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

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

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

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

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 9,115

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

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

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

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

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

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 3,236

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

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

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

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 2,722

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

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

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

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 1,813

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 1,804

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

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

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

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

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 1,595

Molecular dynamics simulations and drug discovery

Durrant, J. D., & McCammon, J. A. (2011) · BMC Biology · doi:10.1186/1741-7007-9-71 · Cited by 1,475

Computational approaches streamlining drug discovery

Sadybekov, A., & Katritch, V. (2023) · Nature · doi:10.1038/s41586-023-05905-z · Cited by 1,424

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

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

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

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

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

Junction Tree Variational Autoencoder for Molecular Graph GenerationPreprint

Jin, W., Barzilay, R., & Jaakkola, T. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.04364 · Cited by 1,053

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

Improved prediction of protein-protein interactions using AlphaFold2

Bryant, P., Pozzati, G., & Elofsson, A. (2021) · doi:10.1101/2021.09.15.460468 · Cited by 1,022

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 966

mRNA vaccine for cancer immunotherapy

Miao, L., Zhang, Y., & Huang, L. (2021) · Molecular Cancer · doi:10.1186/s12943-021-01335-5 · Cited by 966

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 900

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 877

Introduction to artificial intelligence in medicine

Mintz, Y., & Brodie, R. (2019) · Minimally Invasive Therapy & Allied Technologies · doi:10.1080/13645706.2019.1575882 · Cited by 867

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

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

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

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

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

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

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

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 750

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 732

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

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 683

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

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

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

Proceedings of the 2007 ACM/IEEE conference on Supercomputing

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

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 611

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

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

Sledz, P., & Caflisch, A. (2017) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2017.10.010 · Cited by 598

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

An overview of targeted cancer therapy

Padma, V. V. (2015) · Biomedicine · doi:10.7603/s40681-015-0019-4 · Cited by 500

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 499

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

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 490

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 488

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

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

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 466

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

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

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

An Overview of Drug Discovery and Development

Berdigaliyev, N., & Aljofan, M. (2020) · Future Medicinal Chemistry · doi:10.4155/fmc-2019-0307 · Cited by 431

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

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 426

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

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

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

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 391

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 386

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

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

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 371

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

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

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

Benchmarking AlphaFold 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

Before and after AlphaFold2: An overview of protein structure prediction

Bertoline, L. M. F., Lima, A. N., Krieger, J. E., & Teixeira, S. K. (2023) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2023.1120370 · Cited by 330

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

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

Schmid, N., Christ, C. D., Christen, M., Eichenberger, A. P., & van Gunsteren, W. F. (2011) · Computer Physics Communications · doi:10.1016/j.cpc.2011.12.014 · Cited by 319

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

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 315

Characterising metabolomic signatures of lipid-modifying therapies through drug target mendelian randomisation

Richardson, T. G., Leyden, G. M., Wang, Q., Bell, J. A., Elsworth, B., Davey Smith, G., & Holmes, M. V. (2022) · PLOS Biology · doi:10.1371/journal.pbio.3001547 · Cited by 304

Immuno-Oncology: Emerging Targets and Combination Therapies

Marshall, H. T., & Djamgoz, M. B. A. (2018) · Frontiers in Oncology · doi:10.3389/fonc.2018.00315 · Cited by 303

Cancer vaccines

Butterfield, L. H. (2015) · BMJ · doi:10.1136/bmj.h988 · Cited by 288

Benchmarking AlphaFold‐enabled molecular docking predictions for antibiotic discovery

Wong, F., Krishnan, A., Zheng, E. J., Stärk, H., Manson, A. L., Earl, A. M., Jaakkola, T., & Collins, J. J. (2022) · Molecular Systems Biology · doi:10.15252/msb.202211081 · Cited by 284

Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery

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