Success stories of AI in drug discovery - where do things stand?

Software survey: VOSviewer, a computer program for bibliometric mapping

van Eck, N. J., & Waltman, L. (2010) · Scientometrics · doi:10.1007/s11192-009-0146-3 · Cited by 21,484 · see citing works

The practical implementation of artificial intelligence technologies in medicine

He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X., & Zhang, K. (2019) · Nature Medicine · doi:10.1038/s41591-018-0307-0 · Cited by 2,246 · see citing works

Baricitinib plus Remdesivir for Hospitalized Adults with Covid-19

Kalil, A. C., Patterson, T. F., Mehta, A. K., Tomashek, K. M., Wolfe, C. R., Ghazaryan, V., Marconi, V. C., Ruiz-Palacios, G. M., Hsieh, L. L., Kline, S., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2031994 · Cited by 1,840 · 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

Deep learning enables rapid identification of potent DDR1 kinase inhibitors

Zhavoronkov, A., Ivanenkov, Y. A., Aliper, A., Veselov, M. S., Aladinskiy, V. A., Aladinskaya, A. V., Terentiev, V. A., Polykovskiy, D. A., Kuznetsov, M. D., Asadulaev, A., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0224-x · Cited by 1,573 · see citing works

Baricitinib as potential treatment for 2019-nCoV acute respiratory disease

Richardson, P., Griffin, I., Tucker, C., Smith, D., Oechsle, O., Phelan, A., Rawling, M., Savory, E., & Stebbing, J. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30304-4 · Cited by 1,456 · see citing works

COVID-19: combining antiviral and anti-inflammatory treatments

Stebbing, J., Phelan, A., Griffin, I., Tucker, C., Oechsle, O., Smith, D., & Richardson, P. (2020) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(20)30132-8 · Cited by 1,117 · see citing works

Overview of artificial intelligence in medicine

Amisha, Malik, P., Pathania, M., & Rathaur, V. K. (2019) · Journal of Family Medicine and Primary Care · doi:10.4103/jfmpc.jfmpc_440_19 · Cited by 1,106 · cited 1× · see citing works

Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: A review

Fogel, D. B. (2018) · Contemporary Clinical Trials Communications · doi:10.1016/j.conctc.2018.08.001 · Cited by 1,035 · see citing works

Artificial intelligence in drug development: Present status and future prospects

Mak, K.-K., & Pichika, M. R. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2018.11.014 · Cited by 1,018 · see citing works

ADMET modeling approaches in drug discovery

Ferreira, L. L. G., & Andricopulo, A. D. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2019.03.015 · Cited by 842 · see citing works

Artificial Intelligence and Black‐Box Medical Decisions: <i>Accuracy versus Explainability</i>

London, A. J. (2019) · Hastings Center Report · doi:10.1002/hast.973 · Cited by 801 · see citing works

On instabilities of deep learning in image reconstruction and the potential costs of AI

Antun, V., Renna, F., Poon, C., Adcock, B., & Hansen, A. C. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1907377117 · Cited by 792 · see citing works

From machine learning to deep learning: progress in machine intelligence for rational drug discovery

Zhang, L., Tan, J., Han, D., & Zhu, H. (2017) · Drug Discovery Today · doi:10.1016/j.drudis.2017.08.010 · Cited by 760 · see citing works

Next generation antibody drugs: pursuit of the 'high-hanging fruit'

Carter, P. J., & Lazar, G. A. (2017) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2017.227 · Cited by 760 · see citing works

Artificial intelligence for clinical trial design

Harrer, S., Shah, P., Antony, B., & Hu, J. (2019) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2019.05.005 · Cited by 672 · see citing works

Drug–target interaction prediction: databases, web servers and computational models

Chen, X., Yan, C. C., Zhang, X., Zhang, X., Dai, F., Yin, J., & Zhang, Y. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbv066 · Cited by 658 · see citing works

Ultrathin Air-Stable n-Type Organic Phototransistor Array for Conformal Optoelectronics

Liu, M., Wang, H., Tang, Q., Zhao, X., Tong, Y., & Liu, Y. (2018) · Scientific Reports · doi:10.1038/s41598-018-35062-7 · Cited by 577 · see citing works

