An in-depth review of AI-powered advancements in cancer drug discovery

Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries

Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., & Bray, F. (2021) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21660 · Cited by 108,699 · see citing works

Highly accurate protein structure prediction with AlphaFold

Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works

Accurate structure prediction of biomolecular interactions with AlphaFold 3

Abramson, J., Adler, J., Dunger, J., Evans, R., Green, T., Pritzel, A., Ronneberger, O., Willmore, L., Ballard, A. J., Bambrick, J., et al. (2024) · Nature · doi:10.1038/s41586-024-07487-w · Cited by 15,861 · see citing works

ColabFold - Making protein folding accessible to all

Mirdita, M., Schütze, K., Moriwaki, Y., Heo, L., Ovchinnikov, S., & Steinegger, M. (2021) · doi:10.1101/2021.08.15.456425 · Cited by 9,391 · see citing works

CHARMM: The biomolecular simulation program

Brooks, B. R., Brooks, C. L., MacKerell, A. D., Nilsson, L., Petrella, R. J., Roux, B., Won, Y., Archontis, G., Bartels, C., Boresch, S., et al. (2009) · Journal of Computational Chemistry · doi:10.1002/jcc.21287 · Cited by 8,994 · see citing works

AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models

Varadi, M., Anyango, S., Deshpande, M., Nair, S., Natassia, C., Yordanova, G., Yuan, D., Stroe, O., Wood, G., Laydon, A., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab1061 · Cited by 7,850 · see citing works

Sipuleucel-T Immunotherapy for Castration-Resistant Prostate Cancer

Kantoff, P. W., Higano, C. S., Shore, N. D., Berger, E. R., Small, E. J., Penson, D. F., Redfern, C. H., Ferrari, A. C., Dreicer, R., Sims, R. B., et al. (2010) · New England Journal of Medicine · doi:10.1056/nejmoa1001294 · Cited by 5,572 · see citing works

Cp2k: An electronic structure and molecular dynamics software package-quickstep: Efficient and accurate electronic structure calculations

Kühne, T. D., Iannuzzi, M., Del Ben, M., Rybkin, V. V., Seewald, P., Stein, F., Laino, T., Khaliullin, R. Z., Schütt, O., Schiffmann, F., et al. (2020) · The Journal of Chemical Physics · doi:10.1063/5.0007045 · Cited by 4,468 · see citing works

A guide to cancer immunotherapy: from T cell basic science to clinical practice

Waldman, A. D., Fritz, J. M., & Lenardo, M. J. (2020) · Nature Reviews Immunology · doi:10.1038/s41577-020-0306-5 · Cited by 4,154 · see citing works

Improved protein structure prediction using potentials from deep learning

Senior, A. W., Evans, R., Jumper, J., Kirkpatrick, J., Sifre, L., Green, T., Qin, C., Žídek, A., Nelson, A. W. R., Bridgland, A., et al. (2020) · Nature · doi:10.1038/s41586-019-1923-7 · Cited by 3,378 · see citing works

Applications of machine learning in drug discovery and development

Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., Li, B., Madabhushi, A., Shah, P., Spitzer, M., et al. (2019) · Nature Reviews Drug Discovery · doi:10.1038/s41573-019-0024-5 · Cited by 3,228 · see citing works

Scalable molecular dynamics on CPU and GPU architectures with NAMD

Phillips, J. C., Hardy, D. J., Maia, J. D. C., Stone, J. E., Ribeiro, J. V., Bernardi, R. C., Buch, R., Fiorin, G., Hénin, J., Jiang, W., et al. (2020) · The Journal of Chemical Physics · doi:10.1063/5.0014475 · Cited by 3,123 · see citing works

An immunogenic personal neoantigen vaccine for patients with melanoma

Ott, P. A., Hu, Z., Keskin, D. B., Shukla, S. A., Sun, J., Bozym, D. J., Zhang, W., Luoma, A., Giobbie-Hurder, A., Peter, L., et al. (2017) · Nature · doi:10.1038/nature22991 · Cited by 2,936 · see citing works

OpenMM 7: Rapid development of high performance algorithms for molecular dynamics

Eastman, P., Swails, J., Chodera, J. D., McGibbon, R. T., Zhao, Y., Beauchamp, K. A., Wang, L.-P., Simmonett, A. C., Harrigan, M. P., Stern, C. D., et al. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005659 · Cited by 2,748 · see citing works

