., Jaakkola, T. S., & Jensen, K. F. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00601 · Cited by 470 · see citing works

Leveraging large language models for predictive chemistry

Jablonka, K. M., Schwaller, P., Ortega-Guerrero, A., & Smit, B. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-023-00788-1 · Cited by 378 · see citing works

Graph Convolutional Neural Networks for Predicting Drug-Target Interactions

Torng, W., & Altman, R. B. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00628 · Cited by 371 · see citing works

Machine learning–assisted molecular design and efficiency prediction for high-performance organic photovoltaic materials

Sun, W., Zheng, Y., Yang, K., Zhang, Q., Shah, A. A., Wu, Z., Sun, Y., Feng, L., Chen, D., Xiao, Z., et al. (2019) · Science Advances · doi:10.1126/sciadv.aay4275 · Cited by 344 · see citing works

Towards a unifying, systems biology understanding of large-scale cellular death and destruction caused by poorly liganded iron: Parkinson’s, Huntington’s, Alzheimer’s, prions, bactericides, chemical toxicology and others as examples

Kell, D. B. (2010) · Archives of Toxicology · doi:10.1007/s00204-010-0577-x · Cited by 361 · see citing works

Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning

Nandy, A., Duan, C., Taylor, M. G., Liu, F., Steeves, A. H., & Kulik, H. J. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00347 · Cited by 358 · see citing works

Open PHACTS: semantic interoperability for drug discovery

Williams, A. J., Harland, L., Groth, P., Pettifer, S., Chichester, C., Willighagen, E. L., Evelo, C. T., Blomberg, N., Ecker, G., Goble, C., et al. (2012) · Drug Discovery Today · doi:10.1016/j.drudis.2012.05.016 · Cited by 321 · see citing works

Data-driven materials research enabled by natural language processing and information extraction

Olivetti, E. A., Cole, J. M., Kim, E., Kononova, O., Ceder, G., Han, T. Y.-J., & Hiszpanski, A. M. (2020) · Applied Physics Reviews · doi:10.1063/5.0021106 · Cited by 302 · see citing works

Automated Discovery of Reaction Pathways, Rate Constants, and Transition States Using Reactive Molecular Dynamics Simulations

Döntgen, M., Przybylski-Freund, M.-D., Kröger, L. C., Kopp, W. A., Ismail, A. E., & Leonhard, K. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00201 · Cited by 308 · see citing works

Information retrieval and text mining technologies for chemistry

Krallinger, M., Rabal, O., Lourenço, A., Oyarzabal, J., & Valencia, A. (2017) · Chemical Reviews · doi:10.1021/acs.chemrev.6b00851 · Cited by 281 · see citing works

Prediction of chemical reaction yields using deep learning

Schwaller, P., Vaucher, A. C., Laino, T., & Reymond, J.-L. (2021) · Machine Learning: Science and Technology · doi:10.1088/2632-2153/abc81d · Cited by 291 · see citing works

Matched Molecular Pairs as a Medicinal Chemistry Tool: Miniperspective

Griffen, E., Leach, A. G., Robb, G. R., & Warner, D. J. (2011) · Journal of Medicinal Chemistry · doi:10.1021/jm200452d · Cited by 282 · see citing works

Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery

Polykovskiy, D., Zhebrak, A., Vetrov, D., Ivanenkov, Y., Aladinskiy, V., Mamoshina, P., Bozdaganyan, M., Aliper, A., Zhavoronkov, A., & Kadurin, A. (2018) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.8b00839 · Cited by 278 · see citing works

Is Multitask Deep Learning Practical for Pharma?