Interpretation of Compound Activity Predictions from Complex Machine Learning Models Using Local Approximations and Shapley Values

Rodríguez-Pérez, R., & Bajorath, J. (2019) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.9b01101 · Cited by 520 · 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

Machine learning: applications of artificial intelligence to imaging and diagnosis

Nichols, J., Chan, H. H., & Baker, M. A. B. (2018) · Biophysical Reviews · doi:10.1007/s12551-018-0449-9 · Cited by 458 · see citing works

Big data and artificial intelligence modeling for drug discovery

Zhu, H. (2020) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-010919-023324 · Cited by 455 · 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

Targeting Adenosine in Cancer Immunotherapy to Enhance T-Cell Function

Viganó, S., Alatzoglou, D., Irving, M. B., Ménétrier‐Caux, C., Caux, C., Romero, P. J., & Coukos, G. (2019) · Frontiers in Immunology · doi:10.3389/fimmu.2019.00925 · Cited by 422 · see citing works

Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 1: Ways to make an impact, and why we are not there yet

Bender, A., & Cortés-Ciriano, I. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2020.12.009 · Cited by 405 · see citing works

Image-based profiling for drug discovery: due for a machine-learning upgrade?

Chandrasekaran, S. N., Ceulemans, H., Boyd, J. D., & Carpenter, A. E. (2020) · Nature Reviews Drug Discovery · doi:10.1038/s41573-020-00117-w · Cited by 387 · see citing works

Artificial Intelligence for Drug Toxicity and Safety

Basile, A. O., Yahi, A., & Tatonetti, N. P. (2019) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2019.07.005 · Cited by 385 · see citing works

Transfer Learning for Drug Discovery

Cai, C., Wang, S., Xu, Y., Zhang, W., Tang, K., Ouyang, Q., Lai, L., & Pei, J. (2020) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.9b02147 · Cited by 364 · see citing works

Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era

Jing, Y., Bian, Y., Hu, Z., Wang, L., & Xie, X.-Q. S. (2018) · The AAPS Journal · doi:10.1208/s12248-018-0210-0 · Cited by 353 · see citing works

A Deep Learning Approach to Antibiotic Discovery

Stokes, J. M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N. M., MacNair, C. R., French, S., Carfrae, L. A., Bloom‐Ackermann, Z., et al. (2020) · Cell · doi:10.1016/j.cell.2020.04.001 · Cited by 298 · see citing works

Mechanism of baricitinib supports artificial intelligence‐predicted testing in COVID‐19 patients

Stebbing, J., Krishnan, V., de Bono, S., Ottaviani, S., Casalini, G., Richardson, P. J., Monteil, V., Lauschke, V. M., Mirazimi, A., Youhanna, S. C., et al. (2020) · EMBO Molecular Medicine · doi:10.15252/emmm.202012697 · Cited by 291 · see citing works

In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts

Yang, H., Sun, L., Li, W., Liu, G., & Tang, Y. (2018) · Frontiers in Chemistry · doi:10.3389/fchem.2018.00030 · Cited by 282 · see citing works

Baricitinib restrains the immune dysregulation in patients with severe COVID-19

Bronte, V., Ugel, S., Tinazzi, E., Vella, A., De Sanctis, F., Canè, S., Batani, V., Trovato, R., Fiore, A., Petrova, V., et al. (2020) · Journal of Clinical Investigation · doi:10.1172/jci141772 · Cited by 281 · see citing works

Machine and deep learning approaches for cancer drug repurposing

Issa, N. T., Stathias, V., Schürer, S. C., & Dakshanamurthy, S. (2020) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2019.12.011 · Cited by 280 · see citing works

Artificial Intelligence for COVID-19 Drug Discovery and Vaccine Development

Arshadi, A. K., Webb, J., Salem, M., Cruz, E., Calad-Thomson, S., Ghadirian, N., Collins, J., Diez‐Cecilia, E., Kelly, B., Goodarzi, H., et al. (2020) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2020.00065 · Cited by 245 · see citing works