Molecular Docking and Structure-Based Drug Design Strategies

Ferreira, L. L. G., dos Santos, R. N., Oliva, G., & Andricopulo, A. D. (2015) · Molecules · doi:10.3390/molecules200713384 · Cited by 2,645 · see citing works

Convolutional Networks on Graphs for Learning Molecular Fingerprints

Duvenaud, D., Maclaurin, D., Aguilera-Iparraguirre, J., Gómez-Bombarelli, R., Hirzel, T., Aspuru-Guzik, A., & Adams, R. P. (2015) · arXiv · doi:10.48550/arxiv.1509.09292 · Cited by 2,628 · see citing works

Drug resistance in cancer: An overview

Housman, G., Byler, S., Heerboth, S., Lapinska, K., Longacre, M., Snyder, N., & Sarkar, S. (2014) · Cancers · doi:10.3390/cancers6031769 · Cited by 2,463 · see citing works

Scalable Algorithms for Molecular Dynamics Simulations on Commodity Clusters

Bowers, K. J., Chow, D. E., Xu, H., Dror, R. O., Eastwood, M. P., Gregersen, B. A., Klepeis, J. L., Kolossváry, I., Moraes, M. A., Sacerdoti, F. D., et al. (2006) · ACM/IEEE Conference on Supercomputing (SC) · doi:10.1109/sc.2006.54 · Cited by 2,164 · see citing works

Overriding Imatinib Resistance with a Novel ABL Kinase Inhibitor

Shah, N. P., Tran, C., Lee, F. Y., Chen, P., Norris, D. J., & Sawyers, C. L. (2004) · Science · doi:10.1126/science.1099480 · Cited by 1,785 · see citing works

Junction Tree Variational Autoencoder for Molecular Graph GenerationPreprint

Jin, W.-G., Barzilay, R., & Jaakkola, T. (2018) · International Conference on Machine Learning · doi:10.48550/arxiv.1802.04364 · Cited by 1,783 · see citing works

The rise of deep learning in drug discovery

Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., & Blaschke, T. (2018) · Drug Discovery Today · doi:10.1016/j.drudis.2018.01.039 · Cited by 1,770 · see citing works

Small molecules in targeted cancer therapy: advances, challenges, and future perspectives

Zhong, L., Li, Y., Xiong, L., Wang, W., Wu, M., Yuan, T., Yang, W. M., Tian, C., Miao, Z., Wang, T., et al. (2021) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-021-00572-w · Cited by 1,735 · see citing works

Large-scale integration of the plasma proteome with genetics and disease

Ferkingstad, E., Sulem, P., Atlason, B. A., Sveinbjornsson, G., Magnusson, M. I., Styrmisdottir, E. L., Gunnarsdottir, K., Helgason, A., Oddsson, A., Halldorsson, B. V., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00978-w · Cited by 1,697 · see citing works

Mining electronic health records: towards better research applications and clinical care

Jensen, P. B., Jensen, L. J., & Brunak, S. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3208 · Cited by 1,680 · see citing works

The support of human genetic evidence for approved drug indications

Nelson, M. R., Tipney, H., Painter, J. L., Shen, J., Nicoletti, P., Shen, Y., Floratos, A., Sham, P. C., Li, M. J., Wang, J., et al. (2015) · Nature Genetics · doi:10.1038/ng.3314 · Cited by 1,666 · see citing works

Computer Simulation of Molecular Dynamics: Methodology, Applications, and Perspectives in Chemistry

van Gunsteren, W. F., & Berendsen, H. J. C. (1990) · Angewandte Chemie International Edition in English · doi:10.1002/anie.199009921 · Cited by 1,560 · see citing works

Molecular dynamics simulations and drug discovery

Durrant, J. D., & McCammon, J. A. (2011) · BMC Biology · doi:10.1186/1741-7007-9-71 · Cited by 1,412 · see citing works

PD 0332991, a selective cyclin D kinase 4/6 inhibitor, preferentially inhibits proliferation of luminal estrogen receptor-positive human breast cancer cell lines in vitro

Finn, R. S., Dering, J., Conklin, D., Kalous, O., Cohen, D. J., Desai, A. J., Ginther, C., Atefi, M., Chen, I., Fowst, C., et al. (2009) · Breast Cancer Research · doi:10.1186/bcr2419 · Cited by 1,398 · see citing works

Computational approaches streamlining drug discovery

Sadybekov, A., & Katritch, V. (2023) · Nature · doi:10.1038/s41586-023-05905-z · Cited by 1,342 · see citing works

PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene–disease associations

Denny, J. C., Ritchie, M. D., Basford, M. A., Pulley, J. M., Bastarache, L., Brown-Gentry, K., Wang, D., Masys, D. R., Roden, D. M., & Crawford, D. C. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq126 · Cited by 1,219 · see citing works

The interleukin-6 receptor as a target for prevention of coronary heart disease: a mendelian randomisation analysis

(2012) · The Lancet · doi:10.1016/s0140-6736(12)60110-x · Cited by 1,146 · see citing works

Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers

George, H., Duke Jon, D., Shah Nigam, H., Reich Christian, G., Vojtech, H., Schuemie Martijn, J., Suchard Marc, A., Woong, P. R., Kei, W. I. C., Rijnbeek Peter, R., et al. (2015) · Studies in Health Technology and Informatics · doi:10.3233/978-1-61499-564-7-574 · Cited by 1,107 · see citing works

DeepTox: toxicity prediction using deep learning

Mayr, A., Klambauer, G., Unterthiner, T., & Hochreiter, S. (2016) · Frontiers in Environmental Science · doi:10.3389/fenvs.2015.00080 · Cited by 1,081 · see citing works

Autonomous chemical research with large language models

Boiko, D. A., MacKnight, R., Kline, B., & Gomes, G. (2023) · Nature · doi:10.1038/s41586-023-06792-0 · Cited by 1,050 · see citing works

Improved prediction of protein-protein interactions using AlphaFold2

Bryant, P., Pozzati, G., & Elofsson, A. (2021) · doi:10.1101/2021.09.15.460468 · Cited by 1,025 · see citing works

Development of a Large-Scale De-Identified DNA Biobank to Enable Personalized Medicine

Roden, D. M., Pulley, J. M., Basford, M., Bernard, G. R., Clayton, E. W., Balser, J., & Masys, D. R. (2008) · Clinical Pharmacology & Therapeutics · doi:10.1038/clpt.2008.89 · Cited by 959 · see citing works

mRNA vaccine for cancer immunotherapy

Miao, L., Zhang, Y., & Huang, L. (2021) · Molecular Cancer · doi:10.1186/s12943-021-01335-5 · Cited by 947 · see citing works

PREDICT: a method for inferring novel drug indications with application to personalized medicine

Gottlieb, A., Stein, G. Y., Ruppin, E., & Sharan, R. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.26 · Cited by 892 · see citing works

Dose Escalation Methods in Phase I Cancer Clinical Trials

Le Tourneau, C., Lee, J. J., & Siu, L. L. (2009) · JNCI: Journal of the National Cancer Institute · doi:10.1093/jnci/djp079 · Cited by 867 · see citing works

Cancer vaccines: the next immunotherapy frontier

Lin, M. J., Svensson‐Arvelund, J., Lubitz, G. S., Marabelle, A., Melero, I., Brown, B. D., & Brody, J. (2022) · Nature Cancer · doi:10.1038/s43018-022-00418-6 · Cited by 861 · see citing works

RNA-Targeted Therapeutics

Crooke, S. T., Witztum, J. L., Bennett, C. F., & Baker, B. F. (2018) · Cell Metabolism · doi:10.1016/j.cmet.2018.03.004 · Cited by 841 · see citing works

Introduction to artificial intelligence in medicine

Mintz, Y., & Brodie, R. (2019) · Minimally Invasive Therapy & Allied Technologies · doi:10.1080/13645706.2019.1575882 · Cited by 818 · see citing works

Doxorubicin-Tethered Responsive Gold Nanoparticles Facilitate Intracellular Drug Delivery for Overcoming Multidrug Resistance in Cancer Cells

Wang, F., Wang, Y.-C., Dou, S., Xiong, M.-H., Sun, T.-M., & Wang, J. (2011) · ACS Nano · doi:10.1021/nn200007z · Cited by 813 · see citing works

ICP34.5 deleted herpes simplex virus with enhanced oncolytic, immune stimulating, and anti-tumour properties

Liu, B., Robinson, M., Han, Z.-Q., Branston, R. H., English, C., Reay, P. A., McGrath, Y., Thomas, S., Thornton, M., Bullock, P., et al. (2003) · Gene Therapy · doi:10.1038/sj.gt.3301885 · Cited by 804 · see citing works