Ramsundar, B., Liu, B., Wu, Z., Verras, A., Tudor, M., Sheridan, R. P., & Pande, V. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00146 · Cited by 263 · see citing works

Mol-CycleGAN: a generative model for molecular optimization

Maziarka, Ł., Pocha, A., Kaczmarczyk, J., Rataj, K., Danel, T., & Warchoł, M. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-019-0404-1 · Cited by 275 · see citing works

Assessing the impact of generative AI on medicinal chemistry

Walters, W. P., & Murcko, M. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0418-2 · Cited by 262 · see citing works

A review of large language models and autonomous agents in chemistry

Ramos, M. C., Collison, C. J., & White, A. D. (2025) · Chemical Science · doi:10.1039/d4sc03921a · Cited by 274 · see citing works

Discovering highly potent antimicrobial peptides with deep generative model HydrAMP

Szymczak, P., Możejko, M., Grzegorzek, T., Jurczak, R., Bauer, M., Neubauer, D., Sikora, K., Michalski, M., Sroka, J., Setny, P., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36994-z · Cited by 231 · see citing works

Artificial Intelligence for Drug Discovery: Are We There Yet?

Hasselgren, C., & Oprea, T. I. (2024) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-040323-040828 · Cited by 236 · see citing works

14 examples of how LLMs can transform materials science and chemistry: A reflection on a large language model hackathon

Jablonka, K. M., Ai, Q., Al-Feghali, A., Badhwar, S., Bocarsly, J. D., Bran, A. M., Bringuier, S., Brinson, L. C., Choudhary, K., Circi, D., et al. (2023) · Digital Discovery · doi:10.1039/d3dd00113j · Cited by 218 · see citing works

Generative molecular design in low data regimes

Moret, M., Friedrich, L., Grisoni, F., Merk, D., & Schneider, G. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0160-y · Cited by 219 · see citing works

MSNovelist: de novo structure generation from mass spectra

Stravs, M. A., Dührkop, K., Böcker, S., & Zamboni, N. (2022) · Nature Methods · doi:10.1038/s41592-022-01486-3 · Cited by 226 · see citing works

A general model to predict small molecule substrates of enzymes based on machine and deep learning

Kroll, A., Ranjan, S., Engqvist, M. K. M., & Lercher, M. J. (2023) · Nature Communications · doi:10.1038/s41467-023-38347-2 · Cited by 193 · see citing works

Combining generative artificial intelligence and on-chip synthesis for de novo drug design

Grisoni, F., Huisman, B. J. H., Button, A. L., Moret, M., Atz, K., Merk, D., & Schneider, G. (2021) · Science Advances · doi:10.1126/sciadv.abg3338 · Cited by 190 · see citing works

AI on drugs: can artificial intelligence accelerate drug development? Evidence from a large-scale examination of bio-Pharma firms

Lou, B., & Wu, L. (2021) · MIS Quarterly · doi:10.25300/misq/2021/16565 · Cited by 177 · see citing works

ECMDB: the E-coli Metabolome Database

Guo, A. C., Jewison, T., Wilson, M., Liu, Y., Knox, C., Djoumbou, Y., Lo, P., Mandal, R., Krishnamurthy, R., & Wishart, D. S. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks992 · Cited by 164 · see citing works

Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects

Liu, Y., Tan, X., Liang, J., Han, H., Xiang, P., & Yan, W. (2023) · Advanced Functional Materials · doi:10.1002/adfm.202214271 · Cited by 174 · see citing works

Leveraging Uncertainty in Machine Learning Accelerates Biological Discovery and Design

Hie, B., Bryson, B. D., & Berger, B. (2020) · Cell Systems · doi:10.1016/j.cels.2020.09.007 · Cited by 154 · see citing works

Do Large Language Models Understand Chemistry? A Conversation with ChatGPT

Castro Nascimento, C. M., & Pimentel, A. S. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00285 · Cited by 147 · see citing works

Model agnostic generation of counterfactual explanations for molecules

Wellawatte, G. P., Seshadri, A., & White, A. D. (2022) · Chemical Science · doi:10.1039/d1sc05259d · Cited by 143 · see citing works