Comprehensive ensemble in QSAR prediction for drug discovery

Kwon, S., Bae, H., Jo, J., & Yoon, S. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-3135-4 · Cited by 243 · see citing works

Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling

Zhao, L., Ciallella, H. L., Aleksunes, L. M., & Zhu, H. (2020) · Drug Discovery Today · doi:10.1016/j.drudis.2020.07.005 · Cited by 237 · see citing works

A renaissance of neural networks in drug discovery

Baskin, I. I., Winkler, D., & Tetko, I. V. (2016) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2016.1201262 · Cited by 235 · see citing works

Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 2: a discussion of chemical and biological data

Bender, A., & Cortes-Ciriano, I. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2020.11.037 · Cited by 232 · see citing works

Deep learning and alternative learning strategies for retrospective real-world clinical data

Chen, D. J., Liu, S., Kingsbury, P., Sohn, S., Storlie, C. B., Habermann, E. B., Naessens, J. M., Larson, D. W., & Liu, H. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0122-0 · Cited by 223 · see citing works

A deep learning framework for high-throughput mechanism-driven phenotype compound screening and its application to COVID-19 drug repurposing

Pham, T.-H., Qiu, Y., Zeng, J., Xie, L., & Zhang, P. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-020-00285-9 · Cited by 219 · see citing works

Deep Learning in Virtual Screening: Recent Applications and Developments

Kimber, T. B., Chen, Y., & Volkamer, A. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22094435 · Cited by 213 · see citing works

SAR and QSAR modeling of a large collection of LD50 rat acute oral toxicity data

Gadaleta, D., Vuković, K., Toma, C., Lavado, G. J., Karmaus, A. L., Mansouri, K., Kleinstreuer, N., Benfenati, E., & Roncaglioni, A. (2019) · Journal of Cheminformatics · doi:10.1186/s13321-019-0383-2 · Cited by 210 · see citing works

Machine learning models for drug–target interactions: current knowledge and future directions

D’Souza, S., Prema, K. V., & Balaji, S. (2020) · Drug Discovery Today · doi:10.1016/j.drudis.2020.03.003 · Cited by 208 · see citing works

Application of Artificial Intelligence in COVID-19 drug repurposing

Mohanty, S., Rashid, M. H. A., Mridul, M., Mohanty, C., & Swayamsiddha, S. (2020) · Diabetes & Metabolic Syndrome Clinical Research & Reviews · doi:10.1016/j.dsx.2020.06.068 · Cited by 207 · see citing works

JAK inhibition reduces SARS-CoV-2 liver infectivity and modulates inflammatory responses to reduce morbidity and mortality

Stebbing, J., Nievas, G. S., Falcone, M., Youhanna, S. C., Richardson, P. J., Ottaviani, S., Shen, J. X., Sommerauer, C., Tiseo, G., Ghiadoni, L., et al. (2020) · Science Advances · doi:10.1126/sciadv.abe4724 · Cited by 207 · see citing works

Machine Learning-based Virtual Screening and Its Applications to Alzheimer’s Drug Discovery: A Review

Carpenter, K. A., & Huang, X. (2018) · Current Pharmaceutical Design · doi:10.2174/1381612824666180607124038 · Cited by 203 · see citing works

Generative chemistry: drug discovery with deep learning generative models

Bian, Y., & Xie, X.-Q. (2021) · Journal of Molecular Modeling · doi:10.1007/s00894-021-04674-8 · Cited by 193 · see citing works

Cardiovascular calcification: artificial intelligence and big data accelerate mechanistic discovery

Rogers, M. A., & Aïkawa, E. (2018) · Nature Reviews Cardiology · doi:10.1038/s41569-018-0123-8 · Cited by 173 · see citing works

Machine learning and image-based profiling in drug discovery

Scheeder, C., Heigwer, F., & Boutros, M. (2018) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2018.05.004 · Cited by 167 · see citing works

Machine Learning in Amyotrophic Lateral Sclerosis: Achievements, Pitfalls, and Future Directions