Low Data Drug Discovery with One-Shot Learning

Altae-Tran, H., Ramsundar, B., Pappu, A. S., & Pande, V. (2017) · ACS Central Science · doi:10.1021/acscentsci.6b00367 · Cited by 799 · see citing works

Artificial Intelligence in Cancer Research and Precision Medicine

Bhinder, B., Gilvary, C., Madhukar, N. S., & Elemento, O. (2021) · Cancer Discovery · doi:10.1158/2159-8290.cd-21-0090 · Cited by 781 · see citing works

PROVENGE (Sipuleucel-T) in Prostate Cancer: The First FDA-Approved Therapeutic Cancer Vaccine

Cheever, M. A., & Higano, C. S. (2011) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-10-3126 · Cited by 780 · see citing works

HOOMD-blue: A Python package for high-performance molecular dynamics and hard particle Monte Carlo simulations

Anderson, J. A., Gläser, J., & Glotzer, S. C. (2019) · Computational Materials Science · doi:10.1016/j.commatsci.2019.109363 · Cited by 724 · see citing works

Genomic and drug target evaluation of 90 cardiovascular proteins in 30,931 individuals

Folkersen, L., Gustafsson, S., Wang, Q., Hansen, D. H., Hedman, Å. K., Schork, A., Page, K., Zhernakova, D. V., Wu, Y., Peters, J., et al. (2020) · Nature Metabolism · doi:10.1038/s42255-020-00287-2 · Cited by 705 · see citing works

Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development

Salo‐Ahen, O. M. H., Alanko, I., Bhadane, R. N., Bonvin, A. M. J. J., Honorato, R. V., Hossain, S., Juffer, A. H., Kabedev, A., Lahtela-Kakkonen, M. K., Larsen, A. S., et al. (2020) · Processes · doi:10.3390/pr9010071 · Cited by 693 · see citing works

TTD: Therapeutic Target Database describing target druggability information

Zhou, Y., Zhang, Y., Zhao, D., Yu, X., Shen, X., Zhou, Y., Wang, S., Qiu, Y., Chen, Y., & Zhu, F. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad751 · Cited by 667 · see citing works

Cardiac metastases

Bussani, R., De‐Giorgio, F., Abbate, A., & Silvestri, F. (2006) · Journal of Clinical Pathology · doi:10.1136/jcp.2005.035105 · Cited by 657 · see citing works

Are drug targets with genetic support twice as likely to be approved? Revised estimates of the impact of genetic support for drug mechanisms on the probability of drug approval

King, E. A., Davis, J. W., & Degner, J. F. (2019) · PLOS Genetics · doi:10.1371/journal.pgen.1008489 · Cited by 649 · see citing works

Artificial intelligence in COVID-19 drug repurposing

Zhou, Y., Wang, F., Tang, J., Nussinov, R., & Cheng, F. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30192-8 · Cited by 636 · see citing works

Proceedings of the 2007 ACM/IEEE conference on Supercomputing

Verastegui, B. (2007) · doi:10.1145/1362622 · Cited by 619 · see citing works

Mechanisms of Autoinhibition and STI-571/Imatinib Resistance Revealed by Mutagenesis of BCR-ABL

Azam, M., Latek, R. R., & Daley, G. Q. (2003) · Cell · doi:10.1016/s0092-8674(03)00190-9 · Cited by 617 · see citing works

Multidrug Resistance in Cancer: Understanding Molecular Mechanisms, Immunoprevention and Therapeutic Approaches

Emran, T. B., Shahriar, A., Mahmud, A. R., Rahman, T., Abir, M. H., Siddiquee, M. F. R., Ahmed, H., Rahman, N., Nainu, F., Wahyudin, E., et al. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.891652 · Cited by 606 · see citing works

Therapeutic cancer vaccines: Advancements, challenges, and prospects

Fan, T., Zhang, M., Yang, J., Zhu, Z., Cao, W., & Dong, C. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01674-3 · Cited by 597 · see citing works

Protein structure-based drug design: from docking to molecular dynamics.