Computer systems for the prediction of xenobiotic metabolism

Langowski, J., & Long, A. (2002) · Advanced Drug Delivery Reviews · doi:10.1016/s0169-409x(02)00011-x · Cited by 138 · see citing works

tmQM Dataset─Quantum Geometries and Properties of 86k Transition Metal Complexes

Balcells, D., & Skjelstad, B. B. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01041 · Cited by 143 · see citing works

Large language models for reticular chemistry

Zheng, Z., Rampal, N., Inizan, T. J., Borgs, C., Chayes, J. T., & Yaghi, O. M. (2025) · Nature Reviews Materials · doi:10.1038/s41578-025-00772-8 · Cited by 134 · see citing works

Deep learning allows genome-scale prediction of Michaelis constants from structural features

Kroll, A., Engqvist, M. K. M., Heckmann, D., & Lercher, M. J. (2021) · PLOS Biology · doi:10.1371/journal.pbio.3001402 · Cited by 132 · see citing works

Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry

Janet, J. P., Liu, F., Nandy, A., Duan, C., Yang, T., Lin, S., & Kulik, H. J. (2019) · Inorganic Chemistry · doi:10.1021/acs.inorgchem.9b00109 · Cited by 114 · see citing works

DrugCentral 2023 extends human clinical data and integrates veterinary drugs

Avram, S., Wilson, T. B., Curpan, R., Halip, L., Borota, A., Bora, A., Bologa, C. G., Holmes, J., Knockel, J., Yang, J. J., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkac1085 · Cited by 101 · see citing works

Link-INVENT: Generative Linker Design with Reinforcement Learning

Guo, J., Knuth, F., Margreitter, C., Janet, J. P., Papadopoulos, K., Engkvist, O., & Patronov, A. (2022) · doi:10.26434/chemrxiv-2022-qkx9f · Cited by 101 · see citing works

An automated PLS search for biologically relevant QSAR descriptors

Olah, M., Bologa, C., & Oprea, T. I. (2004) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-004-4060-8 · Cited by 91 · see citing works

Annotating metabolite mass spectra with domain-inspired chemical formula transformers

Goldman, S., Wohlwend, J., Stražar, M., Haroush, G., Xavier, R. J., & Coley, C. W. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00708-3 · Cited by 87 · see citing works

Uni-Mol: A Universal 3D Molecular Representation Learning Framework

Zhou, G., Gao, Z., Ding, Q., Zheng, H., Xu, H., Wei, Z., Zhang, L., & Ke, G. (2022) · doi:10.26434/chemrxiv-2022-jjm0j · Cited by 81 · see citing works

Generative Retrieval-Augmented Ontologic Graph and Multiagent Strategies for Interpretive Large Language Model-Based Materials Design

Buehler, M. J. (2024) · ACS Engineering Au · doi:10.1021/acsengineeringau.3c00058 · Cited by 82 · see citing works

Genome scale models of yeast: towards standardized evaluation and consistent omic integration

Sánchez, B. J., & Nielsen, J. (2015) · Integrative Biology · doi:10.1039/c5ib00083a · Cited by 75 · see citing works

Comparison of structure- and ligand-based scoring functions for deep generative models: a GPCR case study

Thomas, M., Smith, R. T., O’Boyle, N. M., de Graaf, C., & Bender, A. (2021) · Journal of Cheminformatics · doi:10.1186/s13321-021-00516-0 · Cited by 78 · see citing works

Data-Driven Advancement of Homogeneous Nickel Catalyst Activity for Aryl Ether Cleavage

Cordova, M., Wodrich, M. D., Meyer, B., Sawatlon, B., & Corminboeuf, C. (2020) · ACS Catalysis · doi:10.1021/acscatal.0c00774 · Cited by 68 · see citing works

LSA-DDI: learning stereochemistry-aware drug interactions via 3D feature fusion and contrastive cross-attention

Wang, S., Yang, C., & Chen, L. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26146799 · Cited by 65 · see citing works