Grollemund, V., Pradat, P., Querin, G., Delbot, F., Chat, G. L., Pradat‐Peyre, J., & Bede, P. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00135 · Cited by 155 · see citing works

Design of efficient computational workflows for in silico drug repurposing

Vanhaelen, Q., Mamoshina, P., Aliper, A. M., Artemov, A., Lezhnina, K., Ozerov, I., Labat, I., & Zhavoronkov, A. (2017) · Drug Discovery Today · doi:10.1016/j.drudis.2016.09.019 · Cited by 150 · see citing works

The emerging roles of artificial intelligence in cancer drug development and precision therapy

Liang, G., Fan, W., Luo, H., & Zhu, X. M. (2020) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2020.110255 · Cited by 149 · see citing works

Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches

Kim, H., Kim, E., Lee, I., Bae, B., Park, M., & Nam, H. (2020) · Biotechnology and Bioprocess Engineering · doi:10.1007/s12257-020-0049-y · Cited by 138 · see citing works

Machine Learning in Pharmacometrics: Opportunities and Challenges

McComb, M., Bies, R., & Ramanathan, M. (2022) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.14801 · Cited by 128 · see citing works

Magnetic Nanoparticles in the Central Nervous System: Targeting Principles, Applications and Safety Issues

D’Agata, F., Ruffinatti, F. A., Boschi, S., Stura, I., Rainero, I., Abollino, O., Cavalli, R., & Guiot, C. (2017) · Molecules · doi:10.3390/molecules23010009 · Cited by 117 · see citing works

A deep learning framework for drug repurposing via emulating clinical trials on real-world patient data

Liu, R., Wei, L., & Zhang, P. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-020-00276-w · Cited by 117 · see citing works

Applications of Machine Learning Methods in Drug Toxicity Prediction

Zhang, L., Zhang, H., Ai, H., Hu, H., Li, S., Zhao, J. H., & Liu, H. (2018) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026618666180727152557 · Cited by 112 · see citing works

Image-based high-content screening in drug discovery

Lin, S., Schorpp, K., Rothenaigner, I., & Hadian, K. (2020) · Drug Discovery Today · doi:10.1016/j.drudis.2020.06.001 · Cited by 111 · see citing works

Exploring the Potential of Artificial Intelligence and Machine Learning to Combat COVID-19 and Existing Opportunities for LMIC: A Scoping Review

Naseem, M., Akhund, R., Arshad, H., & Ibrahim, M. (2020) · Journal of Primary Care & Community Health · doi:10.1177/2150132720963634 · Cited by 105 · see citing works

Design, Synthesis, and Structure−Activity Relationship of Substrate Competitive, Selective, and in Vivo Active Triazole and Thiadiazole Inhibitors of the c-Jun N-Terminal Kinase

De, S. K., Stebbins, J. L., Chen, L., Riel-Mehan, M. M., Machleidt, T., Dahl, R. S., Yuan, H., Emdadi, A., Barile, E., Chen, V., et al. (2009) · Journal of Medicinal Chemistry · doi:10.1021/jm801503n · Cited by 95 · see citing works

A review of big data and medical research

Mallappallil, M. C., Sabu, J., Gruessner, A. C., & Salifu, M. (2020) · SAGE Open Medicine · doi:10.1177/2050312120934839 · Cited by 95 · see citing works

Application of Computational Biology and Artificial Intelligence Technologies in Cancer Precision Drug Discovery

Nagasundaram, N., Yapp, E. K. Y., Le, N. Q. K., Kamaraj, B., Al‐Subaie, A. M., & Yeh, H. (2019) · BioMed Research International · doi:10.1155/2019/8427042 · Cited by 87 · see citing works

Current status and future directions of high-throughput ADME screening in drug discovery

Shou, W. Z. (2020) · Journal of Pharmaceutical Analysis · doi:10.1016/j.jpha.2020.05.004 · Cited by 86 · see citing works

A new paradigm for drug development

Burki, T. K. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30088-1 · Cited by 83 · see citing works

Deep Learning Applications to Combat Novel Coronavirus (COVID-19) Pandemic

Asraf, A., Islam, M. Z., Haque, M. R., & Islam, M. M. (2020) · SN Computer Science · doi:10.1007/s42979-020-00383-w · Cited by 82 · see citing works