Śledź, P., & Caflisch, A. (2018) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2017.10.010 · Cited by 534 · see citing works

Mapping ICD-10 and ICD-10-CM Codes to Phecodes: Workflow Development and Initial Evaluation

Wu, P., Gifford, A., Meng, X., Li, X., Campbell, H. J., Varley, T., Zhao, J., Carroll, R. J., Bastarache, L. A., Denny, J. C., et al. (2019) · JMIR Medical Informatics · doi:10.2196/14325 · Cited by 516 · 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

PotentialNet for Molecular Property Prediction

Feinberg, E. N., Sur, D., Wu, Z., Husic, B. E., Mai, H., Li, Y., Sun, S., Yang, J., Ramsundar, B., & Pande, V. S. (2018) · ACS Central Science · doi:10.1021/acscentsci.8b00507 · Cited by 485 · see citing works

Genetics meets proteomics: perspectives for large population-based studies

Suhre, K., McCarthy, M. I., & Schwenk, J. M. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-020-0268-2 · Cited by 478 · see citing works

Artificial intelligence in cancer target identification and drug discovery

You, Y., Lai, X., Pan, Y. F., Zheng, H., Vera, J., Liu, S., Deng, S., & Zhang, L. (2022) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-022-00994-0 · Cited by 476 · 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

An overview of targeted cancer therapy

Padma, V. V. (2015) · Biomedicine · doi:10.7603/s40681-015-0019-4 · Cited by 464 · see citing works

Artificial intelligence in drug discovery: recent advances and future perspectives

Jiménez-Luna, J., Grisoni, F., Weskamp, N., & Schneider, G. (2021) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2021.1909567 · Cited by 464 · see citing works

Cohort Profile: Estonian Biobank of the Estonian Genome Center, University of Tartu

Leitsalu, L., Haller, T., Esko, T., Tammesoo, M., Alavere, H., Snieder, H., Perola, M., Ng, P. C., Mägi, R., Milani, L. A., et al. (2014) · International Journal of Epidemiology · doi:10.1093/ije/dyt268 · Cited by 437 · see citing works

Dr William Coley and tumour regression: a place in history or in the future

Cann, S. A. H., van Netten, J. P., & van Netten, C. (2003) · Postgraduate Medical Journal · doi:10.1093/postgradmedj/79.938.672 · Cited by 435 · see citing works

Artificial intelligence for good health: a scoping review of the ethics literature.

Murphy, K., Di Ruggiero, E., Upshur, R., Willison, D. J., Malhotra, N., Cai, J. C., Malhotra, N., Lui, V., & Gibson, J. (2021) · BMC Medical Ethics · doi:10.1186/s12910-021-00577-8 · Cited by 435 · see citing works

Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions

Vatansever, S., Schlessinger, A., Wacker, D., Kaniskan, H. Ü., Jin, J., Zhou, M., & Zhang, B. (2021) · Medicinal Research Reviews · doi:10.1002/med.21764 · Cited by 429 · see citing works

Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease

Zhou, W., Kanai, M., Wu, K.-H., Rasheed, H., Tsuo, K., Hirbo, J. B., Wang, Y., Bhattacharya, A., Zhao, H., Namba, S., et al. (2022) · Cell Genomics · doi:10.1016/j.xgen.2022.100192 · Cited by 414 · see citing works

Natural language processing of symptoms documented in free-text narratives of electronic health records: a systematic review

Koleck, T. A., Dreisbach, C., Bourne, P. E., & Henry, S. B. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy173 · Cited by 409 · see citing works

An Overview of Drug Discovery and Development

Berdigaliyev, N., & Aljofan, M. (2020) · Future Medicinal Chemistry · doi:10.4155/fmc-2019-0307 · Cited by 409 · see citing works

Advancing human genetics research and drug discovery through exome sequencing of the UK Biobank

Szustakowski, J. D., Balasubramanian, S., Kvikstad, E., Khalid, S., Bronson, P. G., Sasson, A., Wong, E., Liu, D., Wade Davis, J., Haefliger, C., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00885-0 · Cited by 401 · see citing works

Boosting docking-based virtual screening with deep learning.

Pereira, J. C., Caffarena, E. R., & dos Santos, C. N. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00355 · Cited by 395 · see citing works

Questions for artificial intelligence in health care

Maddox, T. M., Rumsfeld, J. S., & Payne, P. R. O. (2019) · JAMA · doi:10.1001/jama.2018.18932 · Cited by 385 · see citing works

Attention is all you need: utilizing attention in AI-enabled drug discovery

Zhang, Y., Liu, C., Liu, M., Liu, T., Lin, H., Cheng-bing, H., & Lin, N. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad467 · Cited by 385 · see citing works

SCScore: Synthetic Complexity Learned from a Reaction Corpus

Coley, C. W., Rogers, L., Green, W. H., & Jensen, K. F. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00622 · Cited by 381 · see citing works