Uncertainty of Prebiotic Scenarios: The Case of the Non-Enzymatic Reverse Tricarboxylic Acid Cycle

Zubarev, D. Y., Rappoport, D., & Aspuru-Guzik, A. (2015) · Scientific Reports · doi:10.1038/srep08009 · Cited by 63 · see citing works

Machine learning in materials design: Algorithm and application*

Song, Z., Chen, X., Meng, F., Cheng, G., Wang, C., Sun, Z., & Yin, W.-J. (2020) · Chinese Physics B · doi:10.1088/1674-1056/abc0e3 · Cited by 63 · see citing works

Equivariant Graph Neural Networks for Toxicity Prediction

Cremer, J., Medrano Sandonas, L., Tkatchenko, A., Clevert, D.-A., & De Fabritiis, G. (2023) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.3c00032 · Cited by 62 · see citing works

Deep learning for low-data drug discovery: Hurdles and opportunities

van Tilborg, D., Brinkmann, H., Criscuolo, E., Rossen, L., Özçelik, R., & Grisoni, F. (2024) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102818 · Cited by 61 · see citing works

Leveraging Cheminformatics Strategies for Inorganic Discovery: Application to Redox Potential Design

Janet, J. P., Gani, T. Z. H., Steeves, A. H., Ioannidis, E. I., & Kulik, H. J. (2017) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.7b00808 · Cited by 56 · see citing works

Data-Driven Analysis of Hole-Transporting Materials for Perovskite Solar Cells Performance

del Cueto, M., Rawski-Furman, C., Aragó, J., Ortí, E., & Troisi, A. (2022) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.2c04725 · Cited by 38 · see citing works

Application of Quantitative Structure–Activity Relationship Models of 5-HT_1A Receptor Binding to Virtual Screening Identifies Novel and Potent 5-HT_1A Ligands

Luo, M., Wang, X. S., Roth, B. L., Golbraikh, A., & Tropsha, A. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci400460q · Cited by 37 · see citing works

GraphCPI: Graph Neural Representation Learning for Compound-Protein Interaction

Quan, Z., Guo, Y., Lin, X., Wang, Z.-J., & Zeng, X. (2019) · 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm47256.2019.8983267 · Cited by 35 · see citing works

PerturbNet predicts single-cell responses to unseen chemical and genetic perturbations

Yu, H., Qian, W., Song, Y., & Welch, J. D. (2025) · Molecular Systems Biology · doi:10.1038/s44320-025-00131-3 · Cited by 57 · see citing works

Sc2Mol: a scaffold-based two-step molecule generator with variational autoencoder and transformer

Liao, Z., Xie, L., Mamitsuka, H., & Zhu, S. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btac814 · Cited by 24 · see citing works

Chemical design with GPU-based Ising machines

Mao, Z., Matsuda, Y., Tamura, R., & Tsuda, K. (2023) · Digital Discovery · doi:10.1039/d3dd00047h · Cited by 23 · see citing works

In silico comparison of marine, terrestrial and synthetic compounds using ChemGPS-NP for navigating chemical space

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Unleashing the potential of cell painting assays for compound activities and hazards prediction

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Susquehanna Chorale Spring Concert "Roots and Wings"

Hawkins, D. (2017)

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Literature

Barker, J. M., Harjo, S. S., Zydek, F., et al. (1998) · American Indian Culture and Research Journal · doi:10.17953/aicr.22.2.2163426032357v48 · Cited by 1 · see citing works

Honoring Native American Code Talkers

Meadows, W. C. (2011) · American Indian Culture and Research Journal · doi:10.17953/aicr.35.3.r067rg432000t816 · Cited by 5 · see citing works

Citations Gone #Social: Examining the Effect of Altmetrics on Citations and Readership in Communication Research

Wasike, B. (2019) · Social Science Computer Review · doi:10.1177/0894439319873563 · Cited by 26 · see citing works