The power of deep learning to ligand-based novel drug discovery

Baskin, I. I. (2020) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2020.1745183 · Cited by 75 · see citing works

A Review of Deep Learning Methods for Antibodies

Graves, J., Byerly, J., Priego, E., Makkapati, N., Parish, S., Medellin, B. P., & Berrondo, M. (2020) · Antibodies · doi:10.3390/antib9020012 · Cited by 73 · see citing works

Artificial intelligence in the early stages of drug discovery

Cavasotto, C. N., & Filippo, J. I. D. (2020) · Archives of Biochemistry and Biophysics · doi:10.1016/j.abb.2020.108730 · Cited by 67 · see citing works

Computational modeling of human oral bioavailability: what will be next?

Cabrera‐Pérez, M. Á., & Pham‐The, H. (2018) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2018.1463988 · Cited by 64 · see citing works

Use of Artificial Intelligence and Machine Learning for Discovery of Drugs for Neglected Tropical Diseases

Winkler, D. A. (2021) · Frontiers in Chemistry · doi:10.3389/fchem.2021.614073 · Cited by 62 · see citing works

Key indicators of phase transition for clinical trials through machine learning

Feijoo, F., Palopoli, M., Bernstein, J., Siddiqui, S., & Albright, T. E. (2020) · Drug Discovery Today · doi:10.1016/j.drudis.2019.12.014 · Cited by 61 · see citing works

Machine learning for target discovery in drug development

Rodrigues, T., & Bernardes, G. J. L. (2019) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2019.10.003 · Cited by 60 · see citing works

Machine learning models for classification tasks related to drug safety

Rácz, A., Bajusz, D., Miranda‐Quintana, R. A., & Héberger, K. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10239-x · Cited by 57 · see citing works

Text Mining for Drug Discovery

Zheng, S., Dharssi, S., Wu, M., Li, J., & Lu, Z. (2019) · Methods in molecular biology · doi:10.1007/978-1-4939-9089-4_13 · Cited by 56 · see citing works

Selecting Machine-Learning Scoring Functions for Structure-Based Virtual Screening

Ballester, P. J. (2019) · Drug Discovery Today Technologies · doi:10.1016/j.ddtec.2020.09.001 · Cited by 56 · see citing works

The application of machine learning techniques to innovative antibacterial discovery and development

Serafim, M. S. M., Kronenberger, T., Oliveira, P. R., Poso, A., Honorio, K. M., Mota, B. E. F., & Maltarollo, V. G. (2020) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2020.1776696 · Cited by 55 · see citing works

Contribution of machine learning approaches in response to SARS-CoV-2 infection

Mottaqi, M., Mohammadipanah, F., & Sajedi, H. (2021) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2021.100526 · Cited by 55 · see citing works

Structural basis for the cyclophilin A binding affinity and immunosuppressive potency ofE-ISA247 (voclosporin)

Kuglstatter, A., Mueller, F., Kusznir, E. A., Gsell, B., Stihle, M., Thoma, R., Benz, J., Aspeslet, L. J., Freitag, D. G., & Hennig, M. (2011) · Acta Crystallographica Section D Biological Crystallography · doi:10.1107/s0907444910051905 · Cited by 53 · see citing works

Recent Progress in Machine Learning-based Prediction of Peptide Activity for Drug Discovery

Wu, Q., Ke, H., Li, D., Wang, Q., Fang, J., & Zhou, J. (2019) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026619666190122151634 · Cited by 53 · see citing works

Applying Data Analytics and Visualization to Assessing the Research Impact of the Cancer Cell Biology (CCB) Program at the University of North Carolina at Chapel Hill

Yu, F., & Hayes, B. E. (2018) · Journal of eScience Librarianship · doi:10.7191/jeslib.2018.1123 · Cited by 50 · see citing works

Real-world evidence and product development: Opportunities, challenges and risk mitigation