Cancer immunotherapy: a brief review of the history, possibilities, and challenges ahead

Oiseth, S. J., & Aziz, M. S. (2017) · Journal of Cancer Metastasis and Treatment · doi:10.20517/2394-4722.2017.41 · Cited by 372 · see citing works

NNScore 2.0: A Neural-Network Receptor–Ligand Scoring Function

Durrant, J. D., & McCammon, J. A. (2011) · Journal of Chemical Information and Modeling · doi:10.1021/ci2003889 · Cited by 361 · see citing works

Personalized Medicine and the Power of Electronic Health Records

Abul-Husn, N. S., & Kenny, E. E. (2019) · Cell · doi:10.1016/j.cell.2019.02.039 · Cited by 355 · see citing works

AI-powered therapeutic target discovery

Pun, F. W., Ozerov, I. V., & Zhavoronkov, A. (2023) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2023.06.010 · Cited by 341 · see citing works

Benchmarking AlphaFold for protein complex modeling reveals accuracy determinants

Yin, R., Feng, B. Y., Varshney, A., & Pierce, B. G. (2021) · doi:10.1101/2021.10.23.465575 · Cited by 335 · see citing works

Benchmarking <scp>AlphaFold</scp> for protein complex modeling reveals accuracy determinants

Yin, R., Feng, B. Y., Varshney, A., & Pierce, B. G. (2022) · Protein Science · doi:10.1002/pro.4379 · Cited by 335 · see citing works

Cytotoxic activity of some lichen extracts on murine and human cancer cell lines

Bézivin, C., Tomasi, S., Dévéhat, F. L., & Boustié, J. (2003) · Phytomedicine · doi:10.1078/094471103322331458 · Cited by 323 · see citing works

AI in drug discovery and its clinical relevance

Qureshi, R., Irfan, M., Gondal, T. M., Khan, S., Wu, J., Hadi, M. U., Heymach, J., Le, X., Yan, H., & Alam, T. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e17575 · Cited by 319 · see citing works

Architecture, implementation and parallelisation of the GROMOS software for biomolecular simulation

Schmid, N., Christ, C. D., Christen, M., Eichenberger, A. P., & A, W. F. V. G. (2011) · Computer Physics Communications · doi:10.1016/j.cpc.2011.12.014 · Cited by 318 · see citing works

Discovering Anti-Cancer Drugs via Computational Methods

Cui, W., Aouidate, A., Wang, S., Yu, Q., Li, Y.-H., & Yuan, S. (2020) · Frontiers in Pharmacology · doi:10.3389/fphar.2020.00733 · Cited by 310 · see citing works

Before and after AlphaFold2: An overview of protein structure prediction

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Artificial Intelligence Predictive Models of Response to Cytotoxic Chemotherapy Alone or Combined to Targeted Therapy for Metastatic Colorectal Cancer Patients: A Systematic Review and Meta-Analysis

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Genome‐wide and Phenome‐wide Approaches to Understand Variable Drug Actions in Electronic Health Records

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Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery

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Artificial Intelligence and Machine Learning in Cancer Drug Discovery, Nanomedicine, and Precision Oncology

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AI-Assisted Design and Biochemical Optimization of Protein Structures for Enhanced Drug Delivery in Chemotherapy

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Bridging diagnosis and therapeutics: The role of AI in cancer detection and drug development

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Integrating AI into the Clinical Workflows Across the Cancer Care Continuum: Opportunities and Challenges

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Explainable Artificial Intelligence in Drug Discovery and Healthcare: A Comprehensive Review of Integrative Machine Learning Models, Biomedical Applications, and Clinical Translation Issues

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From virtual screening to functional validation: An MM/GBSA and molecular dynamics driven strategy for targeting phytopathogenic fungal ferrochelatase

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Generative AI and medical oncologists: Be curious, but cautious!

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Fitriani, F., Jun, W., & Weber, M. (2026) · Research of Scientia Naturalis · doi:10.70177/scientia.v3i1.3378

Drug target identification and validation using AI

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AI-Driven Target Identification Reshapes Early-Stage Drug Discovery in Triple-Negative Breast Cancer

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Revolutionizing Anti-Cancer Drug Discovery: The Role of Artificial Intelligence

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Role of artificial intelligence in cancer drug discovery and development

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Artificial Intelligence-Based Methods for Drug Repurposing and Development in Cancer

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AI in cancer drug development and immunotherapy

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