Naidoo, P., Bouharati, C., Rambiritch, V., Jose, N. C., Karamchand, S., CHILTON, R. J., & Leisegang, R. F. (2021) · Wiener klinische Wochenschrift · doi:10.1007/s00508-021-01851-w · Cited by 47 · see citing works

Prediction of clinically relevant drug‐induced liver injury from structure using machine learning

Hammann, F., Schöning, V., & Drewe, J. (2018) · Journal of Applied Toxicology · doi:10.1002/jat.3741 · Cited by 42 · see citing works

Repurposing available drugs for neurodevelopmental disorders: The fragile X experience

Tranfaglia, M. R., Thibodeaux, C., Mason, D. J., Brown, D., Roberts, I., Smith, R. J., Guilliams, T., & Cogram, P. (2018) · Neuropharmacology · doi:10.1016/j.neuropharm.2018.05.004 · Cited by 39 · see citing works

Using Drug Expression Profiles and Machine Learning Approach for Drug Repurposing

Zhao, K., & So, H. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-8955-3_13 · Cited by 39 · see citing works

Challenges with multi-objective QSAR in drug discovery

Lambrinidis, G., & Tsantili‐Kakoulidou, A. (2018) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2018.1496079 · Cited by 34 · see citing works

Data Integration Using Advances in Machine Learning in Drug Discovery and Molecular Biology

Hudson, I. L. (2020) · Methods in molecular biology · doi:10.1007/978-1-0716-0826-5_7 · Cited by 33 · see citing works

Application of Machine Learning Approaches for the Design and Study of Anticancer Drugs

Hu, Y., Lu, Y., Wang, S., Zhang, M., Qu, X., & Niu, B. (2018) · Current Drug Targets · doi:10.2174/1389450119666180809122244 · Cited by 32 · see citing works

Recruitment failure and futility were the most common reasons for discontinuation of clinical drug trials. Results of a nationwide inception cohort study in the Netherlands

van den Bogert, C. A., Souverein, P. C., Brekelmans, C. T. M., Janssen, S. W. J., Koëter, G. H., Leufkens, H. G. M., & Bouter, L. M. (2017) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2017.05.001 · Cited by 31 · see citing works

Interpretable machine learning methods for in vitro pharmaceutical formulation development

Ye, Z., Yang, W., Yang, Y., & Ouyang, D. (2021) · Food Frontiers · doi:10.1002/fft2.78 · Cited by 31 · see citing works

Human Intestinal Tissue Explant Exposure to Silver Nanoparticles Reveals Sex Dependent Alterations in Inflammatory Responses and Epithelial Cell Permeability

Gokulan, K., Williams, K., Orr, S. E., & Khare, S. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms22010009 · Cited by 30 · see citing works

Survey of Machine Learning Techniques for Prediction of the Isoform Specificity of Cytochrome P450 Substrates

Xiong, Y., Qiao, Y., Kihara, D., Zhang, H.-Y., Zhu, X., & Wei, D. (2018) · Current Drug Metabolism · doi:10.2174/1389200219666181019094526 · Cited by 29 · see citing works

Emerging role of artificial intelligence in therapeutics for COVID-19: a systematic review

Kaushal, K., Sarma, P., Rana, S., Medhi, B., & Naithani, M. (2020) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2020.1855250 · Cited by 29 · see citing works

Multi-task learning with a natural metric for quantitative structure activity relationship learning

Sadawi, N., Olier, I., Vanschoren, J., van Rijn, J. N., Besnard, J., Bickerton, R., Groşan, C., Soldatova, L., & King, R. D. (2019) · Journal of Cheminformatics · doi:10.1186/s13321-019-0392-1 · Cited by 26 · see citing works

Progress with modeling activity landscapes in drug discovery

Vogt, M. (2018) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2018.1465926 · Cited by 25 · see citing works

COVID-19 Pandemic: Time to Revive the Cyclophilin Inhibitor Alisporivir

Pawlotsky, J. (2020) · Clinical Infectious Diseases · doi:10.1093/cid/ciaa587 · Cited by 25 · see citing works

Machine Learning-Based Modeling of Drug Toxicity

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Integrative approach in the era of failing drug discovery and development

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